Best practises in market mix modelling by Jon Schäffer

Best Practices In Market Mix Modeling by Jim Nail

Helping Business Thrive On Technology Change

BEST PRACTICES

August 25, 2005

B E S T P R AC T I C E S

Includes Forrester user interview data

August 25, 2005

Best Practices In Market Mix Modeling

This is the second document in the “Marketing Measurement” series. by Jim Nail with Chris Charron and Jennifer Joseph

EXECUT I V E S U M MA RY Increased demand by CEOs for greater accountability of marketing spending comes just at a time when proliferating media choices undercut the simplicity of measuring branding or sales impact. Companies can learn valuable lessons from CPG marketers who have begun to untangle multichannel marketing’s eﬀects using sophisticated statistical models. The keys to success lie in scoping the project correctly, gaining the support of stakeholders, and working with a modeling specialist who can apply the ﬁndings to business problems.

TABLE O F CO N T E N TS 2 Left Brain Principles Come To Measurement Models Require Clarity And Commitment 5 The Market Mix Modeling Voyage Phase 1: Plan The Project And Pick The Right Vendor

N OT E S & R E S O U R C E S Forrester interviewed 31 vendor and user companies, including: Accenture, ACNielsen, The Advertising Research Foundation, Dratﬁeld Analytics, iKnowtion, Information Resources Inc., Knowledge Networks, Sequent Partners, and Veridiem.

Phase 2: Prepare The Data And Organization Phase 3: Build, Validate, And Enhance The Model 9 Choosing The Right Market Mix Model Partner WHAT IT MEANS

13 Models Will Subsume The Marketing Process 14 Supplemental Material

Related Research Documents “What B2B Marketers Need From Technology” April 8, 2005, Trends “Where Is Marketing Measurement Headed?” January 13, 2005, Trends “Left Brain Marketing” April 6, 2004, Forrester Big Idea

© 2005, Forrester Research, Inc. All rights reserved. Forrester, Forrester Oval Program, Forrester Wave, WholeView 2, Technographics, and TechRankings are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Forrester clients may make one attributed copy or slide of each ﬁgure contained herein. Additional reproduction is strictly prohibited. For additional reproduction rights and usage information, go to www.forrester.com. Information is based on best available resources. Opinions reﬂect judgment at the time and are subject to change. To purchase reprints of this document, please email resourcecenter@forrester.com.

Best Practices | Best Practices In Market Mix Modeling

RESEARCH CATALYST Results of a survey conducted by Forrester and the Association of National Advertisers on marketing accountability revealed substantial interest in market mix modeling as a tool to bring greater accountability to marketing spending.1 LEFT BRAIN PRINCIPLES COME TO MEASUREMENT Left Brain Marketing — analytical marketing strategies, skills, and processes — is not just for database marketers.2 During the past 15 years, marketers have begun to untangle the sales impact of brand advertising and other elements of an integrated marketing mix, with the help of three technological developments: 1) greater availability of store-level sales data; 2) increasing data processing power; and 3) the application of statistical techniques.3 CPG companies, pulling from the vast data stores of Information Resources, Inc. (IRI) and ACNielsen, have pioneered the technique of modeling marketing tactics with business results. In recent years, this success has begun to interest other industries. For those not familiar with the concept, a market mix model:

· Links marketing spending to sales results. A marketing model lines up a company’s advertising, promotion, and sales activities with product sales over time (see Figure 1). Using statistical techniques such as linear and multivariate regression analysis, statisticians look for correlations between marketing activity and sales volume and then tease out the sales impact of individual media or promotional elements of a campaign. The result is an equation that mathematically describes consumers’ responses to marketing stimuli more comprehensively than a simple direct mail response report and with greater sophistication than a weekly sales report.

· Quantiﬁes the impact of multiple demand drivers. Coeﬃcients in the equation describe

the relative impact of each marketing activity. But marketing isn’t the only thing that drives demand. For example, the price of gasoline has dampened consumer enthusiasm for SUVs, while a scorching heat wave may spur consumption of soft drinks. Modelers compile data on these external factors, analyze their impact on sales, and include relevant factors as additional coeﬃcients in the model equation.

· Reveals marketing’s incremental sales impact. Almost two-thirds of marketers agree on

“incremental sales generated by marketing” as the deﬁnition of ROI.4 Market mix models typically show that 50% to 60% of sales would likely have occurred in the absence of marketing. The model then assigns the remaining sales to the marketing activity that stimulated them and calculates the sales (or proﬁt) per dollar of spending on each marketing activity.

· Analyzes geographic, media, or brand-level budget allocations. Depending on the data

involved and the type of analysis applied, market mix modeling comes in three flavors (see Figure 2). A brand manager can build a marketing mix model comparing the impact of advertising, consumer coupons, and trade promotion, while an advertising manager can create a media mix model of the effects of TV, radio, Internet, and print ads. For the CMO, who must decide how much of the total marketing budget to allocate to each of the company’s brands, a portfolio model can show how to maximize the ROI and can also reveal the halo effect, where advertising for one brand may also drive sales for a related brand.

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Figure 1 Market Mix Models Correlate Marketing Activities To Sales Market mix models . . . . . . correlate media spending . . .

. . . with sales results . . .

Media ďŹ&#x201A;ow-chart

Sales report

Point of purchase

$80

Online

$60

Newspaper

$40

TV (response)

$20

TV (brand)

Data

Market mix model

Data

. . . to calculate the revenue contribution of each marketing stimulus.

$80

Point of purchase Online Newspaper

Television (response) Television (brand) All other

$60

Total

Revenue $40

$20

Time Source: Forrester Research, Inc.

ÂŠ 2005, Forrester Research, Inc. Reproduction Prohibited

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Figure 2 Market Mix Models Address Diﬀerent Business Issues Portfolio model: Analyzes marketing investment across a number of brands or products. Question: How do marketing activities for one brand aﬀect sales for another brand or sub-brand in a company’s portfolio?

Approach: Simultaneously examine the sales response of multiple brands to identify cannibalization or increases in revenue.

Example: A portfolio model showed a soft drink marketer that branding ads increased sales for both regular and diet colas. But diet cola ads did not aﬀect sales for the regular product. The brand allocated budget away from the diet product to increase brand advertising.

Marketing mix model: Determines the relative impact of diﬀerent marketing elements. Question: How should a brand allocate budget among diﬀerent marketing disciplines?

Approach: Examine sales response due to advertising, direct marketing, promotion, etc.

Example: General Mills learned that consumers who bought products during a trade promotion were less likely to buy the product again, were more likely to buy it only at a discount, and bought less of the product.

Media mix model: Determines the relative impact of different media vehicles. Question: How do different media and daypart allocations affect sales?

Approach: Examine sales response due to diﬀerent media (TV, radio, print, online, etc.), diﬀerent levels of spending, or timing of ads (especially TV and radio).

Example: A major food manufacturer learned that print ads had greater impact than incremental TV spending. The brand increased its newspaper and magazine spending by 15%.

Source: Forrester Research, Inc.

Models Require Clarity And Commitment Given the increasing clamor for marketing accountability and the success of market mix models in the CPG industry, it is tempting to view this approach as the answer to all marketing investment questions. Although models are powerful tools, marketers evaluating this technique should understand four limitations. Market mix models:

· Reﬂect history. Statisticians need at least two, and preferably three, years of weekly marketing spending and sales data to build a model. This reliance on the past means that the model can’t project the impact of a medium that hasn’t yet been in the mix and can’t tell if spending has been too low to register an impact. This is common today in online advertising. Also, the model’s accuracy quickly declines when trying to compare the eﬀect of a budget shift of more than 15% with historical trends.5

· Start with a business goal. Market mix modeling is an umbrella term encompassing a range of

techniques that can answer a variety of business questions. Marketers must clearly articulate the

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marketing decisions they are trying to support and the hypotheses they want to test to enable the modeler to bring the right data and analytic techniques to bear. But relating the model to important business issues helps to win approval for the project.

· Require a signiﬁcant commitment. Marketers can expect to spend at least $50,000 to $100,000

to hire a marketing modeling expert, as well as additional costs if they purchase external data. As models become a routine part of marketing, companies will also need to put eﬀort into integration projects to automatically pull data that is buried in the data warehouse or internal ﬁnance and CRM systems. Incorporating usage of the models throughout the marketing process requires further change management.

· Aren’t right for all industries. Market mix models are most appropriate for brands that have

large budgets, a highly diverse marketing mix, and distribution via indirect channels. B2B companies whose marketing objective is to drive leads and spend little on corporate branding are better served by improving their lead management systems.6 Several vendors report that models usually result in a 10% gain in marketing efficiency; thus, small-budget brands, where these savings won’t offset the model costs, shouldn’t use the technique. Similarly, catalog retailers with little spending outside of direct mail won’t derive as much benefit. Products like movies, where each release is a new product launch, won’t have the historical data to build the model.

THE MARKET MIX MODELING VOYAGE Given the effort and expense involved, companies should carefully plan and execute a new modeling initiative. As one retailer noted, “You have to approach a model like a real IT project.” With a decade and a half of history behind this technique, marketers can follow industry best practices but should be ready to tailor the approach to their specific industry, business problems, and data availability. These projects evolve in three phases (see Figure 3): 1. Plan: Scoping the project and selecting a modeling partner. 2. Prepare: Refining the scope and gathering required data. 3. Build: Creating and refining the model equation, implementing model findings, comparing them with actual results, and enhancing the model’s predictive ability. Phase 1: Plan The Project And Pick The Right Vendor While companies with econometric modeling staff may be tempted to go it alone, nuances of marketing modeling require the expertise of a specialist. In addition to marketing knowledge, modeling partners should have the flexibility to adapt their approach, the ability to clearly explain the model’s inner workings, and the willingness to help the company access and use the model on a routine basis. Companies should evaluate a modeler’s:

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Figure 3 The Marketing Mix Model Voyage Steps

Phase

Key tasks

Stakeholders involved

Keys to success

• Select the brand or business unit to lead the project • Select project leader • Deﬁne business issues to address

• Business owner • Project leader • Market research • Finance • Marketing • Sales • IT

• Get executive support • Generate awareness and interest among stakeholders

• Develop vendor selection criteria • Identify qualiﬁed vendors • Conduct RFI, RFP

• Business owner • Project leader • Market research • Marketing

• Pick vendor that can translate model ﬁndings into easily leveraged recommendations • Build conﬁdence of stakeholders in vendor’s approach

• Develop hypotheses to test • Choose model focus, methodology, geographic granularity • Determine variables to include • Identify potential data needs

• Senior executives • Business owner • Project leader • Market research • Finance • Marketing • Sales • IT

• Ensure that all stakeholders have input • Build consensus around scope and methodology • Capture all relevant variables

• Identify data sources • Aggregate data from internal and external sources • Clean, adjust, and prepare data for modeling

• Project leader • IT • Ad agencies and other marketing service providers • External data providers

• Gain cooperation of internal groups to access data • Automate data extraction • Evaluate if data quality is adequate to build the model

Build model

• Review preliminary model ﬁndings • Discuss and clarify ﬁndings that seem contrary to logic • Identify any additional variables or data that should be modeled

• Business owner • Project leader • Market research • Finance • Marketing • Sales

• Insist that vendor clearly explain details of model • Identify and eliminate noncausal correlations • Plan two to three iterations

Test and reﬁne

• Implement model recommendations • Monitor actual results against model projection • Create in-market tests that provide data to improve and expand model

• Business owner • Project leader • Market research • Marketing • Sales • Model vendor

• Validate model with actual marketing results • Promote ﬁndings internally to gain support for further modeling projects

Scope project P L A N

Select vendor

Reﬁne scope P R E P A R E Gather data

B U I L D

Source: Forrester Research, Inc.

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· Skills beyond statistical expertise. Banks, auto companies, and some retailers already do

statistical modeling of the economy to shape business planning. While marketing modeling uses many of the same statistical techniques, the similarity ends there. The greatest diﬀerence is in adjusting marketing spending to account for the lingering eﬀect that an ad campaign has, even after it is over. Transforming marketing spending data into “ad stock” requires knowledge that an econometrician doesn’t have of the decay rate of the impact of an ad campaign.7 Modelers also need to understand common media concepts like gross rating points (GRP) and circulation and must be accustomed to incorporating these disparate data types into a model.

· Category understanding. Models for ﬁnancial services, retail, and pharmaceutical companies

will encounter diﬀerent issues with distribution channels, marketing mix, and data availability. But just because modelers don’t have relevant industry experience isn’t a reason to dismiss them. To create its shortlist of potential marketing partners, Wachovia issued a request for information (RFI) describing the business problem and asking vendors to discuss the approach they would take. The responses allowed Wachovia to determine which vendors had the greatest ﬂexibility and creativity to adapt their experience to the bank’s needs.

· Business model. Some modelers regard the model as their intellectual property and only give the client reports of results. But to get the most out of the modeling investment, marketers should make it clear that over time, they will need increasing access to the model to update it and use it on a routine basis for planning, strategy, and ongoing evaluation of marketing eﬀectiveness.

· Integration skills. Forrester has heard repeatedly that one of the hardest parts of the model

building process is solving the technical and organizational hurdles to accessing necessary data from internal systems and databases. As part of the project framework, companies should plan to build automated data pulls to ease subsequent model updates. Modelers should be able to provide examples of similar projects for other clients and demonstrate the technical skills to deﬁne and direct the work.

Phase 2: Prepare The Data And Organization With a modeler onboard, the next task is to carefully plan the project, identify data sources, and engage other departments or stakeholders that will be aﬀected by the project and the model’s recommendations. This preparation is critical to success, and marketers should guide the project through a series of meetings to build a consensus around the desired outcome from the model and gain support for their continued participation.

· Reﬁne the scope. Using a model to determine marketing ROI sounds deceptively simple, but

before modeling can begin, the company needs to move from the high-level project objectives to a more detailed deﬁnition of the model: which brands will be involved, what the metric of success will be, what type of modeling techniques are best, etc. These decisions can dramatically aﬀect the time, cost, and resources required for the model. One retailer we spoke with spent the

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first 60 days working with the vendor and internal groups to clarify such issues as whether to use increased sales or profits as the metric and whether to look at short-, medium-, or long-term effects.

· Gather the data. The type of model and the metric will begin to shape the data requirements.

Planning should begin to identify which internal and external data sources will need to be tapped. It is not unusual to begin with 20 or more data sources, each of which may require multiple rounds to prepare the data correctly. Companies should staﬀ adequately for this task. One ﬁnancial services company we spoke with had assigned two employees full-time for two months to seek out, extract, and prepare necessary data from internal systems.

Phase 3: Build, Validate, And Enhance The Model Building a model is a lot of science, but it is also partly an art, requiring several rounds of evaluation and reﬁnement before accepting a ﬁnished product. Even so, skeptics are likely to reject the model until a campaign based on its recommendations runs in the market and its projections come true. Once a model proves accurate, marketers should work to expand its power and ability to guide business decisions. The following steps should be taken:

· Iterate the model. Initial drafts of the model should be scrutinized for two factors. First, is

the “R-squared” (the closeness of the correlation between a variable and sales) high enough? If not, the model may need added variables, more detailed data, or, in extreme cases, it may be abandoned. Second, decide if the correlations identified by the model are coincidental or causal. Stakeholders from areas such as finance, sales, and operations should continue their involvement through these stages to help marketing identify spurious correlations. Beyond improving the better model, this will minimize the potential for groups that don’t like the model, on the grounds that a “black box” formula doesn’t accurately reflect the dynamics of the business.

· Validate the forecasts. Models typically identify signiﬁcant changes in the media used or the

budget allocated to diﬀerent campaign elements. None of the companies we spoke with automatically shifted their plans to exactly mirror the model’s recommendations. Instead, they said they would make a less drastic, but still measurable, change in their plans, run a campaign, and match up marketplace results to the model’s forecasts. Only when real results prove the model’s predictive accuracy do companies begin to use the model to drive strategic marketing decisions.

· Construct tests to expand the model’s power. Because models emerge from historical data,

they highlight what worked best among past campaigns. But new media forms, such as product placements, blogs, or, in many cases, online advertising, may not have accumulated enough data for the model to reflect them. To expand the model’s sensitivity, companies should construct a number of different marketing plans in sets of test markets, track the results, and then use the model to add new variables and adjust the coefficient of previously known variables.

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CHOOSING THE RIGHT MARKET MIX MODEL PARTNER In the early days, the modeling industry was made up of a handful of small, independent boutiques. But major agency holding companies, technology consultants, and software companies see business opportunities in marketers’ measurement pain (see Figure 4, see Figure 5, and see Figure 6). Not only does this broader marketplace give marketers more choices, but it also gives them the ability to assemble their own best-of-breed measurement systems by picking the top modeler for their needs, embedding that model in a technology platform, and then engaging the agency or a consultant to implement the changes needed to enhance their business.

· Specialist boutiques thrive. Since the early ’90s, the power of PCs and software has enabled

anyone with a strong background in econometrics and statistics to hang out a shingle as a marketing modeler. Early entrants like Marketing Analytics continue to attract blue-chip clients like Kraft Foods and Nestlé, while newer entrants like iKnowtion continue to spring up. A full-service approach and deep experience make them good choices for companies beginning the modeling learning curve. However, companies need to make sure that these ﬁrms oﬀer complete access to the model, in the event that they decide to bring it in-house or use it in conjunction with another application.

· Marketing services companies expand oﬀerings. Ad agencies like DDB Worldwide

Communications and database marketers like Targetbase have traditionally served clients by creating and implementing marketing campaigns. As clients have begun to demand better measurement of these eﬀorts, these ﬁrms have added modeling expertise to their lists of services. Because they are marketing companies at heart, these types of vendors are often in the best position to enhance the model with deeper media or consumer data. Make sure there is a strong separation of responsibility between the implementation and the measurement sides of the business to avoid biasing the analysis.

· Software companies oﬀer a measurement platform. The combination of data storage

needs, analytic tools, and processing capacity that drives modeling makes it inevitable that a company like Veridiem would enter the ﬁeld with a platform application. As companies embrace modeling as a core aspect of their marketing planning, a platform like Veridiem’s gives marketers greater access to the model to conduct what-if analyses of diﬀerent marketing plan options, while simultaneously grabbing new marketing data to enable more timely model updates. But a platform alone won’t improve marketing; companies must substantially change marketing processes to get value out of this approach.

· Hybrids combine modeling, technology, and consulting. As the ecosystem of tools and services

becomes more complex, vendors have tried to simplify the planning and management of models with more comprehensive solutions. Marketing Management Analytics (MMA), one of the original boutique modelers, launched its Avista ASP service in March 2005, in response to clients’ requests for better access, faster updates, and better tools to get value from the models. Accenture provides the full range of strategic consulting, technology building, and change management services. Firms migrating to this class of vendor should make sure that they are not restricted to using only this ﬁrm’s models but can import and continue to use their existing models.

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Figure 4 Market Mix Model Vendors Oﬀer A Range Of Services — Part 1 Mix model types

Accenture Marketing Sciences The marketing Portfolio Marketing Media analytics Yes Yes Yes division of Oﬀering: Accenture, Users have direct access to with 80 model informed results through employees. dashboards and a suite of tools.

Industry experience

CPG

Retail

Finance

Auto

Pharma

Other

Diﬀerentiators: • Accenture provides patented tools for data preparation, model generation, simulation, optimization, and demand planning models, and it builds dashboards with dynamic reports. • Global consulting capability in CRM, strategy, and change management services. Representative clients: BT, Home Depot, Novartis, Sony, and Visa.

DDB Matrix A 12-person international consultancy owned by DDB Worldwide Communications Group.

Portfolio Marketing Media Yes Yes Yes Oﬀering: Explains the workings of the model, trains clients in its use, and/or will provide ongoing consulting and services.

CPG

Retail

Finance

Auto

Pharma

Other

Differentiator: DDB Matrix states that its approach is to make models understandable and accessible to clients. The staff are expert econometricians, and they also have strong knowledge of business issues, and present model findings in clear, easily leveraged terms.

Representative clients: Dell, Merck, Unilever, and Wells Fargo. iKnowtion A 25employee, independent boutique founded in 1999.

CPG Retail Finance Auto Pharma Other Portfolio Marketing Media Yes Yes Yes Oﬀering: Diﬀerentiator: iKnowtion’s approach is geared toward integrating mix Models provided in Microsoft Excel and SAS formats. Can work models into enterprise analytics that include demand with partners to deliver and customer objectives. customized scenario planning and simulation software. Representative clients: Did not disclose.

Information Resources, Inc. (IRI) Portfolio Marketing Media Division of a CPG Yes Yes Yes syndicated Oﬀering: data and Models results and recommenenterprise dations delivered via a dedicated software client services team that performs provider. ongoing simulations and support.

CPG

Retail

Finance

Auto

Pharma

Other

Diﬀerentiator: IRI’s database has store-level data not available to other modelers. IRI states that its models use Bayesian techniques that allow measurement of individual campaigns at lower levels of geography and smaller segments of consumers than other modeling techniques.

Representative clients: Birds Eye Foods, Campbell’s, Diageo, Kellogg’s, and Welch’s. extensive experience solid experience some experience no clients or limited experience Source: Forrester Research, Inc.

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Figure 5 Market Mix Model Vendors Oﬀer A Range Of Services — Part 2 Mix model types

Knowledge Networks Portfolio Marketing Media Founded in 1998, the Yes Yes Yes firm offers a broad range Offering: Excel simulation tools are exported of market and taught to clients on request. research Ongoing consulting and support services. is based on clients’ familiarity with models and needs.

Industry experience

CPG

Retail

Finance

Auto

Pharma

Other

Differentiators: Knowledge Networks states that its models reflect a more detailed level of consumer behavior. It offers the ability to combine standard modeling techniques with proprietary consumer data from its representative online panel, loyalty card data, and single-source media measurement survey. Representative clients: Did not disclose.

Marketing Analytics CPG Retail Finance Auto Pharma Other A 15-year-old, Portfolio Marketing Media 26-person Yes Yes Yes independent Offering: Differentiator: boutique. Provides a simulator tool to Marketing Analytics reports that it focuses on highly estimate profitability of different automated data collection, modeling, and reporting marketing plans or an optimisystems, enabling faster, more accurate analysis and zation tool for budget allocation. updates. Provides reports. Representative clients: Booz Allen Hamilton, Bristol-Myers Squibb, Kraft Foods, Miller Brewing, Millward Brown, and Sears. Marketing Management Analytics (MMA) CPG Retail Finance Auto Pharma Other Portfolio Marketing Media The 130person Yes Yes Yes industry Differentiator: Offering: pioneer was In April 2005, launched Avista DSS, MMA states that it has built models for more than 1,000 founded in brands worldwide, has developed proprietary techniques 1989. Owned a Web-based hosted service providing access to tools that improve accuracy and predictability, and, through by media Avista DSS, has made the resulting insight more accessible buying holding for what-if scenarios, marketing optimization, benchmarking, and and easily leveraged in marketing decisions. company portfolio management. Aegis PLC Representative clients: Did not disclose. since 1997.

extensive experience solid experience some experience no clients or limited experience Source: Forrester Research, Inc.

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Figure 6 Market Mix Model Vendors Oﬀer A Range Of Services — Part 3 Mix model types

Industry experience

Targetbase A 190person direct marketing agency founded in 1979 and owned by Omnicom.

Portfolio Marketing Media Yes Yes Yes Oﬀering: Clients have Web-based access to a dashboard and reports. A strategic services team provides support to interpret, use, and update models.

CPG

Retail

Finance

Auto

Pharma

Other

Diﬀerentiator: In addition to using syndicated, aggregate-level data, Targetbase frequently includes individual consumer-level data from client databases, third-party sources, or primary research to enable more sophisticated customer segmentations and media buying strategies.

Representative clients: GlaxoSmithKline, Honda, Procter & Gamble, PepsiCo, Sony, and Southwest Airlines.

Veridiem A 7-year-old, 40-person marketing performance management technology and services company.

Portfolio Marketing Media CPG Retail Finance Auto Pharma Other Yes Yes Yes Differentiator: Offering: Offers enterprise software to plan, Veridiem states that its focus is to make marketing track, and analyze marketing effectiveness part of the ongoing marketing process, results. The platform can incorpo- rather than an occasional project. It offers professional rate a model built by another services to link the platform to a company’s marketing vendor or Veridiem’s modeling data infrastructure. partner. Representative clients: Jaguar, Kraft Foods, Nissan, Procter & Gamble, and Subaru.

VNU Modeling & Analytics Portfolio Marketing Media Integrated into VNU’s Yes Yes Yes Advisory Offering: Services unit VNU’s DecisionSMART offering is in 2003. Its modeling skills a Web-based tool for clients to analyze the impact of different date back to marketing efforts on sales and to ACNielsen’s forecast the results of alternate work in the strategies. early ’90s.

CPG

Retail

Finance

Auto

Pharma

Other

Diﬀerentiator: VNU states that it employs data at the store, consumer segment, and media vehicle levels to build models that guide media allocations based on the most responsive consumer segments. The corporate relationship with ACNielsen, Nielsen Media Research, Spectra, and others eases integration of these data sources.

Representative clients: Did not disclose.

extensive experience solid experience some experience no clients or limited experience Source: Forrester Research, Inc.

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W H AT I T M E A N S

MODELS WILL SUBSUME THE MARKETING PROCESS For the CEO, measurement is as much about predictability for Wall Street as it is about improving the eﬃciency of the marketing budget. New types of models and new technologies that make them available to every marketing manager will ensure that companies rely more and more on their models to shape marketing plans that even an investor could love.

· Models will evolve from results analysis to planning tools. Marketing models have become central to marketing and advertising decisions at leading consumer goods companies including Kraft, General Mills, and Coca-Cola. As these companies have gained experience over time, they have begun to run what-if scenarios at diﬀerent budget levels and media allocations to support annual marketing plan development. Today’s Microsoft Excel simulator tools will yield to more sophisticated systems to track, collect, and model marketing in real time.

· Data will move from aggregate sales to individual consumer behavior. In CPG, the most important data comes from ACNielsen or IRI syndicated sales databases. As Left Brain Marketing expands the number of companies with individual consumer data enriched with behavioral insight like site visits and marketing preferences, market mix models will incorporate more granular data. Traditional database hosting ﬁrms like Epsilon and Axciom will follow Targetbase’s lead, adding statisticians to build consumer-segment models, drawing on individual transaction-level data that the ﬁrms hold for their clients.

· Marketing operations will migrate toward finance. As models enable more predictable results from marketing, CFOs will see the modeling process as an extension of the financial controls that they wield in other parts of the organization. To prevent a total takeover of marketing by the finance department, CMOs will instill greater discipline in executing and tracking marketing program results. Creative marketers who chafe under models’ restrictions will redefine their jobs away from managing ad campaigns to new product development, positioning, and communication strategies.

· Market research will become less quantitative. As ﬁnance exerts greater control in day-to-day execution, CMOs will bolster marketing’s inﬂuence by rebuilding marketing research’s capabilities. Their new mission: Find the unique consumer insights that can drive innovative new products. Market research will delve more into “soft” research techniques like anthropological studies and monitoring online consumer-generated media to divine consumers’ latent wants, needs, and motivations.

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SUPPLEMENTAL MATERIAL Companies Interviewed For This Document Accenture

Marketing Management Analytics

ACNielsen

McCormick

The Advertising Research Foundation

OMD

Agilent

Pﬁzer

BMW

Sears

Combe

Sequent Partners

DDB Worldwide Communications

Staples

Dratﬁeld Analytics

Sun Microsystems

Ernst & Young

Targetbase

Ford Motor Company

Veridiem

Georgia-Paciﬁc

Verizon

iKnowtion

VNU

Information Resources Inc.

Wachovia

JPMorgan Chase

Warner Bros. Entertainment

Knowledge Networks

Yahoo!

Marketing Analytics

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ENDNOTES 1

More than 40% of marketers expect to rely more on market mix models or lifetime value models in the coming years — twice the level of current tools like response analysis reports or attitude and usage studies. See the January 13, 2005, Trends “Where Is Marketing Measurement Headed?”

Three technology trends — media fragmentation, addressability, and interactivity — are converging on the world of marketing and advertising. In the new era of Left Brain Marketing, analytic skills grounded in deep audience knowledge will rise to predominance. Creative will remain important but will play a smaller, more sophisticated role. Marketing planning must also adopt more analytical techniques that allocate budget based on impact and not on cost per reach. See the April 6, 2004, Forrester Big Idea “Left Brain Marketing” and see the May 16, 2005, Forrester Big Idea “Left Brain Marketing Planning.”

Direct response techniques can connect a marketing impression to a lead or ﬁnal purchase. However, this type of closed-loop tracking — via Web, phone, or mail — is no longer enough. Advances in computers, databases, and the Internet have brought technological power to marketing to correlate a vast array of marketing stimuli to results. See the September 16, 2002, Report “Mastering Marketing Measurement.”

In the joint survey with the Association of National Advertisers, marketers were given a list of 13 possible metrics to assess their marketing eﬀectiveness and were asked to select those that they currently use. On average, respondents picked six, with “incremental sales generated by marketing” being the most commonly picked. See the January 13, 2005, Trends “Where Is Marketing Measurement Headed?”

Regression models rely on matching observed sales impacts with historical spending patterns, assuming a straight line relationship between the two. Because the relationship is almost certainly not linear, a shift of greater than 15% in spending moves the point of diminishing returns outside of the region that can be accurately predicted and may result in the model projecting higher returns than will actually be delivered.

More than 60% of marketers believe that technology can help them to develop more high-quality leads. But many of the marketers that we interviewed are frustrated by the gap that exists between their marketing and sales processes. Vendors like Aprimo, Eloqua, Pivotal, and Unica oﬀer marketers the ability to execute campaigns and nurture leads, but they require integration with salesforce automation systems to manage the handoﬀ and close the measurement loop. See the April 8, 2005, Trends “What B2B Marketers Need From Technology.”

Advertising research has shown that consumers’ knowledge of an advertising message starts low, builds up throughout a campaign, and then lasts for several weeks after the ad has ceased to run. If a model only attributes sales to ads during the time the ad is running, the model will misrepresent advertising’s true impact. “Ad stock” is the total eﬀect of advertising during the building of message awareness and the decay of consumers’ memory, and it requires an equation within the model to account for this eﬀect.

August 25, 2005

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