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Marketing Mix Modeling in Financial ServicesPOV Prepared by Ninah

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Whilst the use of Marketing Analytics to support Marketing Mix Investments is only around 19.8% for all industries (Source CMOSurvey.ORG, 2015) we know that amongst the main national retail banks it is 100%.

Perhaps this isn’t so surprising. Given the level of accountability demanded of banks following at first Sarbanes-Oxley and more recently Dodd Frank for accountancy standards and capital allocation support, looking to analytics to justify investments is a natural. And in any case, surely the ROI argument could not find a better vertical to resonate in; not only is it the language of finance, it is also the lifeblood for risk scoring and credit appraisal.

So with many banks (and insurance, brokerage and tax preparation firms) leveraging marketing mix the more interesting question is not the degree of penetration for Marketing Mix Modeling (MMM) but rather what is it that distinguishes a finance marketer in the what, how and why of its use.

Working this backwards we’ve seen the single greatest force propelling all banks to use MMM is a greater focus on managing customer value. This in turn has led to linking marketing with the drivers of customer value and that in turn has sped greater centralization of the marketing function – centralizing both geographically and across Lines of Business (LOBs). With this impetus MMM has been leveraged to enable a more effective center by providing it both with the optics into the business as well as some levers to pull in managing it.

So how can we understand the different levels of progression across finance companies? Broadly speaking we see a continuum of user-ship and enablement levels:

• On the production side from a Do it For Me to a Do it Yourself or fully outsourced, to fully internalized,

• On the decision making side from a simple annual channel budget with static Economic Profit or Shareholder Value Added to a live investment optimization across message and channel to drive dynamic Economic Profit and Shareholder Value Added.

Where any given client sits on these continuums typically depends on the level of adoption and use that has been established:

• Some may be using their analytics vendors to do both relatively simple modeling as well as very advanced, yet others have full internalized both channel focused MMM and the advanced modeling. Most typically of course there is a balance, with the more basic work being done internally with the analytics partner used for scale and for advanced developments.

• More complex analytics is typically performed where earlier “quick wins” have been established and credited to the modeling. Once finance departments look to the models to manage brand and promotional investments to drive greater Customer Lifetime value then the marketing department is seen as a key force in centralizing all banking activities

So what drives the level of adoption? In our opinion there are two fundamental drivers:

• Aligning analysis with the actual decision makers needs – build to fit.

• Deliver results with a narrative that is based on the companies own style and language vs. the analytics firms

In addition today another more passive but also more fundamental force is playing into adoption and that is the requirements emanating from model governance and the compliance needs of the Federal Reserve. This is the technical flipside to the organizational appetite above. Both these adoption drivers require a marketing analytics function that can be both highly technical and dexterous whilst also being highly tuned and consultative in their approach.

MMM: POV Prepared by Ninah

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Once a virtuous adoption cycle is established companies are then able to evolve the use of marketing analytics and MMM to more advanced use cases. More specifically in financial services we’ve seen a wide range of applications unique to the industry:

• Multiple Outcome measures: The value equation in financial services tends to be more complex with multiple business outcome metrics impacting value and ROI e.g. acquisition, usage, balances and churn. This dynamic creates a more complex set of dependent variables to fully understand marketing ROI.

• Segmented consumer dynamics: Financial services companies have information not just on buying behavior but who is buying and the tiers of products creating a need for a segmented understanding of marketing impact on business outcome across customer segments and product tiers/types.

• Understanding customer value: Product value used in marketing effectiveness evaluation is dependent on customer behavior and usage, therefore good customer lifetime valuation models at product and/or customer level e.g. Net Present Value SVA models are key to marketing effectiveness in financial services. Engagements will often include some component of building, modifying or deconstructing these models to understand marketing ROI.

• Integrating and balancing direct and mass marketing: Financial services marketers rely on a complex interplay between direct channels e.g. mail, email, search, social and mass channels e.g. display, TV, print. Understanding the contribution of all channels and the interaction effects between channels, particularly direct and mass channels is key in financial services.

• Complex distribution environment: Financial services are often distributed and serviced through multiple outlets e.g. web and branch with different marketing impact by distribution channel, and regional differences in distribution strength. This requires models to be built at a geographically segmented level across multiple channels.

• Balancing branding, acquisition and deepening: Financial services marketers often have to balance a complex set of messaging objectives ranging from brand building to deepening and acquisition. This creates a more complex taxonomy for organizing media and marketing data for marketing mix modeling.

• Broader set of macro drivers: Financial services and brokerage in particular is impacted by a broader set of macro metrics that a typical MMM model e.g. market index, Volatility Index (VIX), yield curve, options expiration dates, housing market, data release, fed actions, unemployment etc. Controlling correctly for these macro drivers is key to getting an accurate marketing estimate.

MMM: POV Prepared by Ninah

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Marketing Mix Modeling in Financial ServicesCase Studies

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Case Study: Building a Strong Case for “Brand Effects” in Insurance Advertising

Building a Strong Case for “Brand Effects” in Insurance Advertising

SituationThe highly competitive U.S. auto insurance marketplace poses a real challenge for marketers, where the path to growth involves building and maintaining brand equity, and demands for a more efficient marketing spend than the competition. With annual budgets for the top insurance firms often exceeding $500MM, building the case for marketing requires multi-year measurement and understanding marketing’s role in building a brand.

ChallengeThe client, a mid-size auto insurance firm, which specializes in direct-to-consumer personal car insurance, acknowledged that advertising does not pay off in the short-term given the high acquisition costs in addition to competitive pricing. They wanted to measure the long-term effects of advertising on brand equity and base sales that had been built over time. The idea was to optimize their media mix, and to ensure that any increased investment would be directed towards the most efficient messaging. It wasn’t enough to understand how each message performed individually; instead it was necessary to understand how they worked together in the short and long-term to drive baseline quotes completed. The client had a firm grasp of its short-term impact of advertising on quotes completed, which was measured by the marketing mix model developed internally by their analytics team. However, they did not have a clear understanding on how to quantify the influence of brand equity and deduce the long-term effect of advertising on baseline quotes completed. Therefore, it was crucial that the results provided insights on both the short and long-term, as well as the overall impact on quotes completed when both were taken into consideration.

GoalThe client looked to Ninah Consulting to help optimize their media mix balancing short-term sales with long-term brand equity. They set out to

understand how brand equity affected business outcome, how the long-term effect of advertising differs from the shorter term and what the optimal allocation is across messages to maximize returns. Essentially, they were building the business case for continued investment behind their brand.

Working with client stakeholders, we identified the following key business questions:

• Which brand equity metrics are the most important to us?

• What is the relationship between brand equity and baseline quotes completed?

• What is the long-term effect of advertising on brand equity, which ultimately leads to baseline quotes completed?

• What is the short and long-term combined effect of advertising on quotes completed?

• What is the optimal message mix (Brand vs. De-positioning vs. Savings)?

SolutionNinah Consulting designed an analytics framework to identify which of the 15 Brand Equity measures had the strongest relationship with baseline quotes. Once the right brand equity measure was determined, an econometric model was built to measure the impact of brand equity on baseline quotes completed while controlling for other baseline factors such as seasonality and macroeconomic factors. A dynamic linear model was also developed previously to compare its “moving base” against the client provided baseline, which was derived from their short-term marketing mix model, to build confidence in using their baseline as our target metric; the two were very similar and highly correlated with one another. Afterwards, another econometric model was built to measure the long-term effect of advertising on brand equity, accounting for any lagged effects,

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carry-over effects, and diminishing returns. Having both models in place, the relationship between advertising and baseline quotes completed by way of brand equity could be quantified. As a result, Ninah was able to optimize the message mix to balance short term sales growth with long term brand equity building.

ResultsNinah Consulting created a simulation tool based on the marketing mix models. The tool enabled the client to scenario plan different media allocations by message type; adjusting for the differences in response during the short and long-term, while capturing the overall effect on quotes completed.In addition, Ninah Consulting also provided a number of valuable insights for the client:

• Unaided Consideration is the most important brand equity metric that best captures changes in baseline sales.

• Brand equity had a contribution of over 50% to baseline quotes completed.

• Television & Video drove 70% of brand equity in the long term, which translated to 37% of baseline quotes completed and 19% of total quotes completed.

• Advertising has a long half-life or carry-over effect of 13 weeks and decays relatively slowly against brand equity.

• There is more room to grow brand equity, and in turn quotes completed, with Brand messaging than De-positioning or Savings; Brand messaging had a relatively linear relationship in driving brand equity, whereas De-positioning and Savings have moderate to strong degrees of diminishing returns.

Case Study: Building a Strong Case for “Brand Effects” in Insurance Advertising

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Case Study: Media Mix Optimization Delivers 17% Increase in ROI for Insurance Provider

Media Mix Optimization Delivers 17% Increase in ROI for Insurance Provider

SituationThe Irish insurance market is highly competitive, battling increasing degrees of consumer switching. Throughout the industry, media activity has risen in recent years, with year-over-year double-digit growth in advertising. That spend includes traditional media, though insurance providers were also directing more euros to digital.

ChallengeFacing increased pressure in a rapidly changing media and consumer engagement landscape, the client, insurance provider, wanted to ensure they were effectively allocating their media budget. They enlisted Ninah Consulting to help them better understand the overall media influence on customer acquisition and revenue. GoalThe primary objective was to understand the true contribution of media and the subsequent optimal media mix to drive acquisition and revenue. To that end, Ninah interviewed key stakeholders across the client organization and identified the following key questions:

• How does performance vary by media channel – TV, online, radio etc.?

• What is the optimum level of TV weights through the year?

• What is the effectiveness of different creative? Campaigns?

• To what extent does competitor media activity impact performance?

• What is the influence of other key factors such as underlying seasonality and the economy?

SolutionNinah established econometric models across key channels (traditional and digital, including mobile – we didn’t do mobile! There is no data on mobile in Ireland, so this is not a claim we can make, I’m afraid!) in order to understand key performance drivers, the influence of each media channel, and the cost efficiencies of each channel in driving acquisition. Utilizing sales conversion rates allied to revenues, Ninah was also able to derive ROI by media channel, which was then used as a basis for budget optimization. ResultsThe models revealed that the digital channels were the most efficient and increasingly important for driving customer acquisition and revenue. TV was still an important part of the mix, but other traditional/offline channels proved least efficient. As a result of the data and analysis provided by Ninah, the client optimized TV seasonally and re-allocated budget from less efficient offline channels to more efficient online channels such as Search. Ninah Consulting’s actionable results armed the client with a more effective media strategy with which to compete in the rapidly changing customer engagement landscape and, notably, a 17% improvement in ROI.

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For more information contact:

Sebastian ShapiroManaging Partner, Ninah

sebastian.shapiro@ninah.com1-212-820-3302

375 Hudson StreetNew York New York 10014United States