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Interview Marc Smaluhn, Managing Director Europe at Netquest, on the Quality of Survey Data Page 10
International Issue 2018/2019
International Market Research What’s Hot in the Industry? Studies, Concepts, Trends
Big Data How to Obtain Deep Insights Page 12
Personas Bring Your Buyer to Life Page 16
Conjoint Analysis The Dos and Don’ts Page 32
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International Issue 2018/2019
International Market Research
Marc Smaluhn, Managing Director Europe at Netquest, on the Quality of Survey Data Page 10
What’s Hot in the Industry? Studies, Concepts, Trends Big Data How to Obtain Deep Insights Page 12
Personas Bring Your Buyer to Life Page 16
Conjoint Analysis The Dos and Don’ts Page 32
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Change Rarely has the international market research industry been in such a state of transformation as it is today. Old structures are breaking down and making room for new ones; not only for innovative research methods, but also for new players on the global market. The transformative pressure is enormous, and the GDPR and quality debates aren’t simplifying matters. But every major change also offers an opportunity – and forces us to take a critical look at the way we work. The same applies when it comes to dealing with big data, and responsibly selecting data sources and business partners, explains Pete Cape in his article on page 12. Fictitious customer profiles can be created to make these data tangible and visible. On page 16, Judith Wieghardt presents the advantages of these “personas” and explains how they can help you make important strategic decisions. Starting on page 40, JD Deitch explains what panel providers should learn from the quality debate and what role automation, artificial intelligence and machine learning play. And Julia Görnandt and Samantha Bond also found out that qualitative research can benefit from automated processes too. They present their findings on page 28.
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But despite all this automation, market research remains a people business. The diversity and expertise of the entire branch can once again be seen and experienced at the international market research show Research & Results 2018 on 24 and 25 October in Munich. Find out all the details starting on page 6.
Dagmar Dreßler Chief Editor dressler@research-results.de
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Events 6 Trade Show: Research & Results 2018
Research 10 Interview Marc Smaluhn, Managing Director Europe at Netquest, on the quality of survey data 12 Big Data, Deep Insights How to use the abundance of data powerfully in market research 16 Customer Centricity How to create and embed buyer personas – a step-by-step guide 20 Questionnaire Design The most common problems and how to solve them
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24 Efficiency What makes operations teams successful? 28 Automation Can artificial intelligence help future-proof qualitative research? 32 Conjoint Analysis Dos and don’ts in product research and pricing – a remedy for notorious problems
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36 Consumers’ Behaviour A combination of classical U&A and permission- based online tracking 40 Quality What can companies learn from the controversial discussion? 44 Qualitative Research Is the future purely digital? A plea to not forget first-hand experiences 47 Concerns The findings of this year’s study “Challenges of Nations”
Ironing out the Details Solving Questionnaire Design and Scripting Problems for Research Clients Page 20
Lifestyle 50 10 Questions – 10 Answers Pierre Pigeon, CEO, AplusA/Ifop Group
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Even Bigger, Even Better The 13th Research & Results is being held in Munich on 24 and 25 October. Here's what the world's leading market research fair has to offer this year.
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The world’s leading market research show Research & Results is on again in the Bavarian state capital from 24 to 25 October 2018. The event’s venue is as well-known as it is tried and proven, with the who’s who of the market research industry coming together at the MOC München conference centre this year under the motto “Ideas for Better Insights”. And that’s not just a catchphrase: Visitors to Research & Results can again expect to find the latest trends, innovative tools, top speakers and endless opportunities to network there.
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Latest Figures – Record Breaking 188 exhibitors from 24 countries were already listed when this report went to press – a new record high is in the offing. International research groups are coming, as are full-service agencies, field research institutes, IT service providers and specialist suppliers. The show’s organisers are also excited to find out if last year’s record number of 3,300 visitors can be broken this time around; and if those visitors again give the event the best possible reviews. The bar is set high, with visitor satisfaction at no less than 90 percent in 2017, but beating that mark motivates the organisers, because they want Research & Results to keep getting better every year.
Workshop Programme – Ready to Go Visitors not only find out what’s hot in the business from the exhibits, but also in the 104 workshops. From customer experience to artificial intelligence, from automation to social listening – the topics are broadly diverse. Advertisement
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Events 44 of the workshops will be held in English or in German with English interpreters. You will find the entire programme online here: www.research-results.com For instance, Kantar TNS invites you to attend an interactive session on young target groups, under the heading “Komm mal rumkumpeln” (Let’s be Buddies). Interrogare reveals dos & don’ts in “Coming of Age: 10 Years of Implicit Research”. In the workshop “From Google to the Mall”, Netquest promises a better understanding of the path to purchase by combining survey and behavioural data. MaritzCX presents “Best Practices that Typify Companies with Successful Customer-Experience Management”. At GIM’s “Germany in Motion!” fitness types and social fitness are scrutinised. And Séissmo plans to reveal what consumers do at night. 44 of the workshops will be held in English or in German with English interpreters. You will find the entire programme online here: www.research-results.com.
Innovation Area – Underway After a successful launch in 2017, the Innovation Area is back again in 2018, but this time on 70 square metres – larger in response to its enormous popularity last year. 30 presentations of 15 minutes each are scheduled for the Innovation Area on both show days, eight of which in English. Details on the programme will be made public shortly, but what we already know is that this is where the most innovative ideas in the industry are presented, and enterprises like Eye Square, Ipsos and IFF International give us sneak peaks at the near future.
Networking – Naturally Alongside the extensive programme, Research & Results also offers uncountable opportunities to meet up with contacts you already know from the industry, and to make new ones. The Research Club is once again holding the already legendary Research & Results Networking Party in the Kongress Bar at Munich’s Theresienhöhe on Wednesday evening. And to draw the curtain on the show on Thursday, the event’s organisers invite you to wind down at the traditional Happy Hour. See you there! www.research-results.com
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Info and Registration Dates: 24 and 25 October 2018 Open to visitors: 24 October: 9 a.m. to 6.30 p.m. 25 October: 9 a.m. to 5.30 p.m. Venue: MOC Veranstaltungscenter Hall 1 and Hall 2 Lilienthalallee 40 ∙ 80939 Munich www.moc-muenchen.de
Registration: You can find the registration form and all other information about Research & Results 2018, with floor plan, workshop programme and exhibitor profiles, at www.research-results.com Admission to the show and workshops is free of charge on both days to registered visitors from the industry.
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DATA PROCESSING & REPORTING
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A Symphony of RR: What is Netquest doing to make sure its clients can have confidence in its results?
Smaluhn: I joined Netquest last year because it is unique in its commitment to quality, innovation, and people. Quality metrics and processes go beyond a single department, involve all aspects of our business, and are inherent within our company values. Following the touchpoints that a client is having with Netquest, here’s what we are doing to always ensure the highest quality throughout the whole journey. Requests for proposals are thoroughly checked for feasibility, and we take a consultative approach with our clients to work out the best solution. All our proposals are fully transparent with a commitment to viability and true cost. Once a project gets commissioned, we double check its scope with our clients and ensure that we match and meet their expectations. Our panels ensure real representation of society and we avoid professional panellists by having a panel which is accessible by invitation only. We treat our panellists fairly, stimulating their participation in projects with gifts and engaging them with transparent and direct communication. Before launching a survey our quality 10
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assurance team checks client surveys on ethical and legal aspects, including user experience. We use in-survey quality controls: measuring length parameters, detecting straightlining, or including trap questions to detect fake panellists. We promote, publish, and implement continuous research on research in close cooperation with universities and academic institutions. Our aim is to find the best way to survey and always get the most accurate results. All Netquest panels have been ISO certified since 2012 which ensures full transparency for data collection and figures that we share with our clients. RR: Netquest links surveys to profile data and the online usage behaviour of the panellists. What are the benefits of this?
Smaluhn: Combining different types of data from the same panellist helps truly understand consumers. It’s what we call “A symphony of data”. A wide range of different data points gathered by different means from the same panellist helps our clients get a 360 view of www.research-results.com
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Data
Right now the industry is discussing how we can ensure the quality of survey data. Research & Results interviewed Marc Smaluhn, Managing Director Europe at Netquest, on the current state of play at Netquest.
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their customers. The main use case is to understand the path to purchase involved in different types of customer journeys. Let’s consider an example: Imagine a client wants to conduct some research about the path to purchase for travel products of people in their thirties in Europe. The data combination makes it possible to focus on a specific group (for example European men and women between 20 and 35 years old). Once segmented (thanks to our profiled panel), we can then observe the specific online behaviour we are interested in (for example what search travel related search terms they use, how they find related information, what travel websites they visit online, for how long, or when they search the web). Besides that, we are continuously adding new types of data. We’ve recently launched the possibility to track the geolocation data of our panellists. So now, we could track if those people visit a travel agency. Afterwards, you could conduct a traditional survey. But you won’t need to ask what they did, because you already know, so you can ask why they did what they did. It gives you a whole new, deeper perspective about consumer behaviour. Of course, this is just an example. We are working on different projects with clients that are getting meaningful insights from the data we are gathering for them. RR: The EU’s General Data Protection Regulation is causing a commotion. Could it not offer in particular the market research industry an opportunity to draw attention to itself, further develop and grow?
Smaluhn: Absolutely, we’ve always seen it like that. The main reason behind the GDPR is to protect personal identifiable information and giving back control to European citizens. We all are online users, so I’m sure we all can understand the importance of this new European regulation just by considering it from a personal perspective. Now, we all in the Market Research industry need to be GDPR compliant, which is really good news. We all have to make sure our panellists give us their explicit consent to be in our panel, communicating our privacy policies in a simple way they can easily understand. We all now have to offer them the option of being removed at any time from our databases. In other words, now, GDPR asks us to treat panellists as people with rights that mustn't be ignored. At Netquest we have been applying data protection rules since the beginning, and prepared for GDPR compliance for years in adjusting process and IT systems. As far as I’m concerned, GDPR is making Market Research a better industry, by setting guidelines we all must follow. RR: We all know you need motivated panellists to obtain good survey data. How do you recruit and incentivize panellists in your company?
Smaluhn: At Netquest we recruit our new panelists by invitation only. That helps us secure the representativity of our panel. So we first decide the quotas we need and we invite them to become panellists. Once they’ve accepted, we base our relationship with our panellist on three aspects. Fair treatment: We fairly reward our panellists for the time they spend answering surveys and participating in research projects but we don’t pay them any money. Unlike other models in market we continue rewarding our panellists even if they get screened out. We www.research-results.com
“As far as I’m concerned, GDPR is making Market Research a better industry, by setting some ethical guidelines we all must follow.”
have created a programme that gives points according to project difficulty. Afterwards, a panellist can exchange those points for gifts in our shop. We try to offer appealing gifts, culturally adapted to each country. Transparent relationship: We keep an open communication with our panellists. Explaining them what are we gathering, and what we need from them. This helps driving intrinsic motivation rather than a pure desire for earning points. Providing a satisfactory experience: We try to provide a good experience. Creating high quality surveys that panellists find entertaining and interesting; avoiding too frequent communication; and of course creating easy to answer and mobile friendly questionnaires. RR: Netquest was founded in Barcelona and has established itself in South America without any language barriers to overcome. Are there any special aspects in South America that the European market researchers have to keep in mind?
Smaluhn: Latin America is a very diverse continent. Each country has its own currency and different aspects which makes it a unique place for research. For instance, the socio economic level calculation criteria depend on each country’s considerations. For online research, more specifically, you have to keep in mind that internet penetration is different in each country as well. And the speed isn’t the same. So you need to be careful because the length of the interview in Latin America might be more than it is in Europe, even if you are doing the same survey. Regarding language barriers, we actually had certain barriers we had to face there. For instance, the Spanish used in Colombia and Venezuela is a lot different from the Spanish used in Argentina, Uruguay and Chile. The same happens with Portuguese; the Brazilian Portuguese isn’t the same Portuguese spoken in Portugal. So the researchers need to keep these sorts of things in mind, once they are developing questionnaires, particularly if we are speaking about multi-country research projects. I know that we, in Netquest, are now writing an eBook with tips of how to conduct online research in Latin America. Once it is ready, we might need an entirely new interview to talk deeper about this matter. With our expansion into Europe – we nowadays cover the main five European markets plus Portugal – we hired experts in each market with a deep understanding of language and culture. Our growth and success in such markets are good indicators that we have been taking the right approach. Netquest turned into a truly international company with strong expertise in all its markets. www.netquest.com esearch &
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Deeper Insights
Big Data in Market Research
Pete Cape on how to use the abundance of data powerfully in market research.
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raditionally, quantitative market research has always been about interviewing participants and collecting data. The core skills of the market researcher were in designing scientific samples and mastering the art of asking questions. Layering onto these core skills an ability to analyse data and provide insights put the researcher in a key role in being able to advise corporations, reducing the risks of poor decision making.
However, for all our science, art and expertise our fundamental tools were never perfect and, despite our best efforts, remain flawed. The flaws in sampling as we move from the theoretical to the practical are well known and their impacts, if not wholly measurable, are accepted as a trade-off worth making. Thus, for example, we understand that theory insists we have a complete sample frame, and that the samples are selected at random and that response rates should be extremely high. In practice we find incomplete frames, poor response rates and the only dimension where we concur with theory is in random person selection – unless we are interviewing on-street or in a mall using quota methods. This is where high quality and scaled panel providers step in, to limit the trade-offs we typically assume we have to make to get to the level of confidence needed to act on our 12
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Spotting Weaknesses
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insights and make an impact. Done well, market research provides critical information for successful decision making for both B2C and B2B companies, ultimately delivering better results and a competitive advantage. Therefore, the decision about which company you choose to source your research sample should be made knowledgeably.
Problematic Questioning In the realm of question design, we have also been less than perfect, for ostensibly good reasons. Sometimes we have been guilty of asking unanswerable questions, sometimes demanding recall of things that cannot be remembered. That we then go on to blame the participant for the flawed answers they give is the subject of a different paper! We know in our hearts that the answers we get are, at best, sub-optimal, yet, again, we know they are accurate enough to base a decision on or to use in analysis. So, for example, we might task a participant to tell us how many hours they listened to the radio last week. A perfectly reasonable question, but one that is almost impossible to www.research-results.com
answer. No-one counts the hours that they are listening to the radio, nor is everyone perfectly aware of when they are hearing radio (and its accompanying adverts) as they are out and about. So the measurement is clearly flawed. However, it still potentially suits our needs since the actual data point we want to know is a person’s relative propensity to be exposed to radio advertising. When we take the raw data and classify people into Heavy, Medium or Light consumers of radio we assume all the mis-recall of listening is working in the same direction and applies equally to everyone. Any error in classification we accept as noise in the data.
Could We Do better? Researchers are generally conservative in their approach, often we value consistency over validity – that is, we would rather have the same slightly inaccurate answer in a tracking study than change the tracker from inaccuracy to accuracy. This makes us unwilling to move away from asking the question differently, and it also makes us resistant to thinking about not asking a question at all. Now we have the opportunity of not asking questions at all, but to take the data from another source.
It’s All About Data Database integration allows us to better understand and analyse the target market in question, enable more accurate segmentation and importantly to fill in the gaps for questions that haven’t been asked but that clients want to know more about. This data item can come in many forms, from previously asked market research questions to probabilistic estimates. In all cases the prudent researcher will be asking themselves two fundamental questions – what exactly is this data? And how representative is it when I apply it to my dataset? We can call the file of data you want to append to, the target file and the file that contains the additional data you want append, the source file. esearch &
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The Problem of Bias The question of representivity is a continuum related to match rates and is analogous to the problem of response rates. If all, or almost all, records in the target file match to those in the source file then there is not a problem, but what if it is only 70 per cent? 50 per cent? 10 per cent? Clearly, we would be getting more concerned as the match percentage approaches 10 per cent, but matching might be a random phenomenon, and related to scale – and so now the 10 per cent match becomes an issue of sample size, not bias. Do you have a large enough sample size to analyse? The problem with bias is that it is impossible to see the effect of it. The best we can do is expect a match rate that is the same as the size of the database relative to the population, even then this does not constitute proof of bias or its absence. And so perhaps the more important question than match rates is the nature of the source file itself – where does it come from and how does that relate to your target file? Size may not be a good guide, even very big may only constitute a small proportion of 14
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Fig. 1: Various data sources: unlocking new insights
the population (and perhaps a biased one at that), it is necessary to know here the data comes from and assess, to the best of your ability, whether that is biased in relation to your target file.
A Degree of Uncertainty Which leads neatly onto the question of what is this data? Researchers, quite rightly, are very concerned with precisely what question was asked, and precisely how was the answer list phrased and presented? And what was the manner and setting in which the question was asked? All this nuance may be lost when dealing with a database and a set of labels. All of this unknown adds a degree of uncertainty or noise in the new file. It is important to find out as much as you can about the data set you are appending, but not to expect perfection, or even perfect information about it.
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All Data is not Created Equal
Powering Insights
Typical data sources can be categorised into the following sections, first-party data, primary research data, second-party data and third-party data.
Integrating the right data across silos and gaining deeper insights of the attitudes and behaviours of ideal target audiences enables companies to formulate the right business strategy that includes product development, optimized marketing communication, and high-performing media and advertising promotions. To conclude, there are many obvious benefits to the appending of data taken from another source, such as asking the participant fewer questions and collecting accurate data that would be incorrectly recalled if asked. However, researchers need to remain mindful of the data sources they elect to integrate. The importance of trusted, GDPR compliant partners has never been greater when it comes to data. When selected carefully, these partners can unlock access to a multitude of data points which can only enhance the insights researchers are providing. ■
First-party data is an organisation’s audience data, generated and collected as part of ongoing business processes including CRM, business analytics, behavioural data, profile data et cetera. These address the What, as in what have consumers already clicked on, interacted with, bought, reviewed or referred. Primary research data – authentic, high-accuracy consumer insights data collected by a trusted, quality data service provider – is also categorised as first-party data when the provider has a direct, permissioned relationship with their panellists. Research data addresses questions of attitudes and behaviour – the Why consumers clicked on, interacted, bought, reviewed or referred a product or category and How. Second party data is another company’s first-party data and results from mutually beneficial data partnerships. Finally, third-party data is data that’s been collected from a variety of online and offline sources that can be integrated with first-party client data and primary research. These address questions of Who precisely the ideal target audience is and What Else they do outside the interactions with your company.
Panel Partners Data appending typically uses a combination of these data sources and generally falls into one of three types. The first type is where the data collection company appends some other data that it knows about its own panellists. In this instance, choice of panel partner is vital; size and scale is important but not to the detriment of quality. Research Now SSI have samples in 94 countries, offering scale but importantly also having over 450 attributes recorded on average for each panellist. This scale means a higher match rate and the in-depth knowledge of each panellist means that quality insights matching the client’s requirements can be integrated into the results.
Consumer Insights The second type of data append is using the clients’ own database. This will provide the exact information the client wants, and will typically help to address the What – as in, what have consumers already clicked on, interacted with, registered for, bought, reviewed, or referred. The level of matches and quality of the data is dependent on the size and organisation of the internal database for this type of append.
Reputable Sources The last type of data append is using third party databases to enhance the results with the information your client wants to know. It is in this arena that very large database suppliers, and those that offer probability based segmentations based on address, are most well-known. Again, it is extremely important when considering this option that the third-party source is reputable and high quality. As tempting as it may be to go for the cheapest option on the market, there can be trade-offs as a result and in a post GDPR world the risks may be greater than the rewards. Research Now SSI works with validated third-party partners to provide enhanced data integration, Mosaic, for example, has developed a multi-level segmentation system for both targeting and analysis appends, at neighbourhood, household and individual level, giving details insights into the audience. (fig 1) www.research-results.com
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Pete Cape Global Knowledge Director at Research Now SSI, is among the most respected, entertaining and thought-provoking presenters in the marketing research industry. Pete has almost 30 years of experience in market research and is a frequent speaker at conferences, seminars and training workshops around the globe as well as a regular contributor to research and marketing publications. www.researchnow.com
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Customer Centricity: Personas
Bring Your Buyer to Life 16
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ave you ever used segmentation outputs to develop an engaging marketing campaign? Or attempted to align your sales strategy to fit all your different customer types’ needs and wants? If your answer is “yes”, then you’re likely to know exactly how difficult it can be to get your creative juices flowing when all you have as a basis for your initiative is an abundance of customer data. The development of personas, descriptive summaries of a group of customers or target customers, embodied within a single character, enables you to bring your segments to life and to make them more tangible when developing marketing and sales initiatives. Focusing your attention on somebody, rather than anybody allows you to more effectively align your strategy around the target audience and to ultimately avoid substantial waste of resources and budgets.
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Personas are much more than just another marketing buzzword: Judith Wieghardt on how to create and embed buyer personas. A step-by-step guide with a focus on b2b markets.
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Creating Ficticious Customer Profiles The term persona was originally introduced by the ancient Romans, literally meaning mask or the character played by an actor. Over 2000 years later, website developers repurposed the term to represent typical visitor profiles that are to this day commonly used to improve the usability of websites. Shortly thereafter, marketers picked up the concept and applied it to the development of fictitious target customer profiles. There are five benefits of personas: 1. They provide companies with a better understanding of customers or target customers. 2. They allow companies to make better choices about who to target and who to ignore. 3. They bring segments to life, enabling internal staff to become more customer-centric. 4. They enable companies to talk to different groups within the market in ways that are relevant and impactful. 5. They help guide decisions on marketing, sales strategy and product or service development. With this in mind, the following article is a step-by-step guide for the development of buyer personas, with a specific focus on b2b markets. Of course, there is no “one size fits all” solution for building and embedding b2b personas, as b2b customer types and their needs are as diverse as b2b markets themselves. Nevertheless, these four steps will give you a rough guideline and considerations to take into account along the way.
Step 1: Gathering Insights to Lay the Foundation The starting point of any persona profiling effort is the collection of insights. These insights can be variously obtained. Typically, personas are derived from an extensive and mostly quantitative customer segmentation. Following this approach, each segment usually lends itself to the creation of one persona. Both segmentation and persona profiling share the aim of establishing distinct and targetable groups within an organisation’s customer base and/or market. Due to their similar objectives, personas and segments are often confused with one another and therefore personas have been criticised as just another marketing buzzword. This is not the case. The difference lies in the presentation of the two. Whereas the output of a segmentation is a description of a customer type, described in a more all-encompassing way (that is, Segment A, the “Price Buyer”), the persona – as the name suggests – is a personified way of describing the very essence of a typical, “best fit” character of this customer group (that is, Steve, the Facility Manager, who wants to get the best deals to look good in front of his boss). Even though the difference between the two appears minimal, it ultimately comes down to human psychology and the fact that we can much better empathise with a person than with a faceless segment. www.research-results.com
Collect Data Extensively Even though personas are often derived from segmentation outputs, the persona creation journey needn’t include segmentation – at least not in its traditional form. Some opt for a qualitative segmentation, whilst others choose to use secondary sources to inform their persona development. Certainly, wherever possible, an extensive collection of primary data is the preferred option. Regardless of which route you pursue, it’s important not to let perfection be the enemy of good. An imperfect achievable outcome is still better than a perfect unachievable outcome. Consider the following sources as a foundation for your persona development: • primary market research (such as customer interviews, surveys, focus groups) • secondary market research (such as reports, articles, industry publications) • company internal data (that is, on customers, markets, sales figures) • anecdotal insight (drawn from experience)
Do Not Underestimate Experience Beware not to underestimate the importance of experience. Utilising internal knowledge and anecdotal insights (for example from the sales force) can be a valuable exercise, especially in the early stages of persona-related insight generation. Ultimately, organisations should work with what they have and what they can feasibly obtain.
Step 2: Bringing Your Persona to Life After the insights are collected and analysed, you can move on to the development of your personas. This is most often done in the form of a brainstorming session or workshop among members of the marketing team, but involving a range of departments (sales, customer service, senior management, back office functions) is likely to deliver a wider spectrum of ideas and should be encouraged.
Get a Holistic View Before you jump ahead to the “fun part” of giving your persona an identity, make sure you have a sufficient idea of the customer group the persona is aiming to represent. Do you know the typical industry, company size, structure, individual role, needs and behaviours of your segment? Having all the information at hand will provide a more holistic view of your segment.
Be Creative Now that you have the “boring stuff” out of the way, it’s time to move on to creating your persona and getting the creative juices flowing. Whilst being a personified representation of a segment, a persona should essentially have the traits of a seemingly real-life person (that is, have a job, a family, a favourite book or film, hobbies, interests, ambitions). esearch &
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Variety: Personas can be visualised in a range of media
Moving on from “What needs does segment A have?” to “What is Steve’s favourite film?” is not easy. Example exercises that can help you in taking this leap are: 1. Describe a day in the life of the persona (strong, tangible link with the business; little creativity required). 2. Create a LinkedIn profile for the persona (focus on professional needs and behaviours, direct link with the business; more creativity required). 3. Create a Facebook profile for the persona (focus on personal needs and behaviours, indirect link with the business; most creativity required).
Find Out What Is Relevant The question you should always ask yourself when building your persona is: “Is this information or characteristic directly or indirectly relevant to how we can serve this segment?” Irrelevant facts might only detract from the essentials and lead to confusion. Specifically when thinking about the persona’s personal life, defining facts that are relevant can be difficult – but not impossible. Could an interest in sci-fi books for example point towards a particular interest in new technologies? Don’t be scared to come up with “crazy ideas” – they tend to be the best. Even if they might seem somewhat absurd to you, they will guide your conversation into a creative direction.
Step 3: Embedding Personas In Your Organisation Once your “family” of personas is in place, the journey does not end. On the contrary, this is merely the beginning. It is likely at this point that each persona is familiar only to a small group of people within the organisation, and only exists in the form of scribbled notes and (possibly) the odd sketch. The personas must now be brought to life and introduced to the rest of the organisation. 18
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Visualise the Personas Variety is the spice of life and organisations should endeavour to visualise their personas using a range of media. Examples adopted by others include: • Profile posters with photographs of each persona, accompanied with key characteristics and a description of key needs, products, sales/marketing messages which may resonate strongly. These posters should be distributed to employees and prominently displayed in offices or locations. You may also wish to create cardboard cut-outs of the personas, which then take up residence in meeting rooms or other office space. During internal discussions, staff should look over their shoulders and ask “What would Steve say?” • LinkedIn and/or Facebook profiles, designed to look realistic and sometimes actually created (enabling “connections” or “friendships” with people within the organisation) (see figures above). • Email messages from personas to introduce them to the organisation and to enable a direct interaction with them. Personal contact with personas triggers interest and deepens understanding of different customer segments. • Online portals, offering an interactive platform to explore the different aspects of a persona’s life (for example, having a look at the persona’s work desk, his/her social media profiles) and to offer extensive information in an easily digestible format. • Face-to-face interactions with actors playing personas at internal events bring personas to life in a literal sense, initiating further conversation and making it a memorable experience for each employee.
Create a Long-Lasting Impression The ways in which a persona can be visualised and embedded within an organisation are endless. Try to think of other ways in which you could encourage a conversation about your personas and create a long-lasting impression among your colleagues. The ultimate goal here is to develop a company culture of customer centricity, helping www.research-results.com
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The ways in which a persona can be visualised and embedded within an organisation are endless.
to keep the customer (and different customer types) at the forefront of all strategic decisions and in everyday operations.
Step 4: Keeping Your Personas Up-To-Date Markets, competitors and customer requirements are constantly evolving. Therefore, it is essential that you revisit personas periodically. This process should be done twofold:
Keep the Conversation Going The best way to keep your personas relevant is to spark on-going anecdotal conversations discussing how the persona could have evolved over the past few months (How would Steve be feeling about the new industry regulations affecting companies like his?). Ideally these conversations should happen frequently. If you’re finding that this isn’t naturally the case, make an effort to get together to discuss every three months.
Do Formal Persona Audits Every three to four years, you should revisit the whole persona creation process, refreshing any research you may have, creating personas from scratch and developing a revised strategy on embedding personas in your organisation. It is not recommended to carry out the process more frequently than every three years, as you can easily lose momentum and fail to take full advantage of the benefits of your existing personas. ■
Judith Wieghardt is Project & Marketing Manager at B2B International. www.b2binternational.com
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When market research clients design and script questionnaires, various problems can occur. Danyo Dimitrov describes the most common ones.
M
arket research (MR) is here to the rescue, trying to explain why we all act the way we do, at least as consumers. For all of us professionally involved in research, it is clear that most of the answers appear in the consumer subconscious and for whatever reason vary in time. Quite a lot of words to simply say that in order to know what is in the consumer’s mind, we need to get on their wave length.
Sample-only Projects
Ironing out the Details 20
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Problems in the Questionnaire Even though this matter sounds a bit too general, it usually demonstrates itself in three very concrete directions: (a) question formulation, order and organization is not ideal; (b) survey is scripted in an odd way or (c) look and feel and overall questionnaire visual representation make completing it more difficult than necessary. When it comes to question order, it is often the case that key demographic questions used for screening out or quota counts are placed very late. This inevitably causes problems with respondent experience as they drop out after spending considerable time providing answers. This may have feasibility implications and will probably add cost to your budget. The most obvious and quickest solution is to rearrange the order of the questions, but the tricky bit is to identify the problematic potential early enough, when such amendments are still possible. www.research-results.com
Photos: © MIKE RICHTER; amnach – fotolia.com, Company
Solving Questionnaire Design and Scripting Problems for Research Clients
A crucial step in getting all the essence and nuance of consumer opinion is to ensure the actual survey is prepared and run correctly, avoiding as many hiccups along the way as possible. One may say this goes without saying and an experienced MR agency should have no problems with putting together and running a successful study. Unfortunately, our experience in servicing all types and sizes of MR clients shows a different story. The findings presented in this article are based on a thorough analysis of 5067 online MR projects that experienced some commonly seen problem, run by JTN Research for various clients in the last three years – 2015, 2016, 2017. All the market research projects in focus here are qualified as “sample-only” for JTN Research, which means that the client is responsible for questionnaire design and scripting, and only requires online panel samples from JTN Research. The results are put together by analyzing the projects’ final reports. These reports are produced by the respective project manager upon closing the given study, right after invoicing, and outline reasons for delays, price updates and other unexpected problems with the given job. Below are the most common problems observed.
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Unfortunately, questionnaire designs sometimes suffer from poor question and answer formulation. “Have you bought a EUR 4,000 watch in the past three months? Yes/No”. Having too obvious answers or screening questions that suggest the “right” answer always leads to poor data quality. Surprisingly our analysis shows that this problem is just as common in large and experienced MR agencies (twelve percent of questionnaire-related problems), as it is with smaller and newer MR clients (eleven percent of the questionnaire-related problems).
Multiple Cell Surveys Focusing on scripting, the complications with structure most often appear in multiple cell surveys. Some studies require two, three or more concepts to be evaluated separately by the same target group. This would mean that each cell has to be evaluated by the same number of respondents, who are balanced in exactly the same way. The problem occurs, when a quicker and more obvious course of action is chosen and the different cells are simply divided into different survey links. This adds unnecessary complexity to the fieldwork, as splitting the sample audience into several portions increases work hours for sampling and creates too much additional hassle at the end of fieldwork, when databases have to be put together. The most useful solution in such a case is to have the so-called least full quota setting. This setting works through a script in the questionnaire, which assigns similar demographic balance in each separate quota cell. This helpful setting adds a bit more work for the programmer, but saves a lot of effort with fieldwork management, and ensures overall budget is kept in check.
Most Common Survey Problems – Combined Results from All Types of Clients Unnecessary nat rep quotas Too many quotas
15 % 12 % 49 %
No sample plan/poor FW planning 42 %
Non-DA survey 19 %
LOI of over 30 minutes Question/answer formulation
12 %
Questionnaire structure
Fig. 1
7%
Source: JTN Research
It goes without saying that what the respondents see in the questionnaire affects their answers and overall survey satisfaction. A very common example is having radio buttons that are too hard to click on correctly and can cause the person to click the wrong option. Another big “no-no” are huge exhausting grids, usually those would be designs with more than five columns and more than six statements on the same screen, or more than several consecutive grid questions. Luckily, in recent years all professional survey programming platforms have developed a wide variety of solutions to guarantee pleasant respondent experience, providing alternatives to repetitive items.
Length of Interview (LOI) LOI is probably the most discussed topic whenever a research project is planned. The end client is always willing to maximize the information obtained with a single study, by asking as many questions as possible, getting all knowledge at once. All MR agencies and panel providers know too well is that the longer the study the more problems there are with data www.research-results.com
DIY Programming Platforms Living in the age of automation, there is increasing interest in DIY survey programming. There are certain benefits to it, like speed, not having to get in touch with programmers, maybe reducing overall costs. Unfortunately, the functionalities these DIY platforms offer are still nowhere near the professional authoring tools out there, and not relying on an experienced survey scripter will very often backfire. The usual complications are limited look & feel and programming options, hard to add sample providers to the study, almost impossible to implement data quality checks (like GeoIP, deduplication, etc.) among others. Having said all this, there is no need to avoid DIY survey programming altogether, but objective evaluation of the complexity of the questionnaire before going for the DIY solution is required.
Device Agnostic Surveys
28 %
Imperfect survey programming
quality. It is quite understandable that completing a study for 35-40 minutes is tiring. Losing interest as a respondent inevitably leads to paying less attention in the questions and answer options. There have been several attempts to solve the LOI problem technologically, applying data weighting techniques, but so far the low-tech solution of getting the project priorities straight and removing the non-vital questions is still the most effective.
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There is abundant evidence supporting device agnostic (DA) survey design. In the mobile era, not being able to complete the study on a smartphone or a tablet is the equivalent of losing at least 50 percent of your eligible sample before even starting data collection. Still, based on our data, 42 percent of all surveys in this analysis are non-DA. About 67 percent of the non-DA projects are commissioned by the larger online MR agencies, where small and medium MR clients tend to be more up-to-date when it comes to mobile survey access. The main reasons behind such results lie in multiple-year tracker jobs. On many occasions end clients are unwilling to make changes to the long-standing design in fear of losing data continuity and comparability between years. However, deciding esults International Issue · 2018/2019
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Quota Setting
Most Common Survey Problems – Results from Large Market Research Agencies Only Unnecessary nat rep quotas Too many quotas
16 % 9%
No sample plan/ poor FW planning
39 %
Non-DA survey LOI of over 30 minutes 11 %
Question/answer formulation Imperfect survey programming Questionnaire structure
Fig. 2
67 %
20 %
10 % 6%
Source: JTN Research
Most Common Survey Problems – Results from Small & Medium Market Research Agencies Only 14 %
Unnecessary nat rep quotas
17 %
Too many quotas No sample plan/ poor FW planning Non-DA survey
64 % 5% 18 %
LOI of over 30 minutes Question/answer formulation
12 % 55 %
Imperfect survey programming Questionnaire structure
Fig. 3
9%
Source: JTN Research
against DA is definitely tantamount to not having the opinion of the majority of mobile device users, which one may argue generates significant inaccuracy.
Organizing Fieldwork and Sample Delivery Practice and experience indicate that the best way to avoid data collection problems and unexpected delays is having a sample plan before starting fieldwork. That involves several important decisions – where is sample coming from; if multiple providers are on board, how will they balance delivery; which are the toughest demographic/consumer groups targeted, so that these can be given enough time in field; what are the key metrics to monitor as an early warning indication? Having these imperative variables sorted often makes the difference between finishing your study on time and budget, and experiencing delays and unexpected extra expenses. Being on the same page with your panel provider is probably the easiest way to prevent unpleasant surprises. Practically, this means having them start with the toughest groups and not rushing to get as many interviews as possible in the first few days. After all, nobody wants to end up looking for young, unemployed males who own an expensive sports car one day before fieldwork is supposed to finish. Planning ahead is the key to avoiding such traps. 22
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Everybody doing a MR study is willing to be able to transfer their findings to the general population. This brings credibility and weight to any MR results. The established way of achieving this is by applying national representative distribution quotas, also referred to as nat rep quotas. Nat rep has become such a holy grail in our industry that it is a requirement in 93 percent of all requests for quotes, and applied as quota stop in more than 90 percent of all projects. However, getting the nat rep validity stamp on your survey data sometimes works against truthful findings and often brings in unnecessary bias. Imagine you are looking to interview only consumers of a specific male aftershave brand. Applying nat rep quotas on a completed interview basis would mean that this brand is proportionately used by men in all ages and regions in the given market. Most definitely untrue, or if such a brand exists, its marketing people are probably the prophets of modern era advertising, successfully reaching all ages and regions in the market. A much more accurate approach would be to go for nat rep spread on completed + screened out respondents. This reaches the audience in the right proportions, but brand usage and awareness is not forced upon completion, but accurately measured. Unfortunately, a very small portion (about three percent) of the projects analyzed here apply this approach. Of course if one only needs the opinion of brand users, then the quota limits have to be reviewed accordingly to avoid bias.
Programmatic Sample and Online Sample Marketplaces Another very modern characteristic of our automation era is getting sample from platforms that put together multiple sample providers, allowing clients to purchase and launch sample quickly, at good cost levels and without having to know providers in the specific market they need to run fieldwork. Obviously, there are quite a few benefits for clients, attracting them more and more to these solutions. After all, who does not like having quick and affordable access to sample in almost every country in the world? Still, there are a number of considerations to be www.research-results.com
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taken into account. Having no contact with the actual provider means that one needs to be very confident in the country-specific knowledge they have of the specific market. In other words, one works under the assumption that their questionnaire is perfectly adapted to the market they are running it in. Receiving that country-specific information from the local provider is on many occasions crucial. Another important matter is sample quality. Keeping interview costs at such levels often indicates that the source is not panel, but river or randomized databases, where respondent incentive is very small or practically non-existent. Also router usage within these platforms implies that a single respondent can take more than one study in one go, bringing that person closer to the definition of “professional panelist”. As the old saying goes – no product can be of high quality, cheap and delivered quickly at the same time, something has to give. Statistics clearly indicates that on average about two of the listed problems are observed in the research jobs analyzed. Thus it is safe to conclude that it is much more likely to see a combination of complications, rather than a standalone issue. The reasons behind these results are hard to identify, but the steady growth and intensive innovation in the online MR industry we have been observing in the past few years – are definitely major factors. Businesses require much more insights, in limited time frames, done in many more markets and many more environments, than before. This obviously puts significant strain on the research workforce, causing us to inevitably take shortcuts as we struggle to keep up with innovation in MR industry. Still, one thing is clear: We definitely live in times of change and growing the opportunity. ■
Danyo Dimitrov has been working in market research since 2002. For the last six years he has been a part of JTN Research, leading the company as Managing Director since the end of 2014. His research background consists of both operations and analytics work, as well as online panel construction and management. Danyo’s professional interests are focused on enhancing respondent experience and interdisciplinary research synergies. www.jtnresearch.com Advertisement
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Efficiently Working Operations Teams
Ultimate
“C
Understanding Global data collection enriched with local features: That is just one challenge of market researchers today. Dezső Karasszon, Bence Tóth, Csaba Csürke and Zsolt Apponyi know what makes operations teams successful.
ustomers don’t measure you on how hard you tried, they measure you on what you deliver.” I often think of this Steve Jobs’ quote when considering how to motivate our employees. European culture recognises and appreciates effort and fight. Of course, results cannot be achieved without hard work and we should tell our young colleagues – the future generation no less – success has no secrets but a price. To achieve results you have to be diligent, there are no shortcuts, no magic recipe, only effort and performance matters. But hard work alone will not deliver results.
Not only about Recognition
Photo: © alphaspirit – fotolia.com
At DataExpert, we have a tradition. Every Friday after work, the whole company gathers to recognise colleagues who have achieved, and we reward the best with some luxury, Hungarian chocolate. This is our ‘Friday Choco late’ event. Recognition motivates people, so it is worth building a company culture around it. Outstanding performance should never go unsung! However it is a serious
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Quality is not what we do, or what our partner asks us to do – we technical people have to recognise the value we generate for our partner.
management challenge, determining who should be recognised as an outstanding performer at our Friday Chocolate event. Should it be the colleagues who worked some overtime and – despite their heroic struggles on an eventful project – which had many twists and turns successfully completed it and received good customer feedback? Or those colleagues who planned their work really well and used some innovative solutions that led to smooth-as-silk technical execution?
Value for the Client But instead of contrasting these two situations, let’s take a different approach: A company like DataExpert, providing market-research technical support, can only measure performance in terms of the results achieved by the client. Quality is not what we do, or what our partner asks us to do – we technical people have to recognise the value we generate for our partner. To illustrate this, here are a few case studies which also include mention of some trends experienced in our profession in the late 2010s.
Example from China This is one of my favourite customer success stories. In a conference call with an Asian client, we covered everything they considered important and what we could provide them with. Then they asked this question: Can we give support for online data collection for a special target group in China? In China, the internet operates through different principles (especially during party congress), meaning that the questionnaires have to be programmed differently. Google references should not be used as they don’t work in that country. All other template references should be constructed from files which can pass through the Great Firewall of China. In general, all image files, CSS and JavaScript references should be made as small as possible and access should be efficiently managed, as the internet speed may be slow for some questionnaire respondents. Our aim is to run the system as smoothly as possible, ensuring the best user-experience. We made all the necessary changes to the Nebu software used by the client, while remaining technically able to technically simulate the test environment as if we were actually in China. Of course, I always want to help, but I had one question in my mind: It’s fine that we should choose the best technical solution and the questionnaire should be quick, but would we ultimately succeed as indicated, in generating the necessary N for our client? That is to say, would enough people respond? We took on the challenge and within a few days received word (“This is to let you know that we got the additional N in China over the weekend thanks to your help!”) from our client. They had reached the necessary number of respondents!
Where Success Hides What this example illustrates is that success for a company delivering technical solutions is not just about executing what the customer wants, nor is it just providing creativity and the highest standard of work. Success for us lies in the customer achieving its goal. What would be the point of offering the best technical support if our partner cannot perform as contracted?
Beneficial Dashboards Data visualisation is necessary as it helps us make good business decisions. When a dashboard is created, the most important question is whether the users will benefit from it. Is the dashboard just there or does it actually help users to achieve their final goal and improve their business? How can we – dashboard designers and dashboard builders – move forward with this? With certain dashboard software you can track which dashboard pages were visited and when. Such is the case with Dapresy’s so called User Statistics – Dashboard usage history. A dashboard is similar to a news portal, in that you need to keep a close eye on it to optimise how it www.research-results.com
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is used and discover the best layout for the monitors, filters and navigations. If a page is not visited in sufficient numbers but is business critical, there are technical measures that can be taken to draw attention to that page. Again, we ask the question: Is it enough to prepare a reporting system according to the customer’s needs, based on state-of-theart technological expectations, with a fancy design? This is all well and good, but the main goal is to make sure good business decisions take place, and that can only be achieved by keeping the dashboard in line with business needs. What options can we apply to achieve this?
Different Backgrounds First, we need to understand that not all users have an in depth market research background, so there are times when the first step is to familiarise the end-user with the possibilities a dashboard offers them. One of Dapresy’s strengths is the ability to customise the layout and functionality based on the client’s needs. For example, you can design the dashboard as intuitively as required to help unexperienced users to understand where the first click takes them, what analytical functionality is available and how to link insights to action to maximise portal utilisation.
Involving the Users Here are a couple more examples of how the Dapresy software provides options that involve the user. If you need to be alerted when there is a critically low value on the respondent or total level, users can be notified by e-mail (based on a pre-defined algorithm) and are allowed to step in; they can enter the system and get more detailed information about why the scores dropped.
Combining Visualisation and Collection The user can also give an open response to a question related to shown data. This blurs the boundaries of data visualisation and data collection, and helps to make sure that they not only clicked on a given page, but understand and are thinking more about what they have seen. As a technical support team our job is to advise our client, and also to see data collection, data management and data visualisation as a whole rather than as separate parts. As such, as part of our vision we have already built a team which has a wide range of expertise – such as data management and design knowledge – available in a united ecosystem, and where team members develop that expertise in a considerable number of areas. The next example illustrates this.
End-to-End Automation One of our international end-users asked us to automate their regular data collection among their suppliers. They wanted to have the sample management, data collection, sending of reminders and exporting of results all in one system. When we questioned them on why they wanted it, they told us it was to increase the proportion of sustainable sources that the raw materials for their packaging came from. By asking why and focusing on our partner’s interests we developed a solution of running an automated data collection system with end-to-end automation which would allow the customer’s staff to collect the necessary 26
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data from the suppliers as efficiently as possible. The system was built in Dimensions (Unicom Intelligence), however even hard-core Dim users would not recognise the system serving the integrated data collection and reporting. What are the main features of the system?
Users as Managers Users are managers of each category. After logging in, they only see their own category. The system sends automated invitations every quarter to current suppliers, after which users can track who has filled out the data collection forms, see the answers question by question and track who started responding but did not finish. If necessary, the system automatically sends reminders by algorithm to the appropriate contacts. At the end of the survey, data can be seen at both the aggregated and record level. In addition, pre-defined charts serve to let anyone immediately see the levels of raw materials from sustainable sources at any given time. The respondents are the customer’s suppliers. They provide the percentages of raw materials, and can also keep track of any difference from the previous data collection round. They can see the category benchmarks if needed, and upload certain files to the user interface – most commonly certificates.
The Right Information, Lower Costs Some important highlights of this project were combining increased efficiency with reduced costs and at the same time providing the right information to the right people – everyone seeing only the information they needed, but no more. The result was that most category managers and suppliers – that is non-professional users – didn’t realise that they were in a data-collection system, the necessary information was collected and immediately available in real time. And finally everything was provided to achieve the ultimate goal of environmentally friendly use of materials!
Task of Market Research Operations Support in 2018 What can we learn from these case studies and what principles should we consider ahead of the 2018 Research & Results exhibition? • Data collection is a global activity, but considering local features is a must. • Reporting requires data visualisation interactivity, two way communication and continuous development. • The boundaries between data collection, data processing and data visualisation are somewhat blurred. • Now is the time to build integrated solutions and end-to-end automation. These challenges can be taken up only by operations teams, the members of which understand the customer and its ultimate business goal. Their work must contribute to the success of their customer and help them gain a competitive edge in the market. Operations teams must be an essential driving force for innovation in our market research business. We would love to hear your thoughts and experience on this in Munich, at the 2018 Research & Results exhibition. Hope to see you there! ■ www.research-results.com
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Dezső Karasszon
Csaba Csürke
CEO and co-founder of DataExpert. Alongside Zoltán Vasvári and his 60+ colleagues he is setting up an organisation where marketing and innovation go hand in hand. DataExpert has been working with many of the top professionals in the market research industry since 2006 and is happy to be one of the exhibitors at Research & Results.
has been with DataExpert for 4 years and is currently head of the Data Visualisation department. During that time he has been juggling data and graphics projects and fulfilling clients’ expectations at the highest level. He enjoys discovering the possibilities behind reporting software and supporting customers with the best options and proposals.
Bence Tóth
Zsolt Apponyi has been with DataExpert since 2014. As an innovation specialist he looks after custom developments and endto-end solutions, supporting all of the process areas for partner markets. He also enjoys broadening the knowledge base of his colleagues and continually improving workflow efficiency. www.dataexpert.hu
has been developing tailor made solutions for clients with DataExpert since he joined in 2013. He has high level survey scripting expertise and strong front end development skills. When he’s not solving complex problems, he’s taking photographs, which he believes helps him with his work in seeing the big picture and noticing small details.
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From Competition to Collaboration
(Wo)man
Machine Can artificial intelligence help future-proof qualitative research? Julia Görnandt and Samantha Bond explain how automation makes sense.
I
t is no secret the market research industry is under pressure to deliver sound and strategic insights within shrinking budgets and timeframes. In fact, ten years from now efficiency is predicted to be the deciding factor when commissioning research. This presents a serious challenge for qualitative research, with traditional methods being directly at odds with the need for speed. However, demand for qualitative insight is predicted to increase, driven by a need to convert big data into smart data and provide meaning, clarity and focus to the growing sea of numbers. Confronted with this scenario, SKIM was keen to investigate potential solutions to ensure qualitative research remains an accessible source of insight for our clients.
An Intelligent Solution?
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Photos: © SKIM Group
From Uber’s self-driving cars to Amazon’s warehouse robots, artificial intelligence (AI) seems to be reaching human-level dexterity, with robots taking on the role of professionals across different industries, from law to healthcare to education. But does this apply to market research? SKIM was curious to explore the possibility, to find out whether machines can identify insights and speed up the qualitative research process. At the same time, we wanted to explore the potential trade-offs and whether robots will render human researchers unnecessary. www.research-results.com
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Head-to-Head Competition William Serfaty, global strategy and insights manager at Danone, turned to SKIM to understand the drivers of consumption for a new product to inform Danone’s communication strategy. Given the very tight timeline for this project, we saw an opportunity to put these robots to the test. With Danone on-board, we partnered with Voxpopme, a video research platform that captures in just a few days, in-context, consumer feedback at scale and provides a range of AI and automation tools to analyse the data. Together, we launched a head-to-head competition between human researchers and machines. Two teams were formed, each tasked with analysing 127 self-recorded consumer videos using different research methodologies. One team had access to AI and automated tools, while the other team relied on traditional human methods of analysis. In total three reports were produced and judged by William Serfarty at Danone: • automated topline • human-only full report • automated + human analysis full report Each team was required to log the amount of time spent analysing the videos and creating each report. By doing so we could compare and evaluate the process and insights produced by machines, humans and a combination of the two.
The Winner Much to our surprise the winner of this competition was the automated plus human team, based on the full report created by human and machine analysis. By using this collaborative approach we were able to produce a full report in half the time it took the human analysis team. However, most unexpectedly, William selected this as his favourite option even before learning it took less time to create. In fact, he deemed both full reports to be equally insightful and actionable and had real trouble telling them apart. This means that by collaborating with machines we can enhance efficiency without compromising the depth and quality of insights. However, it is important to emphasize that collaboration is key. The machine on its own could not produce meaningful insights and the topline output that relied the most on automated tools was not deemed sufficient as a final deliverable. Whilst only taking two days (versus five days) to produce and costing two-thirds of the price, efficiency savings incurred were too much to the detriment of the insights gained. In essence, the trade-off was too great.
The Need for Speed – and Quality Our research therefore validates the use of automated tools during qualitative research analysis whilst also revealing the importance of humans in driving the analysis process. The role of automated tools being to facilitate, not replace researchers. At SKIM we believe these tools can help the qualitative industry adapt its processes and remain accessible to clients in the face of increasing efficiency pressures and achieve the seemingly impossible goal of delivering on time, money and quality expectations, without any trade-off. It is www.research-results.com
for this reason that William was so pleased by the outcome of this experiment and sees this methodology as a way to bridge his stakeholders’ need for both speed and quality. “The outcome was a nice surprise! Now we can get a report faster that provides the level of detail you’d get from a traditional report”, said William Serfaty.
Five Tips on how to Collaborate with Machines Based on our experiment we have identified five tips on how to take advantage of this technology and collaborate with machines during qualitative analysis. 1: Initial scepticism of AI will be inevitable; push past it to reap the rewards of automation. Originally we believed that opportunities to automate were more suited to structured, quantitative research processes. Given the human nature of qualitative research, we went into this experiment questioning whether automation is even possible. Although we’ve seen developments in natural language processing, at this point we are far from achieving total automation. With this in mind, our automated team was initially sceptical and resistant to using the AI tools. It was therefore necessary to overcome this hurdle in order to benefit from automation. By doing, so we discovered the opportunity to halve the time it takes to create an equally insightful full report. 2: Don’t expect machines to provide the answers. While there is much industry hype around AI and other next-gen automation technologies, we learned that machines do not offer a “magic bullet” solution to fulfil a brand’s insight needs. When conducting qualitative analysis, there is currently limited value in automated tools without human involvement. The outputs esearch &
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produced are words and charts that hold little meaning on their own and with mixed levels of accuracy. Machines can’t connect the dots, determine which of the insights are truly key or identify the drivers. Even to create an initial topline report, human analysis is required to review automated outputs, understand their meaning, and narrow down which information is relevant. While in time it is likely their intelligence will increase, for now at least, automation tools cannot provide stand-alone answers. As a result, it is important to understand how best to use them to our advantage. 3: Use AI outputs as the starting point for human analysis. While they don’t provide a magic bullet solution, we learned that automation tools can empower qualitative researchers to conduct analysis at speed. In contrast to our human analysis team, which had to spend a week reviewing all the video transcripts, the starting point for our automated team was the machine outputs. By analysing these, rather than the raw data, within just one day we were able to build up a picture of the overall story and identify key learnings. 4: Expect high-speed analysis to produce high-level findings. When turning to these tools it is important to have the right expectations. If internal time pressure means immediate answers are required, this technology can certainly help. However, the result of high-speed analysis is a bird’s-eye view, meaning very highlevel findings – not the deep dive insights and strategic recommendations clients have come to expect from qualitative studies. 5: Being strategic takes time; don’t cut this corner. Even once we revealed the time and cost involved in creating each of the outputs, Danone’s William Serfaty felt the collaborative (automated + human researcher) report delivered greater returnon-investment over the mostly automated and fully human-generated versions. Thus, reports that rely heavily on automated out30
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puts may be quicker, but speed comes at the cost of strategic and actionable insights.
The Future Potential of AI More time and deeper human analysis is therefore required to translate high-level information into clear guidelines and recommendations. Nevertheless, this process takes qualitative researchers half the time when armed with automated tools to help them. As a result, at SKIM we’re understandably optimistic about the future potential for automation and AI-enhanced qualitative research methodologies. Automated charts, thematic text analysis and filters are basic forms of automation compared to sentiment and object analysis that require machine intelligence. Yet, at this stage it is the basic tools that are most valuable in enabling qualitative researchers to efficiently analyse large quantities of data at speed.
Take Advantage of Automation Yet even despite their limited sophistication, automated tools were able to halve the time it takes to create a full report without compromising the depth and quality of insights. Given this result, SKIM has introduced an automation solution to our qualitative research offerings, SKIM’s Smart Qual. We use this solution as a standalone methodology, pre/post task, or to replace traditional open-ends in surveys to capture in-context insights at speed. As technology advances and becomes more sophisticated, we believe the uptake and benefit of using these tools will only increase and help qualitative researchers to deliver strategic insights faster. At SKIM we are therefore keen to continue our investigation in this area, learning how to collaborate with machines and take advantage of AI and automation! ■ www.research-results.com
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Julia Görnandt is the Country Manager Germany at the insights agency SKIM. She is mainly working with leading Consumer Health and FMCG clients in the DACH region to understand and influence buying decisions across all channels. Julia holds an MSc in Psychology (minor in Rhetoric) from the University of Tübingen, Germany. Before joining the market research industry, she worked as a researcher at the Psychology Department of Stanford University, California.
Samantha Bond specializes in international qualitative research at SKIM. She has six years of experience consulting on the design and execution of complex multi-market qualitative projects, for a range of industry sectors, using both traditional and innovative methodologies (including mobile, biometrics, AI/automation). Samantha holds a 1st in Sociology from the University of Nottingham. In 2017 she won ESOMAR’s Qualitative Excellence award. www.skimgroup.com
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Conjoint Analysis
Do it Right! Sören Scholz discusses the dos and don’ts in product research and pricing – and presents a remedy for notorious problems.
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nderstanding consumer preferences and needs is the most important basis for making successful decisions in almost every business sector, especially product and price management. At the same time this is one of the biggest challenges. Why? Although consumers may have clear preferences for products and prices, they often are not aware of them. This has two main reasons: On the one hand, most decisions are made unconsciously, thus consumers decide mostly according to their feeling and personal perception. On the other hand, products are becoming increasingly similar and price differences less comprehensible. Therefore, market research needs methods that take these circumstances into account. The most common answer to the question “what method could this be?” is: Conjoint Analysis. www.research-results.com
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Fig. 1: Assumptions of traditional CBC
It’s All About Conjoint Since the development of the traditional conjoint measurement approach by Amos Tversky in 1964, it has enjoyed an unparalleled success in market research. Conjoint Analysis is the most public and commonly used quantitative method. Over 100,000 articles have been published on this subject. If you look more closely at this kind of method, you find that there are so many different variants: Adaptive Conjoint Analysis (ACA), Choice-Based Conjoint Analysis (CBC), Hierarchical Individualized Limited Conjoint Analysis (HILCA), Best-Worst-Conjoint, Discrete Choice Modeling (DCM), and many more. Hence, the most important thing is to choose the right and suitable type for each problem. In reality, though, most people think of the traditional Choice-Based Conjoint (CBC) when they think about conjoint analysis.
Allow For Some Limitations The CBC subjects choose their preferred product several times from a limited set of alternatives. In this way, consumer preferences for specific features (also known as utilities) and products with the optimal combination of features can be determined. In addition, the identified consumer preferences can be used to predict consumer behavior, and thus to forecast the market potential of new products. Overall, the CBC has one particular advantage: it has a high degree of similarity to real purchasing decisions. However, some requirements must be met in order for it to work properly. First and foremost: The buying decisions have to be simple, everyday buying decisions and the products have to be things like chocolate bars, wall paint, and cream. When being applied to more complex products, for example the purchase of a kitchen, car or insurance policy, the CBC comes up against its limits. www.research-results.com
The reason for this lies in the assumptions needed to come up with accurate estimates of the perceived value of product features, also known as part-worth utilities. In brief, CBC assumes: • maximum information transparency • fully compensatory decisions • low cognitive load • fast and simple decisions While these situations may be prevalent in many purchase situations for fast-moving consumer goods, this is not necessarily the case for many other, more complex purchase decisions. Figure 1 outlines the underlying dimensions most crucial to better understanding whether the above assumptions are justified or not. (fig. 1)
Product Complexity Let’s consider the purchase decision for an aeroplane flight. Here, various aspects are critical to understand why customers buy which f light ticket, given a large range of competing offers. For instance, the carrier, the location and accessibility of the airport, parking facilities and – last but not least – pricing are relevant aspects which profoundly affect the purchase. Figure 2 shows the typical representation of a choice task in CBC for this decision. Keeping in mind that the respondent has to answer several of these choice tasks, the substantial cognitive efforts that respondents have to take out easily becomes evident. In a nutshell, respondents are easily overwhelmed by the sheer plethora of information. For this reason, already Paul E. Green, one of the founding fathers of Conjoint Analysis, pinpointed that the number of product features included in CBC should be six to eight at maximum. When exceeding these limits, respondents tend to ignore some of the product esearch &
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Fig. 2: Typical representation of a choice task in CBC
attributes or use simplification heuristics, such as “the price must not exceed X €” in order to reduce the cognitive load. This, however, violates the assumption of low cognitive load and – as a consequence – fully compensatory decision behavior. (fig. 2)
Purchase Situation One of the most prominent strengths of CBC is that it closely mimics real purchase decisions. While this is largely applied for standard buying behavior at the point-ofsale (in grocery stores for instance), there are numerous purchase situations in which this is not the case. Let’s get back to the aeroplane f light example above. Here, consumers are not able to see all information simultaneously but have to actively obtain information on the offers available on the market – mainly by running search queries on the internet and sometimes even by reading the fine print of the product pages. Therefore, information search behavior is crucial but highly neglected by traditional conjoint approaches, such as CBC. Rather, CBC provides all information at once – typically in the form of a highly-structured table (see figure 2). While this presentation is easy to grasp for the respondents, there are many real-world purchase decisions where consumers are not able or willing to work so hard to obtain all the required information. For this reason, the assumption of fast and simple decisions may not be adequate in many more complex processes.
Market Complexity As outlined above, CBC mimics simple decisions between only a few product alternatives. In many markets, however, there are dozens of different competing product offerings. Thus, the large number of available product alternatives deteriorates the underlying assumption of maximum information transparency. Many consumers tend not to consider all products available but will just focus on a few alternatives when making their purchase decision. 34
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Extending CBC to Cope With Above Issues In order to address above shortcomings of traditional CBC, Interrogare has developed Information-Display Choice-Based Conjoint (ID CBC). In contrast to providing the respondents with a full table of all attributes at once, all product information is hidden in ID CBC at the beginning. This avoids cognitive burden and information overload directly at the start of the conjoint exercise. Rather than providing all possible information, each participant individually decides subsequently which product features are interesting and uncovers the important attributes row-by-row. During this exercise, the respondent must behave strategically in order to make the best decision suiting their personal needs. Figure 3 shows the layout of the choice task in ID CBC as well as the information acquisition behavior (that is, a search path) of an exemplary respondent. Once, the revealed information is sufficient for a decision, a product alternative can be chosen – just like in traditional CBC choice tasks (fig. 3). In doing so, ID CBC directly incorporates information seeking behavior as a crucial part of the purchase process. While still maintaining the usual CBC results, such as attribute importances and partworth utilities, an investigation of the information search behavior reveals additional insights on how purchase decisions are made, particularly when the assumption of maximum information transparency and low cognitive load are not justified.
Fig. 4: Identification of prototypical search paths
By aligning product communications (for example in commercials or sales conversations) to the information needs of the consumers, the likelihood of a (frictionless) purchase can be increased substantially. (fig. 4) Additionally, the tracked search paths can be intelligently combined with the choice data. By this means, non-compensatory simplification heuristics, such as must-haves (such as “the product must feature this aspect”) and unacceptables (such as “the product must not feature this aspect”) can be identified with high precision. This makes ID CBC an ideal candidate to measure preferences and predict purchase behavior in complex markets, where consumers are hardly able to take into account all product alternatives available on the market but rather use simplification strategies to “muddle through” the purchase decision process. ■
Fig. 3: Choice task in ID CBC
Particularly Advantageous Sören Scholz In a nutshell, the incorporation of respondents’ information search behavior helps ID CBC to better mimic complex purchase decision processes not ideally covered by traditional CBC. Additionally, the tracked information search behavior provides relevant insights in its own right. By analyzing the applied search paths, marketers can not only understand consumers’ preferences, but are also able to understand their predispositions of product search behavior. As outlined in figure 4, several prototypical search paths can be identified. www.research-results.com
is CEO at the market research institute Interrogare. He is in charge of research methods and their implementation. His main research topics are brand management, product development and pricing. Sören has more than ten years of professional experience in marketing science and practice. He studied business economics and psychology at the University of Bielefeld. www.interrogare.de
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On the Right Track
Next Generation Usage and Attitude Surveys
Consumers’ behaviour is often difficult to predict, and usage and attitude (U&A) surveys at times come up against their limits. Gabriele Stöckl, Orhan Kocyigit and Frank Drewes present a new approach – a combination of classical U&A and permission-based online tracking.
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Photo: © ra2 studio – fotolia.com, Company
ohnson & Johnson produces one of the most popular mouthwash products in the US, called Listerine. Its antiseptic characteristics are advertised as “Kills germs that cause bad breath” – so at first glance it is a conventional mouthrinse product with a specific benefit. A second glance and some quick internet research reveal a surprisingly broad range of applications. General practitioners recommend it as a supporting remedy for a sore throat, or to be more precise for bacterial infections of the throat. Affected people discuss Listerine as a home remedy for skin blemishes and experiment with different variants and colours (no final conclusions in yet). The US consumer forum mommysavers.com lists 22 unique uses for Listerine ranging from the suppression of k itchen trash odours (“Soak a paper towel in Listerine and put it at the bottom of your trash can”) to the more adventurous treatment of lice (“Soak your hair in Listerine and cover it with a shower cap for two hours.”).
Understanding Consumers’ Needs Listerine successfully expanded into overseas markets, not only on the strength of its product, but also by understanding and accommodating varying consumer needs in different countries. European consumers, for example, “want their mouthwash to solve more com36
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plicated problems than just bad breath. The biggest problem is if you don’t understand consumer needs,” says Alison Lewis, Chief Marketing Officer for Johnson & Johnson. “If you don’t do that well, you don’t win.” (New York Times, September 12, 2014). Insights in consumer needs are typically the domain of market research. Large-scale “usage and attitude” (U&A) projects attempt to collect all consumer perceptions and assumptions within a product category, to provide a valid base for critical business decisions for at least two years. This said, however, U&A projects tend to be complex, expensive and generally highly demanding both for researchers and participating consumers. Researchers have to collect the informational demands of internal stakeholders, ranging from Research and Development (R&D) to Marketing, translate them into research objectives, operationalise them in consumers’ terms and integrate them into a cohesive research design. Frequently, researchers have to make compromises resulting in surveys which are perceived by participants either as long, thematically incoherent, and/or worded in incomprehensible terms.
Complexity as a Challenge Another challenge in conducting U&A projects is the inherent complexity of the objective. Consumer behaviour and attitudes can seem unpredictable to researchers and consumers. First-time buyers of mouthwash products in developing markets may be interested in improving their breath, caring for their oral health, disinfecting sores or putting it to a not-yet formulated 23rd unique use. Research sticking too closely to (implicit) beliefs about a product category runs the risk of missing important insights or trends. Observational research approaches can offer a more open and unbiased access to consumer behaviour. Unfortunately, they were difficult to implement outside controlled settings and on a large scale until fairly recently. That changed with the advent of online tracking data sources.
Online Tracking Data Sources Online tracking data sources can be basically differentiated by a) the depth versus breadth of the tracked online behaviour, b) its level of privacy versus publicness and c) the availability of a communication channel between researchers and tracked consumers. Social media monitoring, for example, tracks public content (deep) on social networking sites (narrow) without the (immediate) opportunity to ask posting, replying or reading consumers any questions. Providers of clickstream data like Jump Shot monitor any website accesses (broad) but typically not the content (flat), again all without the possibility to communicate with participants. Another option is permission- and cookie-based tracking of specific online content, typically advertising, in an online access panel. www.research-results.com
This technique is offered by a number of market research agencies and it provides the opportunity to enrich the tracking data with surveys and background information about the participants. This kind of online tracking is naturally narrow and flat in our terminology, but highly efficient in testing online ad effectiveness under certain conditions. A comparatively recently developed fourth option is a permission-based, clickstream-like tracking of website accesses in online access panels. This fourth approach provides insights into the online behaviour (which websites are retrieved when and how long in which succession?) which can be matched and merged with panel core data and survey results on an individual level. In other words, it provides an unprecedented opportunity to combine large-scale observations of actual online consumer behaviour with more conventional U&A survey approaches.
Extended Approach: U&A 2.0 The inclusion of permission-based online tracking in classical U&A approaches offers a number of research opportunities which can lift the potential insights to a new level. The tracking supports both PC and mobile devices and records full URL for every online activity. Depending on the organisation of a website, full URL can contain information about searches on the website, website subcategories and topics, visited product sites in online shops and much more. This data source in connection with a well-developed list of relevant terms and topics and a powerful string-parsing tool allows unbiased insights into the way consumers approach a product category online. Potential objectives are the identification and description of: • v isited manufacturer, brand and product websites within the interesting category • category-relevant Google searches and visited research results • online purchases of products in the interesting category • visited consumer forums and discussion threads As already mentioned, these tracking objectives can be qualified by and/or augmented with any survey-based information, for example online behaviour by brand preference, product usage, attitudes towards category and so on.
Genuine Innovation The combination of digital tracking with consumption diaries provides another kind of insight by identifying online activities before, in and after consumption occasions. Potential objectives of this approach are: • description of consumption occasions in terms of online behaviour • identification and targeting of websites, online content and/or behaviour which are associated with the consumption esearch &
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Again, consumption-related online activities can be analysed in connection with any other survey-based information. Summarising the 2.0 promise in the U&A 2.0 proposition, market research does not have to rely solely on stated usage behaviour and attitudes any more. Permission-based online tracking allows large-scale observation of consumer behaviour in a natural environment that can be combined with any other data collection method including diaries and conventional surveys. Permission-based online tracking addresses completely new objectives which elevates it from a mere improvement to a genuine innovation.
A Pilot Project Harris Interactive conducted a U&A 2.0 project on behalf of one of the globally leading FMCG manufacturers in one of the Big Five European markets in 2018. The relevant category is characterised by high consumption and purchase frequencies (several per day and week respectively). The core of the project was a mobile-based consumption diary survey that lasted seven days. Approximately 1,000 participants recorded 50,000 consumption situations in total. Each consumed product and the situation in which it was consumed was exhaustively described. Additionally, half of the respondents also participated in the permission-based tracking of online activities. Consumption and online tracking were accompanied by more conventional U&A surveys.
First Findings A first general insight was that a large proportion of online activities seemed to be independent from the consumption within the relevant category. Popular online services like Google, Amazon, Facebook and WhatsApp on mobile phones are powerful attractors and were retrieved more or less at any time and in any situation. Regarding spontaneous category-related online activities, “health” – compared to “environment” – was the more important one of the assumed trend topics. Both topics were outshone by more mundane activities like online purchases, research of recipes and categoryrelated kitchen appliances. Browsing category-related web content in consumption situations was the exception. The most challenging part in the project design was the synchronisation between automatically recorded time stamps for online activities and self-reported consumption times. The validity of the latter exceeded by far the expectations as exemplified by a certain online service (fig. 1) with a very narrow, category-related field of application: usage of this service peaked ten minutes before a consumption incidence. Another proof was provided by findings like a peak in social networking one hour before socially characterised consumption situations. Both examples highlight the potential of the online tracking in the identification of important online touchpoints.
Online Service X: Traffic Share Depending on Consumption Occasion
Traffic Share Index (Overall Share = 100)
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- 244 + 34 - 71
+ 100
Overall
60 to 11 min. before consumption
Up to 10 min. before consumption
Up to 10 min. after consumption
11 to 60 min. after consumption
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Source: Harris Interactive
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A Powerful Tool Understanding consumer needs remains important for FMCG manufacturers both in developed and emerging markets. Conventional U&A surveys seem to encounter their limit not only in terms of feasibility but also in terms of the insights they can generate. A multitude of internal stakeholders on the one hand and complex, to some extent unpredictable, consumer behaviour on the other hand, require open and unbiased research tools. Permission-based online tracking provides a truly innovative tool that collects observational data on a large time and sample scale. It provides insights into characteristics of consumption situations and category-related online consumer activities ranging from research to actual purchases – so if there is a 23rd unique use for Listerine out there, permission-based tracking will find it. ■
Can’t predict DŽ Ċ ğƃžƃűĊǧ
TRY TOLUNA INSIGHTS AND
KNOW W H AT ’ S NEXT Gabriele Stöckl has been working in market research for more than 15 years and, as Sector Head, has been responsible for consumer goods research at Harris Interactive AG since 2009.
Dr. Orhan Kocyigit is Sector Head Healthcare & Health Technology at Harris Interactive. He holds a PhD in Economic Sciences and has more than 10 years of experience in market research and strategic marketing which he gained from client as well as from institute side.
Frank Drewes is Director Marketing Science at Harris Interactive, which he joined in 2008. He studied Psychology with a focus on organizational psychology and psychological diagnostic and is responsible for the development and implementation of innovative market research tools. www.harris-interactive.de
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Almost Fictitious?
Concrete Steps Panel Companies Must Take to Improve Quality
What can companies learn from the controversial discussion about quality in market research? JD Deitch knows.
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ccording to a recent article in the news imagazine “Der Spiegel”, bthe market research industry has “massive problems with the quality of its data” stemming from multiple shortfalls, including manipulated surveys. The publication did not report this controversial viewpoint without foundation: In fact, they employed a team to evaluate about 600 studies over the last 10 years. What they found was that even reputable market research companies are affected by manipulated data.
Complex and Insurmountable
Photo: © rare – fotolia.com
Are these poor and unreliable outcomes something we should just resign ourselves to in the market research industry? The problems are complex and often seem insurmountable. From bad questionnaire design to fraudulent respondents, each discovery of poor techniques and methods creates demand for stricter controls and standards for the indus-
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try. Yet there will never be a simple solution. There are too many competing commercial interests and, to be fair, our practices are varied and rapidly evolving. It then falls on the shoulders of buyers to demand quality practices and suppliers to fulfil them.
The Quality of Questionnaires One area that the industry has focuses on is the questionnaire itself. For decades we have studied how questions are presented and how respondent answers may be influenced. Whether it is length, question type, mobile-first design or topic, there is always room for improvement on this front.
Panels: Quality or Speed? One aspect the industry has not focused on nearly enough is the panel. Panel companies share culpability for quality issues – this is indisputable. Our responsibility goes far beyond the traditional belief that sample companies simply “provide the people.” The industry accepts this as one of its critical problems, as indicated in Greenbook’s bi-annual GRIT report. With increasing demands for speed, sample companies are under pressure to deliver samples quickly and at lower price points. These problems are so pressing that multiple sample quality initiatives are underway from industry leaders like ESOMAR, GRBN, Samplecon. Sampling is a fundamental pillar of what we do as market researchers, and yet many lack confidence in panel and sample companies to uphold their end of the bargain and provide the foundation for quality insights.
Automation actually has the potential to take us far beyond simple time savings and solve many other common problems, when properly implemented.
Outdated Practices Despite past bleak attempts to create real change, I believe that all hope is not lost. The fact that our practices are evolving does not change the fundamental principles of quality. But it does change how we define and operate practices to ensure quality. The trouble now is that the industry is labouring under beliefs and practices that are outdated.
Automate Everything Central to a new and durable set of practices is embracing technology. The technology available today gives us the ability to quickly and dynamically source sample and the flexibility to support new methods and techniques without compromising quality. By using automation especially, sample companies not only breed efficiency and lower costs, but also fundamentally improve data accuracy and overall agility. Here are just a few of the things that both sample suppliers and buyers must start to consider in order to battle “fictitious” data.
Beyond Time Saving Automation actually has the potential to take us far beyond simple time savings and solve many other common problems, when properly implemented. It has the power to vastly improve reliability, efficiency, global reach and feasibility, and dependability. Though currently underutilized in the industry, automation can help with things like: • I nstant information exchange: Automation is essential for exchanging information between two systems or platforms. Sample companies should be well versed in the use of Application Programming Interfaces (APIs) that enable them to pass project specifications, manage quotas and demographics and transmit targeting data between supply and demand partners. www.research-results.com
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• F easibility: Automation allows us to avoid spreadsheet-driven, manually produced feasibility calculations using simplistic formulas. Automated feasibility processes take into account a huge number of factors – from field parameters to individual behaviours – to provide highly accurate estimates of how a project will fill. This means greater speed and dependability. • F ield Monitoring/Quota Management: Automation allows us to keep a silent and constant vigil on each and every project, instantly detecting anomalies in order to ensure projects remain on track. Quotas can be closed automatically ensuring respondents are not turned away in frustration. Automated field monitoring ensures problems are spotted and corrected immediately before they snowball, allowing for massively improved operational efficiencies. • R ecruitment: When fully automated, sample suppliers can trace panellists all the way back to the source to optimize sourcing based on quality scores. This kind of approach intrinsically ensures that they are recruiting real, attentive, engaged, deeply profiled people who genuinely complete studies. Moreover, automation enables on-the-fly recruitment based on need. Automating the entire process allows the management of more recruitment partners, which means greater reach and diversity, and less “groupthink”. When automation is properly implemented in the recruitment process, it improves everything else downstream, from feasibility estimates to response times to economics for suppliers and buyers. • P rofiling: When panel is properly managed, it is possible to develop deep and rich profiles drawing from literally hundreds of demographics and behaviours. Automation at this stage can use this data to enable precise sample targeting, ensuring that the right respondents are matched with the right study without the annoying requirement (for the respondent) of having to answer the same questions over and over again!
Create Good Experiences, and Stop Tolerating Bad Ones Improving respondent experiences is another way in which automation directly impacts quality. The industry knows that bad respondent experiences yield lower response rates and bad data, yet too many suppliers only pay lip service to this important issue. This goes beyond “bad questionnaires” and includes recruitment and abusive fieldwork processes as well. The technology now exists to improve experiences and stop mistreating respondents. It is more than just saying nice things to respondents; it is about taking concrete steps to act in their interest and protect them from bad experiences.
Right Studies and Right Persons Automation lets us take full advantage of the far-reaching profiling data available for each respondent so that the right study can be matched instantly to the right person. This not only maximizes the likelihood of a respondent completing a survey (which is good for everyone), but also minimizes the regularly-occurring problems that respondents experience, such as being bounced around from router to router, constantly having to answer the same questions about gender, age and occupation, and having to deal with avoidable overquotas and terminations.
Greater Flow of Sample But it doesn’t stop with simple match-making. Automation can also monitor studies and respondents in the field, checking to make sure that the respondent is engaged and that the study is functioning properly by evaluating fieldwork metrics as well as respondent feed42
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back. Good studies such as those that have low dropout rates and high respondent ratings should receive a greater flow of sample. Bad studies such as those with opposite characteristics, should have their flow reduced. Giving respondents the ability to push bad surveys out of field protects respondents and also alerts clients about what will surely be imminent data problems. Platforms that employ “self healing” algorithms of this nature yield better data and preserve the long-term health of the business and the industry. Taking a thoughtful automated approach to respondent engagement can lead to significantly higher satisfaction and engagement. That means better data.
Battle Fraud from Multiple Angles It’s time to stop ignoring and downplaying fraud’s impact on our industry. Estimates of fraudulent survey response ratios range from 5 to 20 percent and more. Worse, the industry is under the impression that fraudsters are simple hackers using “bots” to complete surveys, when in fact the majority of fraud is human led. As fraudsters become more savvy, we must start to combat them with a multi-pronged approach. Traditional static detection techniques, such as Captcha, honeypots, address and e-mail validations, trap questions and open-ended questions are individually and collectively insufficient. While each can be useful, they must be coupled with more advanced and dynamic solutions.
AI and ML No technique is more promising than the use artificial intelligence (AI) and machine learning (ML). With the data points panel companies collect (which number in the billions), including demographics, behavioural indicators and field experience, there is ample information to feed sophisticated AI models to detect and combat fraud. AI techniques can analyze anomalies and patterns in the data, such as user “surges” involving specific demographics, IP ranges or completion time frames, meeting new threats as they arise to significantly mitigate fraud. www.research-results.com
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Providing Concrete Solutions If you delve into the industry, as “Der Spiegel” did, you can quickly start to see the depth and breadth of problems as they relate to data quality. Heightened awareness of these issues brings with it demands for stricter quality controls and even the implementation of more diligent techniques to reduce problems, but, as the article states, “nothing has gotten better.” It is up to all of us to pay better attention to the areas in which we can start to make an impact, and step by step work together to improve insights and outcomes. This starts by being fully aware of all the issues and accepting responsibility for our individual contributions. Essential to solving these issues is going beyond words and adopting technology and practices that provide concrete solutions. ■
JD Deitch is a twenty-year veteran of the market research industry who currently serves as CRO of P2Sample. He began his career client-side in financial services, then went on to hold senior global positions at The NPD Group and Ipsos. Prior to P2Sample, he held executive positions at AYTM and Bakamo.Social. JD is a frequent speaker and a thought leader in the insights industry as it evolves to tackle the challenges of the digital age. He has a Ph.D. in Political Science with Distinction from The American University and a BA from the University of Pennsylvania. He lives in France with his wife and two sons. www.P2Sample.com Advertisement
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Is the Future of Qualitative Research Digital?
Mind the Tech A market research world without online communities and all the new technologies is unthinkable. Still Edward Appleton pleads to not forget first-hand experiences – for quality’s sake. he reality of qualitative research is changing apace – the days where it was mainly about focus groups and in-depth interviews are long gone. • A typical qualitative project in 2018 is likely to be multi-modal, involving any number of approaches – social media analysis, workshops, co-creation sessions, mobile ethnographies, semiotic decoding and cultural interpretation for example. • Organisational structures have also moved on: Agencies with qualitative legacies are increasingly beefing up their analytics departments to offer seamless qual-quant solutions; having an in-house IT function is very common. Analytics is something that qualitative researchers are increasingly seeing as a potential opportunity. • Conceptual thinking has moved to embrace contemporary theories of human behaviour and motivation such as Behavioural Economics. Triangulation is increasingly the norm. Qualitative research is now also comfortable with online methods. Take a look at the Top 10 emerging methods documented by the 2017 Q3/Q4 GRIT report – 4 of them are qualitative, powered by digital (fig. 1). 44
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Easy Digitalisation The advantages of online in qualitative research are proven – corresponding critically to pressures from clients for quicker turnarounds, more actionability, more iterations, lower costs. Just take one aspect – the ability to iterate smoothly. Historically that was challenging in quality – recruitment was ad hoc, projects bumped along in a stopstart mode, with iterations more onerous. With digital it’s much easier – online communities for example that run over the period of weeks can encompass multiple phases, tasks, with the same people involved; concept building is massively easier, and much easier to link smoothly to the insights part. The central role of Online Communities (MROCs) in transforming what qual can do and its standing amongst the client side community is perhaps underplayed. They have been around for a while, as the overview of the development of qualitative research below shows (fig. 2). But as the 2017 GRIT report reveals, MROCs are very widely adopted. To call them an emerging method isn’t accurate any more – MROCs are part of the new normal. www.research-results.com
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MROCs – a Review Time to take a closer look at what they can do, how they have evolved and why they are so popular. Happy Thinking People first started working with Online Communities in 2007 – we were amongst the first in Germany to work with them. Since then we have executed over 400 communities across 40 countries and over 100,000 days spent with different consumers! A look back at the recent past is useful. There are three main developmental phases:
Phase 1: 2007 – 2009 “Big Bang” best describes the excitement of the breakthrough of being able to access real-life glimpses into people’s lives across continents, from the comfort of one’s own desk. Participants sharing pictures, videos, all sorts of minutiae, plus the ability for both researchers and clients to observe the fieldwork as it unfolds. Ethnographic options opened up dramatically. One project we conducted in Turkey exploring female body shaving worked very well via our online community; trying to do that face-to-face would have caused massive challenges.
Phase 2: 2009 – 2013 “Globally Playful.” Functionalities evolved, becoming more interactive and playful, using collages, drag and drop, mappings, quick polls….MROCs became exploratory and evaluative. Closed-ended questions could now be thrown into the mix too. Participants loved it, they spent more time online and uploaded more content. Great for clients as well - watching online, participating via the moderator, sometimes adding tasks as the community progressed, building iteratively and efficiently on findings from the day before.
Phase 3: 2014 – the present “Mobile Rocks MROCs”. As smartphone screen sizes increased, and photo/video functions improved significantly, MROCs embraced mobile. Out-of-home moments became more accessible, for example, more vividly captured. Question and answer formats can easily be replaced by videos with a task, making them more playful; clients can easily get directly involved.
Fig. 1: Top 10 Emerging Methods in Use
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Fig. 2: 4 Phases of Qual Development
Bringing Clients Closer to the Consumer Which brings us to the present: MROCs are modern mainstream, proving their versatility and usefulness for all involved, and are no doubt playing a key role in more and more project designs being digital-only. One example: We recently conducted a three country innovation project across two continents amongst foodies that worked well on our community platform. Foodies were asked to share what inspired them most in their city, uploading comments on others’ posts, pictures, meals, then come up with their own meal ideas, which were structured into positioning platforms. Evaluative gallery walks worked well online, cross-country insights were generated. English worked well for all audiences across geography. In summary: MROCs represent a very contemporary form of research, often imitating social media habits, with lots of sharing, enhancing, show-casing. They critically bring clients closer to their consumers with relatively low effort.
Online Has Its Limits Which leads us to the overwhelming question: Do we even “need” face-to-face at all in qualitative research? Predictions of the “death of the Focus Group” are familiar and exaggerated. But are we arriving at a point where we should question the inexorable rise of digital – is it really making qualitative research more effective? Are we in danger of seeing digital qual as a default option? • Firstly, online isn’t always faster. We recently conducted a splitsample study on the drinks category – identical objectives, audience, knowledge needs, but with one cell using Focus Groups, the other an Online Community. The outputs were similar – same depth of understanding, granularity. The one difference: The results from the Focus Groups were available a whole week quicker than the online cell. The reason for this – a moderator can only properly start the analysis in a community once it’s completed, and that can be on the very last day, depending on participant “compliance”. With groups, it’s much faster – top-lines in particular can be generated within the space of a day, if not the same evening. esearch &
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• S econdly, ethnographies and immersions are best done in person, in situ. There is no substitute for walking alongside someone, or for clients experiencing the reality of their customers’ lives. Often the most interesting things are said when an interview is officially over, the recorder switched off – then people open up. Brands need to avoid losing the emotional connection and intimacy with their audiences. • Thirdly, there are global cultural differences that need respecting. Attempting an online community in India for example is challenging where being interrupted is normal. People in the sub-continent don’t have a tradition of sitting down quietly on their own with a computer. In China the challenge is different – ensuring the researcher really has access to an individual’s opinion. What often happens is a “group effect” whereby participants read others’ entries and imitate what they consider the “best” response. This isn’t plagiarism – but it does limit breadth of response. • Fourthly, there are less developed areas of the world where MROCs are limited due to connectivity issues, power breakdowns for example. And within countries, it’s always important to pilot to check how suitable a topic is for a given audience, and whether they really use digital with total comfort. This is less an issue of demographics – it’s becoming common for MROCs to embrace audiences from 16 to 70 years of age – more of the suitability of the topic in question.
First-Hand Experience To conclude, and returning to the initial question: Will the future of qualitative research be solely, wholly digital? Caution is advised. However sexy, tech advances shouldn’t blind us to the power of small data, however gathered. Context, emotion, culture are arguably at their most powerful when experienced first-hand. Digital ethnographies are affordable proxies, but shouldn’t be confused with real ethnographies. We should also be mindful of tech over-claims (remember the last IT project that came in on time and on budget? No, me neither) and the role of financial backers and IPOs, who have other interests in mind than validity, replicability, or a great research outcome.
Decreasing Productivity The rise of IT over the last 15 years hasn’t actually coincided with a concomitant increase in global productivity. Rather the opposite.It’s certainly premature to estimate the impact of AI and “scalable qual”, engaging with thousands of people globally rather than say 30, instantly coding and sorting media-enriched responses, often video. But is this about the power of numbers – or rather the re-assurance of larger sample sizes? We remain in a world that is data-rich but arguably still insights poor. If digital can help empower qualitative researchers on their quest to be genuine consumer-centric consultants, well and good. But we need to be mindful. MR quality is fragile, an easy victim in the hands of people (often techies) with little or no social science training. There is no such thing as an instant insight, to be accessed on tap.
Pro Confidence Qualitative researchers are increasingly confident about being tech-users, embracing the opportunities digital brings, fusing legacy best practices with new digital tools as they emerge, testing and proving methodically. MROCs have certainly proven their worth in that respect. So here’s raising our glasses and smartphones to MROCs – and whatever next the wonderful world of tech has to offer. ■ 46
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Edward Appleton is Director Global Marketing & Sales with Happy Thinking People. Edward has worked for over 20 years in market research on both Agency and client side. Prior to his current role, Edward was Senior Insights Manager with CocaCola in Berlin; before that he was European Insights Manager at Avery Dennison. His career with Mass Observation UK, which he left to join the Insights team at Nestle UK. He blogs regularly at www.researchandreflect.blogspot.de and for Esomar. www.happythinkingpeople.com
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Study “Challenges of Nations” 2018
A Matter of Concern What are the most challenging issues currently facing countries? Ronald Frank describes the findings of this year’s survey and outlines the challenges that respondents believe their country is facing and the issues that concern them.
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n the context of its “Challenges of Nations” study, the GfK Verein has been conducting an annual survey of citizens from a current total of 19 countries on the major challenges facing their country since 2001. The responses to the 2018 survey once again present empirical evidence of how the significance and impact of individual issues in public – and published – perceptions change over time, particularly in times of increasingly widespread “post-factual” opinions.
cerned. Most frequently, a strong devaluation of the local currency leading to import-related inf lation contributed significantly to driving prices upward. According to the OECD, in 2017 prices rose particularly rapidly in Turkey, Mexico and South Africa, although the local populations there seem less concerned. France and Poland are also above the European average, and here, purchasing power is the key issue.
International worries
Corruption
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Unemployment At 23 percent, concerns regarding the issue of unemployment remain stable in 2018. It is still the number one global concern. Spain is the only country where over half of respondents have expressed concern about this issue. Italy, France, India and South Africa also reflect above-average levels of anxiety here. In general, the level of concern is in line with the actual rate of unemployment in the individual countries. For instance, in South Africa and Spain, the levels of unemployment and of anxiety concerning the issue are both high. Conversely, the official rate of unemployment in India is relatively low, but the population is very worried. Overall, concern about unemployment is the top-ranked challenge in four countries and takes second place in a further three countries.
Cost of Living Somewhat fewer people than in the prior year (one in five respondents, to be precise) were skeptical about prices and purchasing power. This was above all evident in developing and emergent economies. In Kenya, Indonesia, Iran and Russia, this issue is ranked in first place, with respondents in India and Brazil equally conwww.research-results.com
Concerns about corruption, at 14 percent (2017: 16 percent), take third place. Here, too, a clear connection between the level of concern and the actual situation in the individual countries is evident. Only a few European countries – Russia, Turkey and Italy – are relatively unconcerned about corruption. In these countries, despite the significant level of corruption identified by the Transparency International Corruption Perceptions Index (CPI), the level of concern is only moderate. Conversely, in Spain, where the CPI only ascribes a moderate level of corruption, citizens are considerably more worried than in other nations, particularly in Europe. Only Kenyans are even more concerned and in a CPI comparison of countries surveyed, the country comes off worst. In a global comparison, a similar proportion of respondents would like to see criminality counteracted more effectively. In 2018, this was cited as a concern by 13 percent, while the equivalent figure for 2017 was 14 percent. The issue is a major concern for Mexicans (57 percent) and South Africans (43 percent), where the issue ranks first in the concerns league table. At an average of nine percent and seven percent respectively, Asia and Europe seem to be suffering less from criminality. The exceptions are Indonesia and Italy, where the levels of concern in both cases are above the average for their continent. esearch &
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Nations at a glance – Challenges 2018: Top 10 – summary of all countries*: 2018
23
23
Unemployment
Fig. 1
20
22 14
Price/ purchasing power development
16
Corruption
13
14
Crime
11
9
Health service
10
Politics/ government
7
8
Poverty
7
9
Education policy
5
5
Immigration/ integration
5
9
Economic stability
Source: GfK Verein; Base 2018: 23,030 resp. *country base identical, i.e. Belgium, Austria, Netherlands, Switzerland, Nigeria 2017 excluded; in percent
Healthcare Healthcare comes in fifth place on the list of concerns, having risen by two percentage points to a current level of eleven percent. Here, Brazil (60 percent) is by far and away the most concerned country of those surveyed. Moreover, healthcare policy remains at the top of the league table of worries in the UK (37 percent) and Poland (22 percent). More than a quarter of Kenyans are also concerned about the issue, although the east African country only occupies seventh place on the list.
Politics With a decline to eight percent, the combined issues of politics and government are in sixth place on the international agenda. The populations of Brazil and Kenya are most concerned by politics. The reasons for concern vary internationally from a lack of credibility due to corruption, to criticism levied at the country’s respective political direction and failing governments. The latter was lamented in early spring 2018 by Germans (16 percent). One fifth of US respondents expressed concern on this issue, with a further eight percent skeptical about the current White House incumbent, President Trump.
Poverty and Education Ranked seventh and eighth respectively are the subjects of poverty and education policy, although both issues are displaying a declining trend. Astonishingly, in Germany, one of the wealthiest of the nations surveyed, respondents revealed the highest level of concern with regard to poverty (20 percent). While the issue is ranked third here overall, in all other European countries it does not even make the top ten. In both Mexico and Indonesia, twelve percent expressed uncertainty in each case, while in India, Iran and Kenya, one in ten respondents indicated concern. In a European comparison, Germany again expresses greatest concern with regard to education policy (18 percent), putting this issue in fourth place for German respondents. Only the Brazilians see greater room for improvement in their education system. The highest level of concern (39 percent) was identified in the South American country in this year’s survey. Although one fifth of Kenyan respondents expressed skepticism, the issue of education still did not rank among the top ten challenges here.
Migration and Integration At five percent each, the subjects of migration and integration and the economy are ranked ninth and tenth on the list of global concerns. The issue of migration and integration are particular concerns for Europeans (average of 15 percent) and respondents in 48
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the USA (19 percent). Almost half of Germans, nearly one third of Swedes and one in four Italians worry about these issues. In Asia and Africa, the issue is currently irrelevant.
Economics Overall, the economic situation is regarded less critically. However, it is a greater concern in one Asian country. At 41 percent, South Korean respondents put this issue at the top of their list of concerns. In Iran, where one quarter of the population are concerned about the economy, the issue is in third place.
Global Key Challenges The two key challenges at global level, unemployment and inflation, are both ranked at the top of the list of concerns by four nations. For three countries, healthcare is the most pressing problem and in two countries, the issues of immigration and criminality are top of the list. Alternatively, other states are challenged by different, more specific concerns. For example, Turkey, where terrorism is the major headache for the population and Japan, where – due to demographic developments – family policies and provision for the elderly traditionally play major roles. In South Korea, economic development is the primary concern, whereas for the population of the USA, the main focus of anxiety is criticism of policy development and the government.
Challenges in Germany For Germany, 2017 proved to be a critical year: prior to the parliamentary election in fall 2017, a series of election campaign issues dominated the landscape, which varied according to political shade of opinion and which are currently again reflected in the issues worrying Germans.
Politics and Government After the election, the unusual difficulty in forming a government was at the forefront of concerns. With this in mind, 16 percent of citizens had concerns on the combined issues of politics and government. An increase of seven percentage points puts this issue in fifth place. In addition to the election, the extreme length of time taken to form a government contributed to the level of concern. A total of eight percent of respondents clearly expressed their wish for the formation of an effective government or the conclusion of coalition negotiations. However, in this case, a difference in survey timings should be made, because in the interviews conducted in February www.research-results.com
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2018, 14 percent of respondents expressed skepticism, whereas in March, the figure had dropped to just two percent. The new cabinet was finally presented on March 8, 2018, six months after the election.
Immigration and Integration The issue of immigration was not just present in the run-up to the election. Opinions are clearly polarized on this issue in general: Around 17 percent of Germans expressed negative opinions on immigration, with nine percent in favour of more integration and immigration. A good half of citizens indicated concern about immigration and integration. With this, concerns related to this issue have now declined twice in a row, albeit only slightly. In politics, the debate centered on the upper limit of immigration. The compromise in the new coalition agreement provides for an upper limit on migrants of 220,000 per annum. After 900,000 migrants still arrived in Germany in 2015, in 2017 only around 200,000 initial applications for asylum were received. The decline in numbers is mainly due to the agreement reached between Turkey and the EU and the closure of the Balkan route. While this has put a stop to illegal immigrants, legal immigration to Europe will be supported. In fact, in 2016, around half of all migrants arriving in Germany were legal, that is to say that they arrived from other EU countries.
Pensions and Provision In second place on the agenda of concerns is pensions and provision for the elderly, an issue which has risen to 20 percent in 2018. Due to demographic changes, there are increasing numbers of pensioners while there are lower numbers of those in work paying into the system. The coalition agreement provides for the introduction of a statutory pension insurance for self-employed persons. However, there was positive news for pensioners in the short term, with an increase in pensions of 3.2 percent in the west of Germany and 3.4 percent in the east. Pensions in the east and the west parts of Germany should then eventually be on an even keel by 2024.
Poverty Closely associated with the subject of pensions are concerns about poverty, which have also doubled to a record value of 20 percent since 2016. Around half of the respondents expressly indicated pensioner poverty as a problem. In January 2018 an announcement by the board of the Essener Tafel (an organization that runs foodbanks) to the effect that they would no longer accept new clients with a foreign passport, because this was causing existing clients to stay away, caused a furore. The uproar triggered a public debate on poverty and social justice.
Healthcare In this regard, nine percent of Germans, of whom six percent for the first time, expressly indicated concern about the lack of nursing care in connection with concerns about elderly care. A further 14 percent is concerned about the healthcare system in general. An increase of nine percentage points suggests that this concern again definitely on the rise. To counteract the problem, an immediate program is to be launched to create 8,000 new jobs in care homes, with pay rises for geriatric care staff and nurses. The plan includes contingencies to improve both healthcare in rural areas and the benefits for members of the national health insurance system.
Education Education policy is also regarded with growing skepticism by Germans. The concern factor has risen for the second time in a row to stand at 18 percent, which means that it is ranked fourth in the league table. However, according to the OECD, in an international comparison, Germany is top for MINT subjects (mathematics, information technology, natural sciences and technology), something associated with correspondingly positive career prospects in terms of vocational training and university level education. Yet the 2017 education monitor reveals that across Germany as a whole, the education system has ultimately improved only marginally. Most criticism is levelled at the increasing numbers of foreigners dropping out and the overall reading skills of pupils. ■
The “Challenges of Nations” Study These findings are taken from the “Challenges of Nations 2018” study and based on around 23,000 interviews conducted on behalf of the GfK Verein in spring 2018 across 19 countries: Brazil, Germany, France, the UK, India, Indonesia, Iran, Italy, Japan, Kenya, Mexico, Poland, Russia, Sweden, Spain, South Africa, South Korea, Turkey, and the USA. The survey is based on the following openended question which is asked every year: “What do you believe are the most pressing challenges that need to be solved today in [respective country]?” There are no restrictions on the responses given by those surveyed, and several issues can be mentioned.
Unemployment In contrast to the concern about poverty, the level of worry associated with unemployment fell to twelve percent. This is in line with the rate of unemployment: According to the OECD, unemployment was running at 3.8 percent in 2017, whereby during the course of 2017, it declined still further to stand at a record low of 3.5 percent in February 2018. Experts are anticipating a contrary effect on the job market for 2018 as a whole, with higher numbers of migrants seeking employment included in the statistics, causing the rate of unemployment to rise back up. Moreover, a lack of skilled labor is becoming evident in several industries such as the manual trades and healthcare, with particular reference to nursing. www.research-results.com
Ronald Frank heads the field of studies at GfK Verein. He previously worked for many years in media research at GfK SE. He conducts research that unveils fundamental structures and developments for private consumption in society and economy. Most of his studies are available free of charge. www.gfk-verein.org
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10 Questions – 10 Answers
“Somebody needs to tell the story” Pierre Pigeon, CEO, AplusA/Ifop Group Which cliché about market researchers annoys you the most? “That market researchers often provide data and no real insights.” And which one is accurate in your case? “That market researchers are very detailed-oriented.” What would you have liked to have become – apart from market researcher? “As I am a trained endocrinologist, I would most likely have been a physician.” And what did you definitely not want to become? “A singer, due to a lack of talent (ask my teacher).” Do you still enjoy being a market researcher? And why? “Absolutely. It is an exciting area and there are a lot of interesting developments (big data, social media, AI etc.) and especially in the healthcare industry. It is still challenging and is never boring.” Would you advise a young person to become a market researcher today? “Definitely. As I previously mentioned, it is an exciting field where you are continuously learning more every day. Furthermore, I am sure there will soon be a high demand for people who can analyse big data and who are able to draw insightful and meaningful conclusions. Data without any conclusions have no meaning.” What product have you recently bought only because of the advertising? “Alexa.” And was it good? “Some aspects are good, but there is need for improvement. Alexa’s answers are sometimes too superficial.” When did you last cancel your participation in a survey? And why? “A survey that was supposed to only last five minutes and after ten minutes, it was nowhere near ending.”
Pierre Pigeon trained as a medical doctor in 1982, then specialized in endocrinology in 1986. He then joined Aventis-Pasteur where he held a position of Global Marketing Manager for Child Vaccines. He founded AplusA Healthcare Market Research in 1990. AplusA is one of leading independent market research companies dedicated to healthcare. AplusA acquired Ifop in 2016. Most recently, the AplusA/Ifop Group acquired Sociovision. www.aplusaresearch.com www.ifop.com
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Do you think market researchers will be redundant in the near future? And why? “Oh no, somebody needs to tell the story behind the data.”
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Authors: Edward Appleton, Zsolt Apponyi, Samantha Bond, Pete Cape, Csaba Csürke, JD Deitch, Danyo Dimitrov, Frank Drewes, Ronald Frank, Julia Görnandt, Dezső Karasszon, Dr. Orhan Kocyigit, Sören Scholz, Gabriele Stöckl, Bence Tóth, Judith Wieghardt Art Direction & Graphic Design: Nicole Nowak
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