What’s the relationship between profit and customer experience in price optimization?

 In Customer Acquisition and Retention, Customer Experience

Best offer, Price OptimizationWhile price optimization is a powerful tool, it can’t do everything, at least not yet. Enhancements are opening new doors. For example, until now, banks have only been able to set pricing models in batch for large customer segments. Some insurance companies have adopted daily model updates based on changing market data and pricing strategies. Now, the ability to perform realtime optimization for a single consumer is emerging and banks should be taking advantage of it. Each customer can receive a personalized offer based on their needs and elasticity curve. This can have dramatic positive impacts for both profitability and customer experience.

First, an important clarification. In optimization, we take a different view of pricing. Traditionally, we would ask what we must charge to make a profit on a product. In price optimization, we ask what the consumer would be willing to pay. This fundamental difference opens the door to solving some of the problems banks currently face. Challenges with deposit account and debit card pricing can be addressed more elegantly on an individual basis than through bulk pricing.

One challenge that can arise when institutions begin using price optimization is called the loyalty penalty. Since loyal customers are willing to pay more for your products, it is common for a simplistic model to charge them higher rates than new customers. This can quickly lead to satisfaction issues. While the customers may not close their account, they are likely to find the energy to share their frustration with their friends. Word of mouth marketing that is negative will not bring new customers in the door and is the last thing banks need as they try to rebuild damaged reputations.

The loyalty penalty can be addressed by adding constraints to the model. If the model considers the cost of acquiring new customers and the lower servicing cost of an established customer as constraints, the pricing for loyal customers can be held in line and the penalty eliminated. This provides a profitable outcome while still protecting the customer experience.

That brings us to the customer experience question. Is it really possible to maximize profitability (increase prices) while protecting the customer experience? Interestingly, it is actually easier than with traditional pricing models. A standard pricing model will please the individual obsessed with making everything equal, but will do little for consumers seeking a low price or looking for quality over price. By providing each consumer with the price targeted to their interests, needs, and price elasticity, nearly every consumer can be more satisfied with their purchase.

There is one more constraint that comes into play: Value. Somewhere between consumers and regulators the question of value is bound to come up. But this issue, like many others can be addressed in the model through constraints. Adding sufficient features to products (or stripping them away) to ensure that the value is consistent with the price paid can do wonders. As an example, I’m rather frugal and have never paid fees for credit cards or checking accounts. Until one day when a credit card company offered value that seemed worthwhile to me. I now pay a very high annual fee for my credit card and gladly tell others what a great deal it is. I’m sure the company is still coming out ahead, but they made me let go of my money by offering something I cared about.

Using marketing data to understand consumers, behavior data to understand their interests, and pricing data to understand their sensitivity, allows us to achieve a clear image of the product/price mix the consumer is looking for. In many cases this will be more profitable for the bank than giving them an average price. In most cases, the consumer will select a product they prefer. While we may have exceptions, we can achieve better profitability and better customer service at the same time. The key is realtime optimization.


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