Traditional marketing approaches to customer onboarding in the financial industry are based on a “one-size-fits-all” philosophy: credit card offers sent to every address in a zip code, advertisements placed on popular television shows, web banners stretched across multiple websites. Such an approach is known as shotgun marketing. Shotguns spray a vast number of bullets with the hope that at least some of them will hit the target.
The problem with this approach is that the end-target — the paying customer upon which all business depends — is not taken into account. If they happen to open the credit card offer, see the TV ad, or click on the website banner, and if that offer happens to match their current interests, needs, and financial standing, then you may have a conversion. In the meantime, a lot of marketing capital has been spent on a long chain of happenstance without any measurable expectation of return.
For this reason, shotgun approaches are increasingly getting the axe in favor of a rifle approach to marketing. Unlike shotguns, rifle shooting requires one to take careful aim and fire a single shot right at the bull’s eye. Such an approach, known variously as one-on-one, personal, or relationship marketing, treats customers as individuals, with the understanding that each person has their own specific need.
From Next Best Action to Probabilistic Revenue
Financial institutions have been moving away from shotgun marketing towards a more targeted approach. Today, about 90% of mortgage lenders make use of what is known as next-best-action marketing. In this approach, the next best action for the company — in the form of a product, service, or proposition — is determined by considering both customer need and which products generate the most revenue for the lender. This kind of marketing makes more efficient use of resources, providing highly targeted offers to customers, many of whom lack the time to compare products and shop around. And, by taking consumer need into account, lenders are able to generate increased customer loyalty.
While this strategy is far superior to the shotgun approaches of the past, now there is an even more effective method. High-grossing products are good in the abstract, but can the customer afford them? Does the customer want them? Are they even in the market for them?
Probabilistic revenue strategy can answer these questions with precision. Probabilistic revenue strategy is next-best-action marketing taken to the next level—enriched with the power of math, this strategy is more specific, more evolved, more accurate. The strategy relies on a three-variable strategy, adding response rate and conversion rate to next-best-action’s revenue number. The result? Marketing campaigns that are both better targeted and more profitable. Let’s take a closer look.
Probabilistic Revenue Strategy: The Variables
Determining customer need is the first step to using Probabilistic Revenue strategy: here, the key is to figure out which products a certain customer doesn’t yet have and focus on those. From there, the following variables are to be taken into account:
- Response rate – a probability score based on a consumer’s expected performance. The score is derived from available data, overall consumer information or, better yet, custom scores based on marketing campaigns, customer profiles, and the like. The response rate answers the question: what is the likelihood that the customer is in the market for these products?
- Conversion rate – having reduced the pool to those likely to respond to an offer, and by considering the marketing rate for various products, the conversion rate determines what percentage of those left will actually buy.
- Revenue number – a snapshot of the customer’s current loan situation. In the case of credit cards and personal loans, this number is the average revenue per sale. In the case of mortgage or auto loans, it is the revenue, by product, as a percentage of the consumer’s overall balance. For example: if a customer has a $300,000 mortgage and the lender’s cut is 4%, the revenue number for this customer would be $300,000 x 4%, or $12,000.
Making It All Work
Now let’s find the probabilistic revenue for a particular product. The way to do that is to multiply the variables above: response rate X conversion rate X revenue number. So let’s say a bank wants to determine the probabilistic revenue for a mortgage product for customer Smith. The bank has established a historical response rate of 2%, an estimated conversion rate of 4%, and customer Smith has a revenue number, determined above, of $12,000. For Mr. Smith’s mortgage, the bank’s estimated amount of revenue from marketing to that consumer would be:
2% x 4% x $12,000 = $9.60
This same process can be repeated for each of your products and for all of your candidates. Thus, you can easily determine which product ranks the highest per customer, and plan your marketing accordingly.
Financial marketing is evolving from product-centric to customer-centric paradigms, and next-best-action marketing is a step in that direction. Now, with the new generation of probabilistic revenue strategies, it is finally possible to mathematically determine the estimated revenue from marketing a particular product to a particular customer. This allows for individualized, targeted, and cost-effective marketing campaigns like never before: truly a rifle shot in the bull’s eye.