Ensuring Compliant Offers (without eliminating agent judgment)

Let’s start by talking about when an offer is an appropriate thing to be making. Typically, it is either when the customer is willing to pay but not able to or when the customer’s circumstances have changed. The next question is, when is an offer a problem? Generally, it’s when there are two identical customers (statistically speaking) who get different offers, or when the customer’s current circumstances haven’t been taken into account, or finally when the rationale for an offer can’t be described or replicated.

Today’s regulators are asking us to demonstrate comprehensive compliance management and demanding transparency, specifically when it comes to offers. They are requiring proof of fair and consistent treatment, that is reflective of the customer’s current situation, and is repeatable.

This means, we need to determine what data about a customer’s current situation would allow us to take the assessment (done in batch) and re-assess in real time. Then we’d like to model the appropriate program offer sets in terms of what we believe the outcomes will be and make sure we’re not inadvertently creating any disparate impact. Next, we’d like to enable ourselves to capture customer input, re-strategize that individual input, and have the system generate an offer set that is appropriate by account, product, or customer, etc. and also is appropriately reflecting our economics. At the same time we have to be able to capture evidenced that is auditable and provable that the above took place.

So, is there room for Self-Service Offers? Here’s a list of questions to help you determine that.

• Can customer input be captured?-
• Can consistent rules be applied?
• Can offers be re-strategized?
• Can offers be fair to all customers?
• Can results be repeated?
• Can all of the above be proven?

Some say the proof is in the pudding, but actually, the proof is in the reporting. Previously this process was manual, fragmented, incomplete, and subject to interpretation. What’s at stake is that we’re trying to weigh the profitability and customer experience against our reputational risk.

So how can CMC help with this? With CMC’s FlexCollect, you have the ability to dynamically capture customer input, by setting up a series of tasks and task elements to ask the questions you want answered. The FlexCollect strategy engine will then execute upon that input and take into account past customer behavior, scores, and external data entered and will run it against a rule set that is fair and consistent. You control a decision tree or segmentation model that can run (in real time) the exact offers you want to make. It’s completely system controlled and managed and therefore easily reportable.

FlexCollect helps you systemically gather that information but when you execute using FlexCollect, that execution is exactly the same regardless of what channel your customer is interacting with. The same decision engine rules apply regardless of the channel and result in the exact same offer.

What you have is a completely auditable and provable execution.