Banks are seeking to optimize their customer acquisition efforts to meet their strategic goals. Whether the goal is to improve profitability, reduce risk exposure, outperform competitors, increase stock value for investors, or strengthen reputation, banks are looking to maximize their objectives through optimization. In the context of customer acquisition, optimization is most commonly associated with the product offer process. What product should a bank offer a consumer in order to incent the consumer to act in a way that is beneficial to the bank’s strategic goals? This seems like a simple question, but the answer actually requires quite a bit of whiteboard space.
In order to answer this question, we will start by analyzing the different strategies that banks might have for their customer acquisition efforts. We will then delve into the tactics that those strategies will dictate. How are banks going to optimize their product offers to drive their customer acquisition strategies? Based on the offer optimization tactics that banks use to meet their strategic goals, we will then explore the analytics and technology that can be used to enable those tactics.
What are bank’s strategic goals for customer acquisition? The temptation is to simply say “profitability” and leave it at that, but this would be an oversimplification. All financial institutions want to be profitable, but their strategy might not be focused exclusively on short-term profitability at the expense of other objectives that will lead to long-term profitability. Some of those objectives might be improving their customer service, reducing their risk exposure, or beating a competitor for the dominant share of a specific market. Before any offer optimization tactics can be implemented, banks need to know what their strategy is.
The second question that banks need to answer is how are they going to optimize their customer acquisition efforts to fulfill their customer acquisition strategies? What components of their product offers are they going to adjust in order to maximize their results? What metrics are they going to optimize for? With sophisticated offer management and cross-sell systems, banks can optimize their product offers in almost any way they want.
They can optimize every component of their product offers including: the product type, the interest rate, the terms, the fees, the rewards, additional bundled products, and other perks or promotions (introductory interest rates, etc.).
They can optimize their product offer towards any metric that they choose including profitability, acceptance rate, length on book, balance per customer, products per customer, likelihood of attrition, or likelihood of default.
The specifics of how banks optimize their product offers depend on the consumer behaviors they are trying to incent and the strategic goals they are trying to drive towards. Here are a couple of examples.
Bank A Example
Let’s say that Bank A’s strategy for increasing their long-term profitability is to increase the raw number of accounts in their portfolio. To achieve this goal, Bank A is going to optimize their product offers to maximize the acceptance rate of their product offers. The logic of using acceptance rate as the optimization metric is simple, the larger percentage of consumers that accept the bank’s product offers, the more accounts they are going to book. In order to maximize their offer acceptance rate, Bank A’s offer optimization tactics are going to focus on:
- Low interest rates
- Even lower introductory interest rates
- Conservative fees and penalties
Bank B Example
On the other hand, Bank B’s strategy for increasing their long-term profitability is to build deep, long-lasting relationships with only the most profitable consumers. In order to achieve this goal, Bank B is going to focus on maximizing the loyalty and wallet share of their most profitable potential customers. This bank does not care about the quantity of accounts so much as the quality of the accounts. In order to maximize the quality of the accounts they are booking, Bank B’s offer optimization tactics are going to focus on:
- More luxurious (and thus more expensive) product features
- Reward programs
- Bundled product offers
Assumptions and Constraints
In both examples, the hypothetical banks’ strategies are based on a set of assumptions. Bank A is assuming that the costs associated with originating and servicing a larger portfolio of diverse consumers will be outweighed by the benefits of building a broad portfolio of customers. Bank B is making the opposite assumption. They are assuming that the benefits of building a smaller number of very profitable, long-term customers will outweigh the opportunity costs of not building a wider base of customers. Obviously, the effectiveness of the banks’ strategies is dependent on the validity of their assumptions.
Each bank also has to operate within a set of constraints. Take Bank A’s example. They want to increase their acceptance rate by lowering their price. If they wanted to maximize their acceptance rate at the expense of all other considerations, they could lower their interest rate to 0%. This might generate an acceptance rate at or very near 100%, but the new portfolio of accounts wouldn’t be profitable. All banks have constraints (economic or legal) that limit the range of offer components that they can optimize. The trick is to find the optimal place between the two unprofitable extremes (100% acceptance rate or 100% interest rate) that achieves the FI’s strategic goals.
Once a bank has whiteboarded out their offer optimization strategy and tactics, they need to figure out how they are going to enable everything. There are a couple of important pieces that banks need to consider. The first piece is insight into the consumer. If Bank B wants to optimize its product offers to build strong relationships with only its most profitable customers, it first needs to be able to accurately predict which consumers will become profitable customers. This level of insight can be reached through a variety of different analytic models that are currently available in the market. A lifetime profitability model can help banks determine which consumers will be most likely to turn into profitable, long-term customers. An attrition risk model can help banks predict which customers will defect to a competitor if they don’t receive better service. A model that looks at the next most likely product to be accepted can give banks insight into which products a consumer would prefer to be cross-sold or offered as part of a product bundle. In order for offer optimization to be successful, banks need to know how to optimize each individual product offer to meet the individual needs of each of their customers. Analytic models can provide banks with the insight necessary to do just that.
The second piece that banks need to look at for implementing offer optimization is enabling technology. Having an offer optimization strategy is great, but if banks can’t enable that strategy consistently across all of their channels and lines of business then it won’t be nearly as effective as it could be. If Banks can’t easily incorporate a variety of data sources and analytic models into your decisioning process, then the optimization decisions that they make will not be as accurate or relevant to their customers as they would like. If banks can’t instantly prescreen customers and present firm, personalized product offers in realtime at the point of customer interaction then they will miss out on a lot of sales opportunities. Bottom line, if banks don’t have the right enabling technology, then all their strategies for offer optimization and cross-sell will only ever be ideas on a white board.