# Improving retention and collection performance through intelligent repricing

> Industry Sector: **Financial Services**
>
> Business Function: **Operations**

A payments processor wanted to increase revenue from small and medium merchants by optimizing collections, repricing less sensitive merchants, and retaining high-value customers at risk of churn. With a fragmented data landscape of tremendous scale, they could not run the analyses necessary to act on these ideas.

## Challenge

At the scale that this processor operates, there were two primary challenges:

* No effective way to prioritize or rank merchants for collections
* Limited ability to determine the best rates and fees for a merchant

## Solution

* Complete view of merchant activity — The company developed an unprecedented understanding of their customer base with a large volume of integrated data on customer activity, pricing, payment terminals, billing, fraud, and credit history.

* Improved collections — Analysts develop statistical models to rate accounts by how likely they are to pay. High-likelihood accounts are routed to internal collections teams, while low-likelihood accounts are referred to third-party collectors.

* New pricing strategies — Analysts perform high-scale analysis to assess the impact of different fee structures on customer retention. Sales teams use these insights to better price new accounts and reprice existing accounts to prevent churn.

![Improving Retention and Collection Performance Through Intelligent Repricing](../../../assets/010b49be66ee4b390ae6dbfb3bbc8e66ee9157afeab59b642c3ddff2d11605c9.jpg)

### Stakeholders and user groups

* Merchant Collections Team
* Financial Analysts
* Data Scientists
* Sales Teams

A financial analyst uses Foundry to go through a re-pricing exercise for merchants to ensure retention but also maximize revenue.

## Impact

* Improved collection performance by filtering uncollectible accounts to 3rd parties and prioritizing highly collectible, high-value accounts. This generated millions of additional collections dollars per year projected due to the improved prioritization.
* The repricing model facilitated repricing exercises for merchants which increased retention and generated additional millions above targeted revenue.

## How it works

* Integrated the Customer Relationship Management (CRM) system and other datasources to create a single Foundry [Ontology](../../ontology/overview/index.md) to understand Merchant activity. Objects and relations were created for customers, their activity, pricing, payment terminals, billing, fraud, credit history, and more.
* [Contour](../../contour/overview/index.md) and other Foundry analysis tools used to perform analyses to determine new collections and pricing approaches. Scenario analysis of different pricing options is straight forward in [Contour](../../contour/overview/index.md) and [Code Workbook](../../code-workbook/overview/index.md). [Vertex](../../vertex/overview/index.md) and Foundry Scenarios could be applied to further enable this process.
* Machine Learning models for prioritizing collections and determining repricing were implemented in [Code Repositories](../../code-repositories/overview/index.md). [Foundry ML](../../model-integration/overview/index.md) could have been considered here as well.
* The repricing application was implemented in [Slate](../../slate/overview/index.md).

## Implement a similar use case

This use case implements the following Pattern. Follow the link below to read more about a particular Pattern and learn how it is implemented within Foundry.

* [Investigation and cohorting](../../use-case-patterns/investigation-and-cohorting/index.md) (used for 3 other use cases)

Want more information on this use case? Looking to implement something similar? [Get started with Palantir. ↗](https://www.palantir.com/contact/get-started/)