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 / Summer 2007 / Issue 47(originally published by Booz & Company)


Services in Search of True Marketing ROI

Service industries -- including financial services, health care, utilities, and telecom -- have access to vast consumer data, but lack the tools that most consumer goods companies use to understand customer behavior.

How can a company with multiple marketing initiatives see a 500 percent return from one and a 25 percent loss from another and not know which is the winner?

In financial services, this problem is surprisingly common. Unlike other industries, such as consumer packaged goods (CPG), most of the financial-services (FS) industry has not devised the capabilities necessary to fully interpret the vast amount of data it collects. Thus, FS companies frequently cannot analyze the return on investment (ROI) of their marketing spend. The industry lays out more than $10 billion annually for marketing. Its ad spending alone is approximately $8.5 billion, putting it fourth among all industries (behind automotive, retail, and telecom/Internet), according to Advertising Age — and that means a lot of money is going to waste.

Financial services is not the only industry in this bind. Health care, utilities, telecommunications, and the airlines are also heavily invested in marketing and unsure of the payback. This, in turn, leaves company executives in the dark about two critical aspects of their business: the effectiveness of their promotional efforts and the performance of their marketing chiefs.

The paradox for all of these industries, but especially financial services, is that these companies have access to a huge amount of consumer data, but lack the tools that most consumer goods companies use to gain an in-depth understanding of consumer behavior —  coupons, point-of-sale data, discounting, and other types of trade expenditures. Further, CPG companies do not contend with certain complexities that affect the FS industry, such as interest rates, market psychology, or variations in customer profitability due to product mix.

It’s no surprise, then, that there’s no standard approach to market analysis for companies in FS and similarly hamstrung industries. They get their information in varying ways: Some amass enormous 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 different, equally simplistic forecasting models. Some still rely on “control” markets, a method most industries discarded long ago because it excludes certain markets from promotional initiatives in order to use them as a baseline, resulting in forgone sales opportunities or diminished brand presence in those markets.

The good news is that there are ways to conquer these deficits and boost marketing performance while reaping significant savings. We’ve found three methodologies that can help marketing executives answer the following questions: How much are we really spending on marketing, and across what types of initiatives? How much of a return are we getting across the entire spend, and where is it coming from? Is the marketing strategy aligned with the sales strategy, including the desired customer relationships? The particular methodologies — or combination of methodologies — that will work for any given company depend on the amount and kind of data available. The three approaches are:

  • Breakeven Analyses. These can add quantitative rigor to certain types of marketing expenditures that lack true outcome data. Consider the case of a mortgage bank’s $2 million TV sponsorship, which allows the bank to air several commercials during a particular show. A breakeven analysis estimates the level of viewer penetration needed to justify the cost of advertising. The bank estimates that sponsorship can reach one-quarter of the 4 million households watching the show. Knowing that 2.5 percent of U.S. households are taking out mortgages during the year of the sponsorship, the bank projects that 25,000 of the viewers it will reach fall into the pool of potential customers. Given that the estimated lifetime value of a mortgage for this bank is $4,000, it will need 500 new customers from the pool of 25,000 to break even. The bank’s national market share is already 6 percent, so it could reasonably expect to “automatically” get 1,500 of these customers, even without advertising on the program. That means that the sponsorship will have to win the bank an 8 percent market share among the show’s viewers to be worthwhile — a breakeven target that the bank believes is achievable. It is therefore satisfied with this investment.
  • Impact Models. Many FS companies have already captured the data necessary to assess their return on marketing investments; they merely need to create appropriate impact models that regularly feed this information to the marketing team in a format they can use. This 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, and remove them 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) examine the marketing variables to estimate sales lift and use that figure to determine the incremental number of products sold that can be attributed directly to the marketing campaign; and (5) calculate the “lifetime value” of a single sale, then multiply the number of products sold by the lifetime value figure to determine actual ROI.
  • Consumer Funnels. If a company is already using its data to move customers through the successive stages of a desired relationship, a consumer funnel can be a valuable way to examine the progression. Most FS companies place their target customers in one of six stages in their relationship with the bank: awareness, preference, intent, purchase, penetration, or retention. Examining the “conversion” factor from one stage to the next can reveal 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.
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