One of our customers offers consumer financing through small and medium-sized “merchants”: online and storefront retailers. Their go-to-market (GTM) team is under pressure to rapidly increase revenue among existing customers. Their most effective strategy is having relationship managers work closely with merchants to develop growth programs.
Today, their relationship managers can cover 20-30% of accounts, with no budget to hire more team members. Thus, to maximize relationship managers’ time, it becomes important to prioritize the right accounts.
The GTM team had an account prioritization model in place, but it wasn’t performing well. It was built with inputs from D&B data, industry sources, and internal data about usage of the program. To address gaps in the prioritization model, the GTM team had a set of questions they wanted to answer:
- What is our current wallet share (percent of purchases) at the merchant? Focus on merchants where we have lower wallet share and thus more room to grow.
- Which merchants are in our sweet spot for transaction size? We see more success with merchants whose average transaction sizes are between $500 and $10,000.
- Which merchants have large enough revenue? Focus on merchants with $500,000+ in annual revenue.
- Which merchants are growing? Prioritize merchants that are growing in popularity.
The GTM team decided to pilot Enigma data to gauge how it might improve their prioritization model.
To help answer their questions and hone their focus, the team used Enrich, data that filled gaps in their account intelligence. For example, they were able to find “wallet share” by comparing their customer revenue figure with Enigma’s data on merchants’ total card revenue. They could see signals of merchant growth with revenue growth rates.
The GTM team retrained their customer prioritization model with the new Enigma data and continue to run the model once a month to identify priority accounts. To keep their data current, they send Enigma their customer list monthly to be enriched with our data attributes and feed it back into their prioritization model.
- Uncovered that ~20% of their customers weren’t prioritized correctly
- Determined prioritization changes could generate an estimated $3.5M in incremental revenue
- Saw 10X ROI with Enrich