Credit products are often perceived as simple and easy to compare because price appears to be the dominant variable. Interest rates seem to determine which offer is better, and many providers optimize their funnels accordingly.
However, this view misses an important point. The price of a loan is rarely fixed upfront and often remains uncertain at the beginning of the journey. It depends on individual creditworthiness and only becomes concrete later in the process. As a result, the market optimizes around a variable that users do not actually know when making their initial decision.
Why interest rates are not a reliable decision variable
From a user perspective, comparing loans based on interest rates creates a false sense of certainty. Users see representative rates and assume that these will apply to them. In reality, many users end up with significantly different offers once their individual profile is assessed. This creates a gap between expectation and outcome that directly impacts user experience. Representative pricing makes offers more realistic, but not more personal.
The two thirds rule as a first step towards transparency
Regulation has already tried to address this issue. In many markets, lenders are required to display representative interest rates that apply to a majority of customers. This improves transparency and reduces misleading expectations. However, it still operates on aggregated data and does not answer the key question for an individual user.
The real problem in credit funnels
The most important question for users is not only what a loan costs. It is whether they will be approved at all. This gap between expected and actual outcomes introduces friction into the funnel. Users apply with unrealistic assumptions, receive rejections or worse terms, and experience frustration as a result. This leads to drop offs, lower trust, and inefficient processes for both users and providers.
Why many funnels optimize for the wrong variable
From a product and growth perspective, this points to a structural issue. Many funnels focus heavily on pricing signals and conversion optimization, while largely ignoring expectation management. A funnel that does not actively manage expectations will inevitably create friction. The consequence is a mismatch between user intent and actual outcomes, which reduces both efficiency and satisfaction.
Adding a second decision dimension
One way to address this problem is to introduce an additional decision dimension. Instead of focusing only on price, users can also be informed about the likelihood of approval. This shifts the decision from a one dimensional comparison to a more realistic assessment of available options.
An example of this approach is a loan calculator that includes an approval probability alongside interest rate and monthly payment. This probability can be derived from anonymized, statistical data based on real, completed loans. The goal is not to predict an exact outcome, but to provide users with a more realistic understanding early in the journey.
I shared some of these thoughts on LinkedIn.
From price comparison to expectation management
This approach fundamentally changes the role of the funnel. Instead of maximizing applications, the funnel starts guiding users towards realistic outcomes. The shift from price optimization to expectation management reflects a broader change in how credit journeys are designed. Over time, this can improve user experience, increase funnel efficiency, and enhance the overall quality of conversions.
Why expectation management will become a key lever
Expectation management is likely to become one of the most important levers in credit funnels. The key question is no longer which offer is cheapest. The key question is which offer is realistically accessible. It will be interesting to see how different players in the market approach this shift.
