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POSTMASTER:
INTERVIEW
Transforming the Customer Experience: An Interview with Jon Picoult, Founder and Principal, Watermark Consulting
Jon Picoult is a renowned expert on customer experience and the author of “From Impressed to Obsessed: 12 Principles for Turning Customers and Employees into Lifelong Fans”.
U.S. Direct Mail Is Up: Is Your Volume Growing, Too?
talkingpoints
Introhive, a relationship intelligence platform, has been selected by KPMG firms to help transform and enhance the company’s front office and enable clientfacing professionals to become the most trusted provider supporting the c-suite agenda.
In collaboration with Introhive and Salesforce, KPMG firms have accelerated their digitalfirst strategy to drive revenue growth in a changing marketplace where client demands are shifting. With a focus on increasing crossfunctional data visibility across teams, KPMG firms are laying the foundation for more meaningful prospect and client interactions, enhanced collaboration and cross-selling, and a better client experience for external clients and internal practitioners.
KPMG firms are investing heavily in their clients by focusing on new capabilities to adapt to the changing market and retain a competitive advantage. Technology advancements made to KPMG firms’ front office leverage Introhive’s data-driven client insights to gain a deeper understanding of their organizational relationship capital and maximize the value of those relationships, fostering exceptional client experiences and helping to increase sales. By taking a united approach to digital transformation, KPMG firms are poised to accelerate both retention and revenue growth.
Introhive’s AI-powered platform automates data analysis and uses relationship intelligence to help KPMG firms win more engagements and improve productivity. The platform also provides a birds-eye view of KPMG firms’ relationships to provide KPMG professionals with the necessary information to assist KPMG firms in their business development efforts.
“Our journey with KPMG goes back several years, beginning with a concept for streamlined data quality management which is now becoming a reality. We are thrilled that we can support KPMG firms as they continue to grow and modernize how they use technology in a world where AI and Machine
Intelligence are becoming more prevalent.” – Mike Waugh, Vice President of Product, Introhive.
For more than a decade, Introhive has revolutionized how organizations identify valuable relationships by providing executive, business development, and marketing teams with a firm-wide view of active client relationships. This ultimately helps unlock the full potential of their collective network and maximize profitability. For more information, visit www.introhive.com.
Introhive is the leading Relationship Intelligence Platform that empowers professional services firms with trusted data, relationship insights, and actionable intelligence. Our solution enables businesses to identify selling opportunities, win new clients, and grow existing accounts. Trusted by the top firms worldwide, Introhive supports over 250,000 users in 90+ countries. With offices in the US, Canada, and the UK, we’re committed to helping businesses optimize their revenue opportunities. KPMG is a global organization of independent professional services firms providing Audit, Tax and Advisory services. KPMG is the brand under which the member firms of KPMG International Limited (“KPMG International”) operate and provide professional services. “KPMG” is used to refer to individual member firms within the KPMG organization or to one or more member firms collectively.
A new resource from Info-Tech Research Group, the Toronto-based global research and advisory firm, is addressing common challenges in data management and offers strategies to align analytics with business goals.
The comprehensive research provides organizations with actionable frameworks and insights to develop robust reporting and analytics practices, driving better decisionmaking and business outcomes.
As organizations strive to make data-driven decisions to stay competitive and responsive in a fast-paced market, they often face challenges in effectively leveraging reporting and analytics. In a new research-backed resource, Info-Tech Research Group explains that the sheer volume of data and the need for accurate insights can overwhelm even the most advanced IT teams, leading to inefficient processes and missed opportunities. To address these challenges, Info-Tech Research Group has released its latest blueprint, Build a Reporting and Analytical Insights Strategy. This comprehensive resource offers actionable research insights and analyst guidance to help IT leaders and their teams build effective reporting and analytics practices that align with business goals and
drive improved outcomes.
“Self-service capabilities are recognized as one of the top features that contribute to increased usage and adoption of business intelligence (BI) and analytics solutions,” says Ryui Sun, research analyst at Info-Tech Research Group. “Self-service BI tools are becoming prevalent because businesses want more control over their data, signaling a move toward a more federated operating model for the BI team, as well as a focus on customers and service delivery.”
Info-Tech’s newly published blueprint emphasizes the importance of aligning reporting and analytical insights strategies with organizational goals. According to the firm’s research, many organizations struggle with data silos, inconsistent data quality, and the lack of a cohesive strategy, leading to inaccurate insights and a lack of trust in data.
The Build a Reporting and Analytical Insights Strategy blueprint provides a structured approach to overcoming these challenges by offering detailed methodologies for data integration, quality assurance, and strategy development. This methodology ensures that organizations can rely on their data to make informed decisions and drive business success.
“Success in technology and data leadership is contingent on the ability to effectively manage the move toward a federated operating model,” explains Sun. “To support this change, it is essential to follow humancentered design practices and deliver the outstanding reporting and analytical insights that stakeholders demand.”
The blueprint highlights the need for organizations to adopt best practices in data governance and integration, ensuring data quality and consistency across all reporting and analytics activities. By implementing these practices, organizations can create a unified data environment that supports strategic decision-making and operational
efficiency. Info-Tech also provides insights into developing a culture of continuous improvement, enabling organizations to adapt to evolving business needs and technological advancements.
Info-Tech’s resource advises the following four key elements to help organizations build an effective reporting and analytical insights strategy:
❯ Align Organizational Strategy With Reporting and Analytics Insights Strategy: Establish a clear business context to align the BI team with the most critical needs of business customers and the strategic priorities of the organization.
❯ Choose and Customize Operating Model: Select the appropriate BI operating model based on the existing BI environment and business requirements.
❯ Build a Reporting and Analytics Insights Strategy: Assess current BI maturity and define the future state to provide a benchmark for the BI program.
❯ Create a Strategy and Initiative Roadmap for Continuous Improvement: Develop a comprehensive BI strategy and roadmap to guide continuous improvement and ensure the BI program is fit for purpose.
Info-Tech’s blueprint equips IT leaders with the knowledge and tools to foster a datadriven culture within their organizations. By implementing the outlined strategies, organizations can enhance their decision-making processes, improve operational efficiency, and achieve better business outcomes. The firm’s insights ensure that organizations can build a resilient and adaptable BI framework that not only meets current demands but also evolves with future technological advancements and business needs.
Four months after strengthening its Strategic Leadership team to set the course for continued growth, Torontobased personalization and marketing transformation agency William Thomas Digital (WTD) announces its next wave of leadership enhancements with an investment in next-gen creative excellence and a series of strategic promotions.
Rado Ratkovic is hired as Creative Director, bringing 20+ years’ experience in creative leadership roles. Previously serving as Creative Director and Digital Design Director for Rangle, Wunderman Thompson, Bond, and Klick Health, Rado has earned a reputation as a peerless collaborator, leader, and creative innovator, with skills that will strengthen WTD’s creative capabilities, and accelerate its leadership across next-gen technologies and experiences.
Rado joins the agency’s integrated, poly-disciplinary Strategy house, within which Creative, Strategy, UX, and Insights cross-pollinate to fuel the agency’s globally recognized products and experiences.
Sheng Sinn is promoted to Chief Client Officer, a role from which his unyielding commitment to excellence and superior client service will touch WTD’s full roster of clients. Drawing from more than 20 years across Havas, Leo Burnett, BBDO, DDB, Wunderman Thomson and others, Sheng has been instrumental in strengthening WTD’s client service practice, passionately serving as strategic advisor for his portfolio of clients. Sinn’s mandate will include activating the full potential of the client service team across all clients, to set a new standard of excellence in
client-agency partnerships and personalized customer experiences.
Ruth Bastedo is promoted to VP, Senior Business Consultant. This newly created role will leverage Ruth’s strengths identifying strategic opportunities for businesses of all scales and specializations, and her entrepreneurial growth orientation honed over decades in the industry. In her new role, Ruth will work with the Strategy, Client Service and Growth teams to maximize growth across the agency at a time of unprecedented opportunity.
“As a purpose-built agency, we’re committed to keeping our clients two steps ahead,” explains Greg Elliott, Managing Director at William Thomas Digital. “And that means leveraging our agility as an independent agency to make big decisions and bold moves in ways others can’t — just as advancements in data, tech and consumer behaviours converge to drive watershed change.”
Emma Lyndon, COO, adds, “These promotions and Rado’s hire are a promise to our clients. We’re fully invested in building the agency that will carry them into the future, with senior roles in place to identify untapped growth, fully realize the opportunity creatively — and then deliver exceptionally.”
William Thomas Digital is at the forefront of creating personalized customer experiences, combining data-driven insights and technological innovation to craft engagements that resonate deeply with consumers. A trusted partner to some of North America’s leading brands, WTD is committed to driving growth for brands, establishing new benchmarks in customer loyalty, and delivering unforgettable experiences.
talkingpoints
Yellow.ai, a global leader in AI-first customer service automation, has unveiled research titled ‘Betting Big on AI-First: Insights from Customer Service Leaders.’ The study sheds light on AI adoption trends within North American customer service operations and contact centers. According to the findings, an impressive 84.5 percent of respondents either plan to or are already leveraging AI, underscoring its pivotal role in shaping the industry’s future.
The study highlights that North American organizations are increasingly ramping up their efforts in response to the rising demand for customer service automation, moving beyond basic automation to embrace AI-first approaches. Initial efforts across organizations of different sizes have focused on gathering insights and laying foundations, with a few deployments already live. The findings indicate a substantial upcoming increase in project executions, with 58 percent planning to integrate AI into their contact centers and customer service operations within the next 12 months.
The survey of 200 customer service professionals and executives across 12+ industries, conducted at Customer Contact Week 2024, provided a deeper understanding of the pivotal drivers of AI adoption, as well as current trends and attitudes towards AI integration in customer service operations, including:
AI implementation is present and functional across the entire CX function. A notable one in four organizations (26.5 percent) have already implemented AI within their customer service or contact center environments. The automation is not just limited to customer interactions, where over 60.2 percent have automated customer-facing processes, but also extends to 51 percent automating backend operations, showcasing a comprehensive adoption strategy.
Objectives to elevate key business metrics are fueling AI adoption. Nearly three-quarters of respondents view increasing efficiency (74.7 percent) and improving customer
satisfaction (73.2 percent) as the leading drivers for their AI adoption. A further 66.7 percent see reducing operational costs as a primary objective.
Data-driven decision-making is on the rise. The focus on customer service analytics and reporting is gaining momentum, with 49.2 percent of organizations automating this domain and 54.5 percent looking to implement AI to enhance data insights.
Despite enthusiasm for AI, some challenges persist. For 67 percent of respondents, integration with existing systems is the primary obstacle for AI implementation, with considerations around high implementation cost (36 percent) and data privacy (33 percent) following behind.
Executives are dedicated to training employees to work with AI. 56.5 percent are committed to upskilling or reskilling their workforce, ensuring that employees evolve alongside the technology as they adopt and integrate AI within their systems.
“Our study reveals that North American customer service professionals are eager to adopt AI, inspired by the success of pilot projects. Even those who have yet to begin AI initiatives are motivated to join this movement and avoid falling behind,” said Raghu Ravinutala, CEO & Co-founder of Yellow.ai. “Looking ahead, nearly onethird (32.5 percent) of respondents plan to implement fully autonomous customer service operations. This involves AI systems handling 100 percent of customer interactions, with employees up-skilled to oversee, manage, and fine-tune the AI system in real-time. This readiness demonstrates the industry’s embrace of future tech advancements, and we’re enthusiastic about leading this evolution for our customers.”
With 61 percent of executives foreseeing positive customer responses post-AI implementation, there’s evident confidence in AI’s ability to enhance customer experiences. Companies currently using AI in customer service display heightened confidence in its integrations, expecting 100 percent positive customer reactions. The survey strongly indicates that successfully and strategically integrating AI positions companies well for the future amid evolving technology and rising customer expectations.
High Tide Inc, the high-impact, retailforward enterprise built to deliver realworld value across every component of cannabis, announced its Cabana Club loyalty program has surpassed 1.5 million members across Canada.
Membership has increased 5 percent since June 13, 2024, when the Company reported Q2 2024 results. This represents an annual
growth rate of 37 percent. More than 1.2 million members have joined the loyalty program since the Company unveiled its innovative discount club model in October 2021, an increase of more than 400 percent since its launch.
“I am thrilled that our first-of-its-kind discount club model in cannabis has grown by more than 400 percent in just a few short years. This rapid surge in loyalty has resulted in our Canna Cabana store network generating more than 111 percent growth in same store sales since the launch of the discount club model. With our long-term goal of reaching at least 300 locations across Canada we believe our Cabana Club membership trajectory will keep growing to beyond 2 million members in the months and years to come,” said Raj Grover, Chief Founder and Chief Executive Officer of High Tide.
The Canna Cabana retail cannabis store located at 150 Sidney Street, in Belleville, Ontario will begin selling recreational cannabis products and consumption accessories for adult use today. This opening will mark High Tide’s 181st Canna Cabana branded retail cannabis location in Canada, the 66th in Ontario and the first store in Belleville.
“I am pleased to announce the opening of our first Cabana in Belleville, as we continue to selectively and strategically expand our innovative discount club model into underserved communities. The Ontario cannabis market, the largest in Canada, holds great potential for the future expansion of our Canna Cabana brand as we continue building towards our goal of 150 locations across the province. Our focus will always be to offer an unbeatable selection and prices to communities across Ontario and beyond,” added Grover.
High Tide Inc. is the leading communitygrown, retail-forward cannabis enterprise engineered to unleash the full value of the world’s most powerful plant and is the second-largest cannabis retailer globally by store count. Bricks & Mortar Retail: Canna Cabana is the largest cannabis retail chain in Canada, with 181 current locations spanning British Columbia, Alberta, Saskatchewan, Manitoba, Ontario and growing. In 2021, Canna Cabana became the first cannabis discount club retailer in the world.
From Raw Data to Real Profits: A
Primer for Building a Thriving Data Business
BY ARI LIBARIKIAN,
Building a profitable data business hinges on having not just the right data but also a business model and enterprise capabilities to support it. Almost two centuries ago, Lewis Tappan and John M. Bradstreet illustrated the potential for turning data into a profitable product. At the time, businesses and merchants were expanding their operations and needed a reliable way to determine the creditworthiness of potential partners. Bankers and investors were eager for more consistent, objective information in this burgeoning economy to guide their lending and investment decisions.
Tappan and Bradstreet established firms dedicated to collecting, analyzing, and selling data, along with the insights they derived from it. Their firms filled a critical gap in the market, eventually merging to form Dun & Bradstreet.
Fast-forward to today, when companies are often awash in data and trying to figure out if they can turn it into a business. The answer isn’t obvious. Building a data business is not for every
institution, especially in a field where a few dominant players with massive advantages in data already exist. However, the potential rewards can be immense for companies that can unlock unique data, analytics, or organizational know-how to create a product that addresses an untapped market opportunity.
A European building-materials company identified a new-business opportunity with more than $500 million in enterprise value by turning an internal tool for tracking key performance indicators (KPIs) into a product it could sell externally. Similarly, a telecom company is on track to realize $200 million in new revenue in less than five years by using its data to build a digital lending business. And they’re not alone.
McKinsey’s annual survey of business leaders on new-business building found that approximately 40 percent of them expect to create data, analytics, and AI-based businesses in the next five years — the highest of any new-business building category.
How do you know if building a data business can create value for
your organization? In this article, we share why now is the right time to consider it, how to assess whether it’s a good fit, and what the critical considerations are for getting started.
Why now
While leaders have sized up data businesses for over a decade, evolving technology capabilities and greater adoption worldwide of AI and analytics have increased the feasibility of monetizing data today. Four technology shifts in particular have enabled companies to create new data products faster, and less expensively than ever: Enhanced data-management efficiency: Companies can more efficiently process, manage, access, and reuse data in real time across different platforms thanks to greater sophistication of data tools and technologies. This efficiency is crucial for creating a scalable and sustainable data business.
Generative AI (gen AI): A few years ago, converting unstructured data, such as text, images, and videos, into a standardized form so it could be accessed and analyzed was prohibitively expensive for
What is a data product?
A data product aims to deliver a high-quality, readyto-use data set that can be analyzed and applied to various use cases to answer critical business questions. Data products eliminate much of the duplication of effort that comes with developing bespoke data solutions for each use case. Like physical products, data products evolve based on the needs of customers, who may include internal and external users. This modularity is crucial when building and scaling a data business to ensure the company can rapidly expand its offerings as customer needs evolve. As a best practice, a data product should be able to support varied requirements for data storage, processing, and management to match the different user “consumption archetypes,” such as digital applications or reporting systems. Each data product should be owned by a data-product manager responsible for building and continually improving it and ensuring it is well governed to build end-user trust and product quality. Finally, data products should be easily discoverable so that anyone across the enterprise can leverage them. (For more, see “How to unlock the full value of data? Manage it like a product,” on McKinsey.com.)
KAYVAUN ROWSHANKISH, MARKUS BERGER-DE LEÓN, AND VISHNU KAMALNATH
most companies. Gen AI has made structuring such data more cost-effective, enabling broader use. Combined with the emergence of low-code and no-code analytics platforms that democratize AI and analytics, data businesses can now derive more value from their data.
Increased access to real-world data: As Internet of Things (IoT) adoption accelerates, the costs and barriers associated with implementing sensor technology and capturing real-world data have significantly decreased. Companies can now gather real-world data more quickly and affordably and make it accessible to a broader range of applications.
Growing use of internal data products: Industry leaders are increasingly treating data like a product internally so that a given data set can support many different use cases (see sidebar, “What is a data product?”). This “data packaging” gives them a head start in monetizing their data.
a data solution to help suppliers better understand customers’ shopping behavior, among other insights. The company’s product, called Walmart Luminate, filled a gap in the market, enabling the company to achieve strong market adoption and 80 percent quarter-over-quarter growth during its first year. Because data businesses often require strong value propositions and unique data advantages to win, we expect a small group of data businesses to emerge and dominate industryspecific markets over the next decade. Those who come later may find it difficult to catch up.
Assessing the opportunity and the right strategy
At its foundation, a data business must have access to a sizable amount of data (internal or external) or an approach to processing data and extrapolating business value from it that is unique enough to address an
pursue three broad strategies for building such data sets, each with a different value proposition and critical success factors: Create an industry standard: Typically, these data businesses start as data aggregators, assembling a massive amount of unique data. Some can reach a tipping point when a network effect scales the utility of their product until it eventually surpasses alternative offerings and becomes an industry standard. This can be a very effective strategy. Consider Reddit’s $60 million annual deal enabling Google to train its AI models on its data. But it’s also one of the most difficult business models to pursue. Once the market leaders become established as a de facto standard, it is increasingly difficult for new entrants to compete. Succeeding here requires an asymmetric advantage in data access, a first-mover edge, or both. One financial-services firm has
a way that improves its predictive accuracy. It then offers customers an easy-to-use platform to access the resulting insights.
Additionally, we anticipate the thirst for data-driven decision making to intensify as leaders vie for their share of the up to $17.7 trillion in value potential from data and analytics. Add in another $2.6 trillion to $4.4 trillion from gen AI.3 This can create fertile ground for data and AI products. Consider Walmart Data Ventures, which launched
unmet market need. Demographic shopping data, for instance, may not be valuable today, given its ample availability from current market leaders in the space. However, data on real-time shopping preferences in niche market segments could be valuable to some companies as they localize market strategies.
In our experience, leaders can
positioned itself to become the go-to choice for accurate pricing predictions in regions where pricing dynamics change quickly. The company has done this by collecting novel data from satellite images, listings, public filings, ads, direct calls, and business locations (sometimes dispatching individuals in person to capture specifics not available online) and analyzing it in
Harness insights from an engaged user base: With the appropriate data usage rights, organizations can turn data collected from an engaged user base into valuable insights for advertisers, suppliers, partners, and users. Benchmarks and behavioral data from digital interactions, for instance, can be sold “as is” via data marketplaces or combined with analytics and sold as insights directly to buyers. Companies can also use these insights to sell targeted ads on their digital channels. This strategy depends heavily on the uniqueness of the data and the company’s ability to create a strong product value proposition for customers. The business case becomes more attractive if it can trigger a “flywheel effect” in which the sale of data products increases the sales or stickiness of core products. The financial-services firm paved the way for incremental revenue by stitching its popular data and analytics products into an intelligent workflow solution that automated a critical business process for its customers and accelerated their decision making. This integrated solution also boosted sales of the company’s
McKinsey & Company
other offerings because customers preferred to stay in its ecosystem for other data and analytics needs.
Turn sizable organizational know-how into a product: For example, knowledge and capabilities accumulated in building a tool to solve an internal business problem can sometimes evolve into a profitable offering. This was the strategy of the European buildingmaterials company cited earlier that turned an internal tool for tracking efficiency into a softwareas-a-service (SaaS) product. While leaders were initially concerned about cannibalizing the competitive advantage gained from this data, their analysis found that turning this data into a product would be more lucrative for the company than keeping the tool for itself.
This organizational know-how can also emerge as a company collects unique data as a by-product of its core operations. One company is transforming the way it operates and creating new data-driven revenue streams by adding IoT sensors to its assets and using the resulting insights to enhance its customers’ operations. For instance, with temperature and GPS data from the sensors, the company’s customers can make better routing decisions in transit for temperaturesensitive shipments.
Critical considerations for building an enduring data business
Leaders who identify a potential opportunity to monetize data should expect a three-to-five-year runway to achieve the economies of scale that are the foundation of a high-margin, enduring offering. (Launching a minimum viable product for market testing should occur within the first 12 to 18 months.)
Navigating this terrain successfully requires defining a strong customer value proposition, implementing an operating model and technology capabilities capable of scaling and sustaining the business, and addressing up front any data privacy and security concerns that might affect operations.
Defining a strong customer value proposition We find there are generally two product attributes that can impact customer value proposition and
adoption:
1. The type of “intelligence” a data product offers. The classic DIKW framework—data, information, knowledge, and wisdom—offers one hierarchy for assessing the potential value and durability of an offering. Companies can create profitable products by selling volumes of raw data or information—basically data that has been contextualized in some way, such as purchasing habits extracted from sales data. However, the higher a data product ascends in this value chain, the greater its value for end users and the more difficult it is for competitors to replicate it, resulting in higher margins and customer retention over time.
2. Product-delivery archetype. Raw data is usually delivered through a data platform such as a data marketplace. Other types of intelligence are offered through traditional insights platforms, such as an analytics tool, or intelligent applications integrated directly into an end user’s workflow. Here, the more integrated an offering is within an end user’s decision making and workflow, the greater its potential value to end users, the higher the margins, and the more likely it is that end users will come to view it as essential to their daily work, reducing customer attrition (Exhibit 4).
Over time, as your customer base increases, a virtuous cycle is created in which data and feedback from a growing body of interactions further differentiate the offering and increase customer loyalty. Early customer research and testing of a minimum viable product are essential in developing a new offering in order to avoid the trap of overestimating its potential. One common approach is recruiting a small group of target customers willing to be early adopters and to offer continuous feedback throughout the product build.
A European pharmaceutical company ensured the successful launch of its highly anticipated new data product by initiating a consistent feedback loop with potential customers to validate every feature during development. This included
frequent communication with customers and A/B-style prototype testing as it developed a minimum viable product. During development, subjectmatter experts also validated the algorithmic outputs frequently, which was crucial to winning the trust of the pilot customers. These feedback loops can be used throughout the product’s life cycle. While the financialservices firm’s initial offering was market leading, it realized through ongoing customer feedback that the value of its product increased with the timeliness of its insights. By creating a new workflow solution that delivered realtime intelligence at the point of decision making, the company could increase the value from its existing data assets fivefold to tenfold.
Adapting your operating model
One of the most common mistakes we have seen companies make in building a data business is neglecting to adapt their organizations and capabilities to effectively support the delivery of data products. Enterprises building data businesses need to orient their organizations around new profit-and-loss (P&L) expectations, new pricing and sales models, and investment in new technical skills: Incentivizing growth potential over short-term profits: Often, data products fail due to unreasonable performance expectations in the first year. As is the case with any start-up venture, during the first one and a half to two years, leaders should base incentives on KPIs that measure growth potential rather than short-term profitability. These KPIs typically include customer growth and retention, monthly recurring revenue (in the case of SaaS products), or lifetime value of a customer compared to the cost of customer acquisition (LTV versus CAC). One strategy to support this is separating the data business from the parent company through internal organizational and accounting controls or by creating a distinct legal entity. The European building materials company, for instance, spun off its data business as a subsidiary, enabling the new
entity to increase its autonomy and make decisions faster. Adopting new sales and pricing models: On the sales side, most data businesses will need to hire new talent and upskill existing talent to explain and demonstrate a data product’s value—tailoring and delivering demos, engaging customers early with pilot programs, developing relationships with senior technology or data decision makers, supporting new pricing models (for example, freemium models), and helping clients understand deployment considerations. For instance, one large consumer data and research company seeded a digital go-tomarket team to lead product-based sales efforts with free trials and targeted usage tiers as it built out its offering. Beyond initial sales, setting up an organization to assist clients in ensuring their customers’ success in using the product, as well as upskilling talent to provide ongoing client advice, can help improve the customer experience. Pricing data products can be tricky, as the value of “better, faster decision making and workflows” — often fundamental benefits of this type of offering — can vary more than it does for traditional products. The utility of a data product isn’t always immediately apparent to customers, making it more challenging to convince them that these products soothe a distinct pain point, especially when other options are available. As a result, it is critical to invest in adequate pricing research to ascertain the product’s usefulness to the customer and what the customer is willing to pay for it. This research can also often guide a company on how best to align its offerings with customer pain points and position them against alternatives on the market.
Investing in specialized technical skills: The goal is to support growth and sustainability of the data business and build out the company’s capacity to monetize data. The type of intelligence a data business delivers within the DIKW value chain will dictate the kind of technical talent a company needs. Leaders seeking to provide raw data will need to invest primarily in data engineers, while those seeking to provide end users with
more sophisticated insights, such as knowledge or wisdom, will need to increase their bench of data scientists as well as their AI and machine learning engineers.
Modernizing your data technologies
Without a modern data architecture, it is tough for a data business to scale and sustain a leading customer experience. Depending on the starting point and complexity of data assets, a strong data foundation could take anywhere between six to 15 months to establish.
From raw data to real profits: A primer for building a thriving data business Some foundational technologies are table stakes that every data business will require (Exhibit 5). Additional investments will depend on the type of data and delivery method used. For instance, some data businesses, such as
those seeking to provide customers with access to large volumes of data and information, typically provide a portal (commonly called a storefront) from which customers can search for and view details about the data sets.
Those companies that plan to offer an intelligence platform or develop a broad set of data products will need to embed strong MLOps and DataOps tooling, technologies, and practices into their platforms. These approaches enable companies to more rapidly, reliably, and cost-effectively deliver new AI capabilities as part of their offering, while effectively managing risk.
Managing data security, privacy, and intellectual property rights
Data security, privacy, and ownership are significant concerns for any leader. But the potential
impact these risks can have on a data company’s business models and ability to expand raises the stakes significantly. As a result, leaders should ensure that their business, technology, cyber, and legal teams collaborate often and early on assessing the opportunities.
Following are four issues that will require early attention: Understanding the rights you — and others — have related to data: What are the sources of your data–first parties, vendors, and so on — and how was the data acquired? Are there limits to how you may use the data or concerns about whether it is derived from underlying data sets that have issues (for example, training data for generative AI that may be copyrighted material)? Data businesses should assess their data and closely follow the evolving conversation over data rights,
particularly as innovative technology collides with, and spurs, these conversations. Developing consistent data privacy principles at inception: Identifying how the business will collect, use, retain, delete, and protect personal data before products launch can shield data businesses from potential setbacks and time-consuming hurdles when introducing new products and features, as well as uphold trust with customers.
Examining and tracking local laws: Varying country, regional, and sector laws may influence how a data business collects, shares, processes, stores, secures, and manages data. Additionally, some jurisdictions have more clearly defined regulations than others, which leads to greater predictability. Leaders will need to consider their appetite for the uncertainty and risk of operating in areas where regulations are not so clearly defined.
Prioritizing data governance and security: This is typically the “weakest link” that prevents data businesses from scaling. Data governance and security capabilities, such as quickly identifying and resolving data issues and effectively managing data access and entitlements, are foundational to delivering a quality product to a growing user base.
Building a valuable data set and associated insights can take time, giving those that move first a sizable advantage in seizing untapped market opportunities. But institutions that enter this market should have a unique data set that addresses an unmet customer need and the right capabilities to scale their product.
Those who do may not only build a scalable and profitable business but also potentially create an enduring brand.
ARI LIBARIKIAN and KAYVAUN ROWSHANKISH are senior partners in McKinsey’s New York office, MARKUS BERGER-DE LEÓN is a senior partner in the Berlin office, and VISHNU KAMALNATH is a partner in the Boston office. The authors wish to thank PRAVANJANA DAS and ROB MUELLER for their contributions to this article.
McKinsey & Company
Transforming the Customer Experience:
An Interview with Jon Picoult, Founder and Principal, Watermark Consulting
Jon Picoult is a renowned expert on customer experience and the author of “From Impressed to Obsessed: 12 Principles for Turning Customers and Employees into Lifelong Fans”.
BY STEPHEN SHAW
You would think by now that just about every company would have accepted the necessity to deliver an exceptional customer experience.
Apparently not, according to Forrester Research.
Forrester’s latest Customer Experience Survey found that CX quality has fallen for an “unprecedented” third year in a row. In fact, average CX quality has dropped to its lowest point since 2016. “US consumers are having, on average, the worst experiences in a decade,” explained Rick Parrish, VP and research director at Forrester.
“Brands want to create better experiences, and they realize that putting the customer at the center of their business is the way to do it. However, organizations struggle with the scale of change that this requires.”
Forrester also conducts Net Promoter Score rankings for 100 brands in Canada and the most recently published report mirrored the CX findings: the NPS score dropped or remained the same for all industries last year. In commenting on the NPS results, Parrish stated what every consumer already knows: “In Canada, brands are struggling to provide effective, easy, and emotionally positive experiences for customers”.
He went on to say: “When brands put customers at the center of their leadership, strategy, and operations, they enable stronger customer loyalty — which in turn drives revenue, profitability, and business resilience, even in uncertain times.”
INTERVIEW
All too true. Strangely, that linkage between loyalty and profitability seems to have escaped the notice of most CEOs.
Treating people with respect — showing appreciation for their business — making their lives easier, more productive, more rewarding — minimizing their level of effort — being a company that cares about customers after the sale as much as before — all of that goes a long way to securing greater brand loyalty. Yet most companies get a failing grade when evaluated against those basic principles. It seems companies are headed in the opposite direction, prioritizing profits over people. Customer service is seen as a cost — which explains the rush to adopt AI-powered chatbots meant to take over from live agents. It also explains why it can be so hard to find a phone number to call if you have a question or complaint.
Today more than ever customers are feeling shafted, exploited, taken for granted. And their simmering resentment over complaint handling is spilling over into angry confrontations with front line workers, according to the 2023 U.S. National Customer Rage Survey. “The incidence and public displays of customer rage are commonplace, on the increase and can be scary”, the report observes.
Corporate mistreatment of customers is baffling when you consider that repeat buyers almost always account for a disproportionate share of total sales and are the only sustainable source of recurring revenue. As long as the experience a brand offers is indistinguishable from competitors, it is substitutable, and doomed to compete on price.
Marketing has to shoulder some of the blame for this corporate apathy toward CX, too busy chasing market share to give much thought to expanding “share of heart”. Certainly most CMOs appreciate the importance of delivering something other than a “me-too” experience even if company leadership does not. It’s just that most marketers struggle to be seen as “serious people” capable of serving as change agents, even though their job is to make an emotional connection with people. And according to renowned CX expert Jon Picoult, the transformation of customer experience starts with understanding how people feel.
To create genuinely loyal customers — emotionally committed customers who love the brand, who will go out of their way to buy it, who are true fans, who are quick to forgive and to recommend — Picoult believes it is essential to deliver a more “memorable experience”. In his book “From Impressed to Obsessed” he offers 12 principles for turning customers into “lifelong fans”, derived in part from insights into how the mind works from the field of cognitive science where he gained his university degree.
STEPHEN SHAW (SS): What drew you to the field of cognitive science?
JON PICOULT (JP): The thing I had always been interested in at university was getting
computers to mimic human thought. This was 25 or 30 years ago, and people had been promising AI for years. My focus was understanding how the human mind interprets language and then getting computers to replicate that. And I have to admit, I never expected that I would use my cognitive science degree in the way that I have. Because today it’s central to my philosophy, the notion of how you sculpt memories and customer experiences.
Marketing has to shoulder some of the blame for this corporate apathy toward CX, too busy chasing market share…
the term means, and that it’s fundamentally different from customer service.
SS: In its recent annual CX assessment of different industries Forrester Research reported that quality has hit a new low and has declined for the third year in a row. What do you think are the main factors behind this trend? You’d think things would be getting better, not worse.
JP: Fundamentally, the issue is that there are two types of CX strategies — you either focus on maximizing loyalty or you focus on minimizing attrition. And those are two very different strategies. Maximizing loyalty is about trying to figure out how do I enrich the lives of my customers. How do I create that emotional connection? How do I turn them from “impressed to obsessed”? But if the strategy is minimizing attrition, as it is for most companies, what they’re asking themselves is, what’s the minimum amount that we can do that will keep people from leaving us? And that’s a very different strategy. If you look at your retention numbers, if you look at your financials, you might say, okay, we’re striking the right balance. But I think that’s short-sighted. It might deliver short term profits, but I don’t think it’s a long term solution. That’s why customer experience is not improving across the board.
SS: What made you decide to focus on CX as a discipline?
JP: In my financial services career, I ended up leading not just customer service, but also marketing, operations, distribution, sales, and even IT, at one point. I had an opportunity to see how the customer experience was being engineered and delivered from several different perspectives. Customer experience was something I was always passionate about. And I felt I had some credibility, having served in all of those different functional roles. And where I recognized many companies go wrong is they don’t realize that each functional silo is working at cross purposes and not coalescing around a common vision of the experience.
SS: When you started down that path 15 or so years ago was there a groundswell starting to form around CX?
JP: Back then, if you said customer experience to someone what they heard was customer service. Even today, I find that’s a challenge with many organizations. It is still unusual to come across a C-suite that views customer experience as more than just the initiative de jour. I think that many CX efforts are corporate window dressing. It’s a box to be checked. And so I absolutely believe that there is still a lot of missionary work to be done for senior leaders to really understand not just the value and the ROI of customer experience, but also just what
SS: It seems to me that most companies view customer service as a cost, not as an investment in the relationship. And that is abundantly clear in a recent New York Times article on Amazon whose clear intention is to automate as much of customer service as possible to take cost out of the business.
JP: One thing I would say about Amazon — and I haven’t read that Times article — but they look for ways to make the experience not only more pleasant, but also more efficient. And a key way of doing that, of course, is automating things. And if a company can solve a customer’s problem without having them to talk to a live rep, that’s going to be their preference.
One thing I would say about Amazon is that they are a company that has been very thoughtful about how to introduce automated and self service capabilities. As a consumer, I encounter a lot of chatbots that just have me ripping my hair out. I had one just this morning with my cable company, one of the most hated companies in the country. I was boiling over with anger. I have to contrast that with Amazon: I think they’ve been very clever about how they have structured their chatbots. One thing I think Amazon’s done well is triaging in an automated way to direct people to the service channel that’s best for them.
SS: Here in Canada there are some truly abysmal companies whose underwater NPS scores don’t seem to trouble them at all because they’re making out like bandits. You’ve got the telcos, the banks, the cable
INTERVIEW
guys, the national airlines, for sure, with the exception of a company called Porter. If you were talking to their respective boards today, what would you say to them?
JP: What I would tell the C-suite at those companies, or their boards of directors, is you need to look at the annals of corporate history and see that it is littered with the carcasses of companies that were dominating their markets, that didn’t think they needed to invest and improve their customer experience. And then what happened? Some new company figured out a better way to serve the customer and ate their lunch accordingly. Eventually, if you are truly delivering a mediocre experience to your customers, you are ripe for disruption by new entrants. The party eventually ends.
SS: I think part of the challenge here are oligopolies that have a stranglehold on the marketplace. You certainly see that in the grocery business here in Canada.
JP: We as consumers also bear some responsibility for encouraging the behaviour you’re talking about, because we are lazy creatures at heart. And this is what companies count on. Companies count on consumers not investing the time and energy to research an alternative, to transition to another cable company or some other insurance provider or bank. So we as consumers need to make sure we vote with our feet. You need to overcome what is an inherent human quality to take the path of least resistance by sticking with your current provider, even though you hate them, because they’re just not bad enough to make you invest the time to go elsewhere.
SS: One the key concepts in your book is the idea that’s it’s important not just to create a satisfied customer, but to create a memorable experience. Smart companies, you say, are in the business of shaping customers’ memories. What people remember are the peak experiences. Can you just elaborate on this theory, which I think is drawn from cognitive science, is it not?
JP: The whole idea that you’re talking about is this notion that what great companies recognize is that they’re not just in the business of shaping people’s experiences, they’re in the business of shaping their memories. And indeed, I would argue that how people remember their experience with a business is even more important than the experience itself. An experience doesn’t play back in our memory like watching a video. That is not how it works. We remember experiences as a series of snapshots. And it’s not just any snapshot that we remember. Where the camera shutter clicks is during the peaks in the experience - the high points — and during the valleys in the experience — the low points — and then also at the last thing that happens to us. That’s what gets cemented in our memory. The last thing that happens to us in any interaction exerts a
disproportionate influence over our perceptions and memory of that interaction. So that’s what I’m talking about when I discuss the psychology and the memory science behind customer experience. Because what I think great companies do is they understand these principles and they work very thoughtfully and deliberately to sculpt people’s perceptions and memories.
SS: If you have an equal number of good and bad experiences, is it effectively a wash?
JP: Not really. What happens is the things that are going to exert greater influence happen towards the end. If there are valleys, you just want to make sure that they are predominantly not happening in the latter half of the experience. Because again, just the way our memory works, if you have an equal number of peaks and an equal number of valleys, if the peaks are happening towards the end, that’s going to carry the day, and in some, that’s going to elevate the impression rather than deflating it.
Great companies…work very thoughtfully and deliberately to sculpt people’s perceptions and memories.
SS: You state that how a customer feels about the experience is really what matters. Is that something you can actually design for in advance? Can you actually design the experience to drive feelings of joy, or relief, or exuberance?
JP: Absolutely. One of the twelve principles in the book is about stirring emotion. You might remember in the book there was a company called Framebridge. They’re an online service for framing photos or memorabilia. And my son used them one day to frame a picture, a gift for his grandparents of a photo he had taken of some mountains in the Berkshires in Western Massachusetts. He got the package back with the framed photo, and there was this personalized, typewritten note in it, which clearly indicated that somebody had looked at the photo. And the note said something like, we love your photo, we can just feel the clear,
crisp mountain air, and I mean they frame hundreds of thousands of pieces of photos and artwork every year. It was clear that this was a personalized note. Talk about memory making. Because there’s an element of surprise there. There’s an element of reassurance. So, yeah, absolutely, I think that you can, by design, create an experience that is emotionally resonant.
SS: We do satisfaction and loyalty studies for some of our clients and one of the anomalies we regularly see is if a product complaint or warranty claim is satisfactorily handled, that person ends up being more loyal than the average customer. There’s a term for it that you reference in the book: the Service Recovery Paradox.
JP: Yeah. So if you go back to that principle about the recency bias and making sure you finish on a high note, what that really indicates is that if there is an issue, like a warranty claim, if it is handled really well, you have the opportunity to create a peak at the end of the experience that actually eclipses the negativity of the product failure itself. It’s just the way our memories work. If you over rotate on that recovery, in this case, the warranty claim, that’s what people are going to remember, and that will dissolve from their memory that they even had to make a warranty claim in the first place. You can actually create a more loyal customer after the recovery than what you had before the failure.
SS: Which ones of your twelve principles are in your view the most critical to getting the customer experience right?
JP: I’m going to highlight three. The first one, I would say, is the idea of making it effortless for customers. I said to you a little earlier, we are lazy creatures at heart. That is just human nature. Oftentimes it’s not the best product or service that wins in the marketplace. It’s just the one that was easiest for people to access and utilize. Making the experience effortless is a really effective way to endear yourself to your customer, because customers’ most valuable, finite resource is their time. And when you make it effortless for them, you are giving them the gift of time and convenience, and in return, they’ll reward you with their enduring loyalty.
The second one I would call out would be the idea of stirring emotion. And we talked about this already. I quote in the book, Jonathan Haidt1, who’s a famous social psychologist and professor at NYU, who once said that the emotional tail wags the rational dog. Because customer perceptions and memories of an encounter are influenced by how they feel after that interaction. If every time you call a company, you’re made to feel like you were a burden on that service rep, if you were made to feel unvalued, or uninformed, or dumb, or embarrassed in some way, it doesn’t matter that they pick up the phone in 10 seconds flat, you’re still going to walk away with a negative impression. So that’s why the emotion piece is so critical.
The third one that I would highlight for you, which I have to admit that I love a little bit more maybe than the others, is the notion of giving your customer the perception of control. The reason I love this one is because it often costs nothing, yet it has such a significant impact on the impression that you leave on your customers. Again, this goes back to psychology. We, as human beings, like to be in control of what’s going on around us. We are control freaks at heart.
If people are made to feel like they have control over the experience, they will feel better about that experience. It’s almost magical that way. And I think the classic example of this is the difference between a known wait and an unknown wait. When you get into line, and you’re told the approximate wait time, that feels like a very different experience than if nobody says anything to you. Because when you don’t have your expectation set, you are wallowing in ambiguity. And when you’re wallowing in ambiguity, you feel like you’ve lost control of what’s going on around you. Just by setting an expectation of a wait time totally changes the experience.
SS: As you know, the way a lot of companies are trying to eradicate pain points is through customer journey mapping. You’re not a big fan. I’m curious why that is.
JP: I think that it is a tool that is suitable in certain instances, such as helping to give people in an organization a better idea of how the experience actually unfolds and how the typical customer feels at each stage of that experience. So I’m not saying it’s not without value. However, the issue that I’ve got with customer journey mapping is that it is typically done at a 30,000 foot level, meaning that you get a bunch of people in a room and there are post it notes and there’s a big poster board, or there’s a big wall where you’re kind of drawing out this graphical visual illustration of the customer journey, and you’re mapping what you think are the high points and the low points. And one key miss is that many companies never involve customers. You could do it, but you would come up with a whole bunch of conclusions that you’re going to throw out once you inject the customer perspective. So the way people go about journey mapping is just navel gazing, and it often has important blind spots. The hearts and the minds of your customers are not won at 30,000 ft. Traditional journey mapping exercises don’t go deep enough, in my view, to reveal some of the minutiae that can have a very significant impact on the impression being left on your customers.
SS: I want to touch on another key area which is measurement. What’s on your ideal CX dashboard?
JP: I think that many CX practitioners are almost religious in their zeal for a particular metric, and I think that’s dangerous. Every metric has pros and cons, and I think that it’s important to acknowledge that. Personally, do I think Net
Traditional journey mapping
exercises don’t go deep enough.
Promoter is a good measure? Absolutely. I like it, and I probably favour it over a standard CSAT measure. But the reason I say that is because I think that Net Promoter, when implemented correctly, can be a very effective tool for helping to shape a culture. But I would never tell anyone, hey, it’s NPS or nothing. That I think is crazy. The key is you just need to make sure that there is a formal, institutionalized and disciplined approach for continuously soliciting the input of your customer. And that voice of the customer program should not be limited to just surveys. I think there are other instruments that the company should be using to make sure they’re keeping their finger on the pulse of the customer.
SS: What does an optimal organizational structure look like to support end-to-end CX? JP: Some of the most successful setups I’ve seen are dependent not on the function, but rather on the person. And here’s why I say that. People who are successful in a CX leadership role within an organization have a very unique set of skills, in my view. It’s a unique balance of right brain and left brain. You could give CX to a marketer, and they could come up with all kinds of great ideas, but they might struggle with execution, they might struggle with practicality, and so that doesn’t get you anywhere. Now, conversely, you could give it to an operations person, and they might be great at execution and practicality, but they might be limited in terms of thinking, “How do I bring the brand to life in every interaction?”. Which the marketer would be able to do effectively. So I find that the people who really succeed in these roles are the ones who can easily skate across those different domains. And the fact is, those people could live anywhere in the organization. I think you’ve got to ask yourself, who’s the person on my team that is best suited to build relationships with their peers? Because let’s face it, that’s a really tough challenge for anyone in a CX role. You need to be an influencer with the heads of all the different functions — somebody who can earn the respect of the head of sales, of the head of service, of the head of distribution, of the head of IT.
SS: Assuming a company wants to become customer centric, who should be accountable for driving that change?
JP: The answer I would give you is executivelevel commitment. There is simply no substitute for having a CEO or a President or some other member of the C-suite who is just inherently passionate about this topic and believes it in their bones. Because that’s how you start to get away from the initiative de jour pitfall. It’s not a surprise that you often find that in founders of companies. I mean, this has to be woven through the organization, and there’s no better person to do it than the person who’s at that C-level. Might that C-level individual choose to hire a head of customer experience to spearhead things? Yes, and that might be the right answer. Might there be a steering committee? Yes. But ultimately it’s that C-suite individual, because that’s the person who’s going to look the head of IT in the eye, the head of legal in the eye, the head of service in the eye, the head of sales in the eye, and have the tough discussions with them to make sure they are working to optimize the end to end customer experience.
SS: And the CEO is the one who has to convince the board.
JP: When I launched Watermark in 2009, I said to myself, hey, what language does the board understand? What language does the C-suite understand? And the language that they understand is the language of shareholder value. Whether you’re a public or private entity, that is a language everybody understands. And that’s when I said to myself, hey, wouldn’t it be interesting to take a look at the shareholder returns of the publicly traded companies to compare the best and the worst in customer experience. And from that was born the Watermark CX ROI study2, which we’ve updated every few years now, and there’s a new one coming out actually later this summer. The companies that lead in customer experience outperform those that lag by over three to one in shareholder return. And that’s for, I think, a 13 year period. To me, that’s the exclamation point on the case for customer experience. What that study really shows is that the return from a great customer experience is not soft and intangible. It is real, it is material, and you can take it to the bank.
1. Jonathan Haidt is an American social psychologist whose area of study is moral foundations theory which deals with the origins of human moral reasoning.
2. The Watermark Customer Experience ROI Study is based on the cumulative total stock return of Customer Experience (CX) Leaders and Laggards as identified by third party CX rankings.
STEPHEN SHAW is the Chief Strategy Officer of Kenna, a marketing solutions provider specializing in delivering a more unified customer experience. He is also the host of the Customer First Thinking podcast. Stephen can be reached via e-mail at sshaw@kenna.ca
Harnessing the Power of Loyalty Programs:
The intersection of retail media networks and data-driven advertising
BY JEREMY SIMPSON
In the ever-evolving landscape of datadriven advertising, the recent rise of Retail Media Networks (RMNs), backed by some of Canada’s largest rewards and loyalty programs, have emerged as a growing force. eMarketer reporting expects 15.3 percent growth in Canada retail media this year, which is outpacing global growth at 0.1 percent and is the fastest growing media channel, even surpassing OTT growth. This progression and evolution of RMNs is reshaping how brands engage with media partners, ad tech and consumers in Canada.
in Canada
The symbiotic relationship between loyalty programs and RMNs not only fuels the sale of advertising but also drives the advancement of ad tech, unlocking new opportunities for targeted marketing and personalized experiences.
Let’s explore how loyalty programs serve as the driving data points behind the sale of advertising within RMNs and the implications for brands and retailers in Canada.
Leveraging loyalty data
At the heart of RMNs lies the vast pool of shopper data, and
loyalty programs serve as the primary source of this data. Every transaction, interaction, and engagement within a loyalty program generates a wealth of data points, providing retailers and brands with important insights into consumer behaviour, preferences, and purchasing patterns. From browsing history to past purchases, demographic information to location data, loyalty programs capture a comprehensive view of the customer journey, empowering advertisers to deliver highly targeted and relevant ads.
Targeted advertising precision
By using loyalty data with RMNs, advertisers gain unprecedented precision in targeting their campaigns. With access to granular insights about consumer buying behaviours, brands can tailor their advertising messages to resonate with specific audience segments, maximizing relevance and engagement. For example, a retailer can leverage loyalty data to identify high-value customers and deliver personalized promotions or recommendations based on their purchase history and preferences. This targeted approach not only enhances the effectiveness of
The convergence of loyalty programs and RMNs is driving innovation in ad tech.
advertising but also fosters stronger connections between brands and consumers.
Navigating data privacy and consent
The utilization of loyalty data within RMNs also raises important considerations around data privacy and consumer consent. Retailers and advertisers must prioritize transparency and compliance with regulations such as the Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada. By implementing robust data governance practices and obtaining explicit consent from consumers, retailers can build trust and confidence in their advertising initiatives, fostering long-term relationships with customers. Ensuring the underlying consent requirements of consumers that are tied to these loyalty programs is fundamental before moving with forward with any application of that data to inform media decisions.
Monetizing first-party data
For retailers, loyalty programs provide meaningful first-party data that can be monetized to help provide consumers with the products and services they want by combining the data with media and creating an RMN. By offering advertising opportunities within their loyalty ecosystems, retailers can capitalize on their deep understanding of customer behaviour to create value for both advertisers and consumers. Through targeted ads, sponsored content, or exclusive offers, retailers can enhance the overall loyalty program experience while generating incremental revenue streams. This monetization strategy not only drives profitability but also reinforces the retailer’s position as a trusted intermediary between
brands and consumers. However, it is important for marketers and advertisers to maintain an eye on owned assets versus purchased (offsite media and the cost of inventory vs. data, managed service fees, etc.). IAB in the US has highlighted best practices and allows for focus on RMNs that align with this approach
Driving innovation in adtech
The convergence of loyalty programs and RMNs is driving innovation in ad tech, paving the way for advanced targeting capabilities and measurement solutions. Advertisers can leverage machine learning algorithms and predictive analytics to optimize campaign performance and maximize ROI. In theory, by analyzing loyalty data in realtime, advertisers can identify emerging trends, anticipate consumer preferences, and tailor their privacy safe environments for activation and analysis across several platforms and teams — it’s likely there is a lag in optimization and a lost opportunity within the current state. Additionally, advancements in attribution modeling and cross-channel tracking enable advertisers to measure the impact of their campaigns across the entire customer journey, from initial impression to final purchase.
Closed-Loop measurement
A unique aspect of loyalty programs and RMNs is the implementation of closed-loop measurement strategies within the corresponding retail banner. Closed-loop measurement refers to the ability to track the entire consumer journey, from ad exposure to purchase, within a controlled ecosystem. By integrating loyalty program data with RMNs, retailers and advertisers can achieve unprecedented visibility into the
impact of advertising on actual purchase behavior. For instance, a retailer can attribute sales directly to specific ad campaigns, enabling a comprehensive analysis of return on ad spend (ROAS) and marketing effectiveness.
Promoting measurement that spans the consumer journey is a worthwhile, as marketers and advertisers often chase short-term metrics as opposed to adopting a fuller approach to understanding consumer buying behaviour and contributions from upper and lower funnel activities. This closed-loop approach not only validates the performance of advertising initiatives but also facilitates continuous optimization and refinement of targeting strategies, driving greater efficiency and ROI for brands. By leveraging closed-loop measurement, retailers and advertisers in Canada can unlock valuable insights into the true impact of their marketing efforts, ultimately driving growth and profitability in the competitive retail landscape.
Navigating data transparency and management challenges
Despite the undeniable benefits of leveraging first-party data within RMNs, challenges related to data transparency and sharing persist within the Canadian retail landscape. According to a survey conducted by the Path to Purchase Institute, 28 percent of marketers highlighted transparency and sharing as significant hurdles in working with RMNs. This underscores the importance of establishing clear guidelines and protocols for data usage and sharing among retailers, brands, and advertising partners. Additionally, the proliferation of RMNs introduces complexities in platform management, as noted by approximately one-fifth of marketers. With a
multitude of platforms — from marketplaces like Amazon to mass merchandisers like Walmart and category specialists like Loblaw Companies — each offering distinct nuances in execution and measurement, retailers and advertisers face the daunting task of navigating diverse ecosystems. Furthermore, comparing performance across RMNs poses a formidable challenge, as highlighted by 40 percent of marketing executives in a Q4 2022 Forrester survey cited by Forbes. The varied metrics, attribution models, and reporting structures employed by different RMNs necessitate a nuanced approach to performance analysis and benchmarking, requiring marketers to invest in sophisticated measurement tools and methodologies. Addressing these challenges will be paramount to maximizing the efficacy and efficiency of RMN advertising campaigns in Canada’s rapidly evolving retail landscape.
Conclusion
In conclusion, the integration of loyalty programs into RMNs represents a paradigm shift in data-driven advertising in Canada. By harnessing the power of loyalty data, retailers and brands can deliver targeted, personalized experiences that resonate with today’s discerning consumers. As RMNs continue to evolve and innovate, fueled by the rich insights derived from loyalty programs, they will undoubtedly remain at the forefront of driving advertising effectiveness and efficiency in the Canadian retail landscape.
JEREMY SIMPSON is the Chief Activation Officer for Omnicom Media Group. This article originally appeared on the Canadian Marketing Association’s AdTech Committee content section.
U.S. Direct Mail Is Up: Is Your Volume Growing, Too?
BY HEIDI TOLLIVER-WALKER
So much has been said about the declines in the volume of direct mail. As the volume in the mailbox has decreased, however, much is also being made about the direct mail’s increase in effectiveness when it does arrive.
What do the latest statistics say? They say that direct mail’s effectiveness is fueling its comeback.
Let’s start with data from Statista. According to the research company, the global direct mail advertising market is expected to grow at a CAGR of 1.14 percent from 2024 to 2029, reaching a projected market volume of $61.59 billion by 2029. Statista projects a CAGR of 2.7 percent from 2024 to 2028, reaching a market size of $87.93 billion by 2028.
Direct mail investments are up
This data is consistent with that from Sequel, a direct response marketing agency. After surveying 350 U.S. B2B companies and B2C companies, it found that, 61 percent of marketers have increased their direct mail investments in the last 12 months, up 12 percent from 2023. Looking ahead to the next 12 months, Sequel found that 54 percent of marketers expect to continue to increase their mail budgets, the highest increase of any surveyed channel.
Sequel also found that direct mail volume is not only up, but engagement with direct mail is up, as well. Its survey revealed that the percent of people actively engaged with their mail is up between 2023–2024 to 72 percent.
Cost per acquisition is dropping
This is good news, especially since direct mail isn’t known for having a low cost per acquisition. On average, its CPA has been $100–$250. But as people pay more attention to direct mail, and more companies are using targeted messaging, that CPA has started to come down. Sequel found the percentage of respondents with a direct mail CPA of less than $150 has doubled, from 29 percent in 2023 to 66 percent in 2024.
While only a single company, Postcard Mania recently shared its internal numbers, and they support both sets of research.
Shared in its blog post, “166 Direct Mail Statistics You Should Know in 2024,” PostcardMania reported that its average weekly mail volume has increased 104 percent between 2010–2019. Between 2019–2022 alone, average weekly mail volume increased 39 percent.
Not only this, but PostcardMania analyzed 114,373 leads that converted to sales. It found that postcard leads generated 6x more revenue per lead than leads from digital sources.
We attributegrowth to…
To what do we attribute this growth? Yes, there is less mail in the mailbox and we are seeing an increase in targeting that makes direct mail more effective. But according to Sequel, this growth also has to do with the advantages of direct mail, specifically. Here are the top three:
1. Quality audience targeting data
2. Ease of tracking attribution and performance.
3. Ability to integrate with digital campaigns.
In fact, with a nod to the latter, Sequel found that 76 percent of marketers are combining/plan to combine the two most valuable direct response tactics: email and direct mail.
So is direct mail surging? You bet — and it’s growing for all the reasons we’ve been talking about for years. So these numbers shouldn’t be any surprise to any of us in the printing industry. It’s just really fun to see them “out there” for customers to see.
HEIDI TOLLIVER-WALKER is former print industry magazine editor and long-time industry analyst, content developer, author, and blogger. She has written for the industry’s top publications, research companies, and private companies for the past three decades — so long that she still has an AOL address, which she signed up for back when AOL was still cool. You can reach her at htollvr@aol.com.
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With an aging population, increased immigration, relocation and changing commuter habits, our suite of demographic products help you stay on top of the changes – nationally, by neighbourhood, and everywhere in between.
Environmental concern, privacy, trust and social connectedness. Psychographic indicators have shifted and Canadians’ social circumstances have changed. It’s never been more important to look at these indicators and map them to different populations.
EA’s ground-breaking mobile movement and web behaviour databases not only help you keep track of the “clicks versus bricks”, but know which Canadians are driving these trends nationally and locally as those behaviours change.
Comprehensive, updated financial metrics on Canadians. Understand who is most stretched to make ends meet, who is affected by property market conditions and who has money to donate to their favourite charities.
