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:
Articulate the campaign’s objectives and identify the variables that could affect outcomes.
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.
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.
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.
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.