Identifying cross-sell opportunities after M&A
Cross-selling can play an important role in customer expansion strategies after M&A. Positioning relevant additional products with existing customers is often easier than acquiring new ones. Especially for multiple companies that have diverse, complimentary product portfolios, executing a cross-sell strategy can be rewarding.
Creating insights to enable cross-selling is typically straightforward. A simple cross-sell matrix, where each row represents a customer and each column a product or product group, provides a clear picture. Here's an illustrative example:
You can use the matrix to identify ‘white spots’ for logical product combinations which can be targeted by your sales teams.
The challenge of multiple customer administrations
After M&A with cross-sell potential, you want to execute on this potential as soon as possible. But a challenge arises: how do you identify matching customers across different customer administrations? Without identifying customer account matches, creating a cross-sell matrix becomes impossible.
Typically, companies consider two options:
- Consolidate customer administrations into a single system.
- Keep the administrations separate and periodically conduct (manual) matching exercises to pinpoint cross-sell opportunities.
When possible, merging both administrations is the logical approach. A rich, clean customer dataset is the foundation for many key processes, including cross-selling. But this can be a tough project to execute, especially when done manually.
Also, from a business perspective it’s not always possible or preferred to consolidate into a single system (yet). Often this also means that cross-sell initiatives are put on hold. One-off manual matching exercises give insights that are static and stale, limiting their usefulness in ongoing proactive sales strategies.
Koalitix's solution for customer data management
At Koalitix, we've developed a solution to automatically identify matching customers across different administrations and/or systems. The solution identifies 'exact matches' or ‘related matches’ (logos), and automatically creates a mapping between relevant customer records.
Our AI driven matching algorithm ingests data from each administration (e.g. customer name, address, contact details, tax IDs, etc.) and automatically estimates the probability of a match. Details and matches can be reviewed and accepted through an intuitive interface and results can even be enriched and synced back to relevant systems if desired.
With this solution we ensure you have a clean combined customer dataset across all of your data sources that makes creating cross-sell insights easy.
Interested in learning more? Don't hesitate to get in touch!