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Getting a Return on Financial-Services Marketing

The financial industry lags others in making the connection between marketing investments and returns. Three analytical tools can help financial-services companies develop the capability to see more clearly.

(originally published by Booz & Company)

How can a company with multiple marketing initiatives see a 500 percent return on one hand and a 25 percent loss on the other, and not be able to show which is the winner?

In financial services, this is a common problem. Unlike other industries, such as consumer packaged-goods (CPGs), most of the financial-services (FS) industry has not devised the capabilities necessary to fully interpret the range of data it collects. Thus, FS companies frequently cannot analyze the return on investment (ROI) on their marketing spend. According to Booz Allen Hamilton estimates, the industry lays out more than $10 billion annually for marketing; its ad spending alone is approximately $8.5 billion, putting it fourth, behind automotive, retail, and telecom/Internet — and that means a lot of money is going to waste.

The FS industry can boost its marketing effectiveness by 15 to 25 percent, resulting in significant bottom-line savings, by putting in place tools and processes that will measure marketing ROI more accurately than marketers’ intuition. Putting those elements in place requires marketers to start by answering a series of questions:

  • How much are we really spending on marketing, and across what types of initiatives?
  • Across the entire spend, how much of a return are we getting and where is it coming from?
  • Is the marketing strategy aligned with the sales strategy, including the desired customer relationships?

Gathering this information allows FS marketers to base strategic decisions on hard numbers rather than soft estimates. One bank, for instance, discovered that the vehicles it uses to acquire and retain customers yielded wildly different levels of effectiveness. It also found, to its surprise, that results it had previously attributed to marketing were actually more closely related to its number of branches per capita. Another company drew on its ROI findings to slash its annual broadcast television budget from $70 million to $10 million, and shift spending into cable television, online media, and sponsorship channels.

Digging Up the Information
The paradox of FS marketing is that the industry has access to more marketing data than nearly any other industry, due to its in-depth relationships with its customers, but it is much more difficult to capture, analyze, and act on this data than it is in other industries. FS companies face certain inherent structural constraints in financial services that other industries don’t. In CPG, for instance, coupons, point-of-sale data, and trade spend information — materials that FS doesn’t have at its disposal — paint a clear picture that marketers can use in analysis. Further, CPG companies do not contend with complexities such as interest-rate climates or variations in customer profitability due to product mix.

As a result of these constraints, the FS industry has little in the way of standard approaches to market analysis. The ways FS companies gather data vary broadly: Some gather massive quantities of raw data; others rely heavily on anecdotal evidence. Some marketing teams still measure performance armed with only year-over-year comparison methods, whereas others use equally simple forecasting models. Finally, the FS industry still relies on cumbersome “control” markets. This method has long been discredited in most industries because it excludes certain markets in order to use them as a baseline, resulting in forgone sales opportunities or diminished brand presence in those markets.

Building a Toolbox
Although these sector-specific conditions are constraints, they don’t have to be barriers. We have used three ROI methodologies at companies with various levels of data at their disposal; the amount of data available to a particular company will determine how best to employ some combination of these three strategies.

Breakeven Analyses. In cases in which data is difficult to obtain, breakeven analyses add quantitative rigor to certain types of marketing spend that lack true outcome data. Consider the case of a mortgage bank’s $2 million TV show sponsorship, which allows the bank to air several commercials and associate its name and logo with the show. A breakeven analysis estimates what sort of viewer penetration would be needed to justify the spend. The bank estimates that, of the estimated 4 million households that regularly watch the show, the sponsorship reaches one-quarter. From these 1 million households, the bank estimates that 25,000 are in the market for a mortgage, because 2.5 percent of U.S. households took out mortgages in that year.

For this bank, the estimated lifetime value of a mortgage is $4,000. Therefore, the bank would need 500 new customers from the pool of 25,000 to break even. This bank’s national market share is already 6 percent, so the bank could reasonably expect to “automatically” get 1,500 of these customers, and would therefore need to boost its share of this group to 8 percent. Given the strength of viewers’ loyalty to the TV show and the implied loyalty to its sponsors, the bank believes the breakeven target to be achievable and is satisfied with its investment in the sponsorship.

Impact Models. Where good outcome data exists, impact models reduce noise and isolate the linkage between marketing actions and business outcomes. Many FS companies have already captured the data necessary to understand their return on marketing investments; next, they should put tools in place that regularly feed this information to the marketing team in a format they can use. Doing so requires five steps:

  1. Articulate the campaign’s objectives and identify the variables that could affect outcomes.
  2. Determine which variables, such as seasonal or macroeconomic factors, are beyond the marketing team’s control so that these factors can be removed from the equation.
  3. Use simple regression-modeling tools to establish the relative weight of all variables, then develop a pilot formula to determine the base level of sales.
  4. Using the same model, examine the coefficients on marketing variables to estimate sales lift, as well as the precision of that estimate; use this opportunity to check the model for logical coherence.
  5. Use the sales lift percentage figure to determine the number of incremental products sold that can be attributed directly to the marketing campaign. In parallel, calculate the “lifetime value” of a single sale, using a formula that includes elements such as the annual discount rate, the average tenure of a customer with a particular product, and the pretax annual profit per product sold. The goal is to determine actual ROI by multiplying the number of products sold by the lifetime value figure, and then setting that figure against the marketing investment.

Consumer Funnels. In cases in which an FS company has a significant amount of data and is already using that data to move customers through the successive stages of a desired relationship, a consumer funnel can be a useful way to examine the progression and identify clogs.

Most FS companies identify their target customers as being at a particular stage in the relationship — awareness, preference, intent, purchase, penetration, or retention — with the number of target customers at each successive stage decreasing. Looking at the “conversion” factor between neighboring stages indicates the weak points. For example, strong conversions from awareness to preference to intent and then a significant drop-off before purchase could indicate a sales bottleneck. However, if the conversion drop-off occurs between awareness and preference, the problem may be a lack of trust or poor brand equity.

A company can also use this methodology as a budget-planning tool. It might, for example, decide to continue funding a customer-awareness campaign with a low short-term financial return, because it now understands more clearly how such initiatives drive long-term success in high-return stages of the relationship, such as customer retention.

Reaping the Results
Equipped with these quantitative capabilities, the FS chief marketing officer can engage more productively with the board, senior leadership, or the line in a common language that all stakeholders can understand. The organizational and cultural legacies within most large financial-services companies ensure that building these capabilities will not be a simple undertaking. Our direct experience has shown, however, that it will be an enormously effective one.

Author profiles:

Joni Bessler ( is a vice president with Booz Allen Hamilton in San Francisco. She specializes in strategy and operational effectiveness for financial-services companies.

Steven Treppo ( is a principal with Booz Allen Hamilton in Cleveland. His work is primarily conducted in the area of growth strategy development with consumer packaged-goods companies, with a focus on analytical marketing.

Ashok Notaney ( is a senior associate with Booz Allen Hamilton in San Francisco. He focuses on operations strategy for retail consumer and retail financial-services companies. 

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