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Increase Your Portfolio Profitability By Outpricing the Competition

When it comes to outpricing the competition, what we often see in the financial services industry is a race to the bottom – in other words, a rush to the lowest price in order to get more customers. However, “outpricing” encompasses much more than that because, at the end of the day, profitability is needed.

Outpricing is comprised of 4 main concepts, and each piece builds on the last. When you are able to effectively outprice your competitors, it means you are getting to that (prospective) approved customer at the right price.

Data & Logic

The first piece of outpricing, which is probably the most difficult, is about having a deeper understanding your data. What data is available to you and what do you have access to? Where is the data located? Is it sitting in multiple silos within your organization? Are you using internal and external data? These are all questions you should be asking.

In most cases, the data you need is out there and available – but it isn’t standardized, which means it isn’t really usable. Five years ago, it’s likely that a particular data field represented something totally different than it does today. At first glance, it may seem like the data looks useful and accessible, but having a deeper awareness of that data is very important.

The first step is identifying what data you have access to, what data you need access to, and then how you’re going to access it. Once you gather the right information, it can be incorporated into an automated decision-making tool.

Analytic Maturity

The second piece of outpricing is the concept of analytic maturity. Analytics is a journey within itself that requires self-reflection. There are 5 different stages in the analytic journey, each with its own set of benefits, which usually begins with some basic judgmental decisioning.

  1. Profiling & Segmentation

A lot of organizations are in this stage where they’re establishing broad segments based on customer profile data. This means you’re segmenting the population into large groups and then making determinations based on that segmentation. This allows organizations to leverage their experience and knowledge in developing the segmentation.

  1. Predictive Models (or “Scores”)

The second stage is where you’re leveraging predictive models/scores. The type of model used is going to depend on the kinds of decisions you’re making. Keep in mind that most predictive models are rank ordering your prospects on a single dimension. For example, if you’re making a credit decision, you’re rank ordering based on the likelihood that an applicant is a good or bad risk. From an origination perspective, you’re going to integrate the scoring into your profiling and segmentation so that the two can go hand-in-hand. At this stage, you’ve now incorporated more analytics into your journey.

  1. Multiple Score Trade-Off

This level involves creating micro segments by matrixing 2 or 3 predictive models. This means you’re taking multiple scores and creating multiple matrices to evaluate prospective customers. It gives you the ability to analyze micro segments and is more powerful than using single scores because now you’re incorporating predictive models and multiple dimensions into your decision.

  1. Data Driven Decision Trees

This stage takes profiling and segmentation to another level where you’re creating many micro segments by combining policy, scores, and segmentations focused on one or more profit drivers. Instead of just doing broad-based customer profiling, it allows you to create smaller segments by looking at the customer profile, scores, and many other factors that may be specific to your organization.

  1. Decision Optimization

The next stage is optimization, which brings all the predictive analytics into a single decision framework and assigns the optimal action for each account/process.

It’s important to take an objective evaluation of where you are on this analytic journey. Your level will determine how effectively you’ll be able to maintain profitability while outpacing the competition.

The analytics journey continues to grow and expand as new tactics are continually being evaluated. Wherever you are on the journey, now it the time to work on making progress – your organization and your customers will start to benefit as soon as you begin.

Accurate Evaluation

The next piece of outpricing is accurate evaluation. Evaluating the data will help your organization identify what is predictive so it can be used to make good decisions. Making accurate evaluations happens when you have the right data, the right scores, the right profiling, and the right decision trees. Not only will this help you avoid risk, but it also helps bring in the customers that are going to be the most valuable (and the most profitable) to you – and you will be able to price them accordingly.

You need to be leveraging the data and analytics associated with pricing so you can offer the right prices to the right customers. You also want to be aware of any relationship history you may have with a customer to help provide an accurate evaluation, which goes hand-in-hand with accurate pricing. This may mean having the wisdom and foresight to realize that a particular customer will be profitable despite riskiness. In other words, in addition to pricing to risk, you want to price to relationship as well.

Risk Evaluation

The final part of outpricing, risk evaluation, puts all of the previous pieces together. In order to accurately evaluate risk, you need data, analytics, an understanding of how that person transacts, their payment history, how they use your products, etc. You have to consider the full relationship that you have with the customer, as well as internal policy rules and what impact those rules might have for offering certain products to customers (i.e. aggregated exposure).

Essentially, you need to know as much as you can about a prospective customer. You may have all the data, but the key is making it into something actionable. That’s the analytic side of the journey – understanding the data and its implications, and applying all the different policy rules and various risk segmentations in order to get to the right customer. Then, when you have the right customer, you need to make sure that the pricing is coordinated with all of the information that you know about that person.

Conclusion

Outpricing the competition means getting data that’s actionable, getting to the right customer, and ensuring that your pricing is aligned with that customer. All of these factors must be considered if you want to outprice your competitors and build a profitable portfolio.

About Author
Therese Henry is a senior director with FICO

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