Our client is a global healthcare manufacturer. They were keen to support their sales team with validated insight into their best sales opportunities. To get there, they needed to reconcile their CRM records with external market data to figure out:
· Where potential customers were
· Which of those were existing customers vs. net new
· Which of these were most likely to buy from them (and which products would be most of interest)
The data sets were huge – so doing this manually wasn’t a good use of their sales teams’ time. But when they’d previously attempted to automate the process, the quality of the results hadn’t been good enough. That’s where we came in.
How We Helped Them
1. Cleaning the data. The key challenge was that merging two complex data sets created a lot of duplicate records. There was a fair degree of uncertainty in the data (e.g., a pharmacy that also had an urgent care facility would show as two different entries). We developed a scoring model that helped us achieve an excellent degree (97%) of accuracy.
2. Validating it. This validation process works similarly to how a critical human would approach it. We built a sequence of tests across three key dimensions: name (the same facility might operate under different names); geolocation (how close do different addresses need to be to be the same facility?); type of business (e.g., is it a drugstore or a hospital?).
3. Supplying Sales with reliable insight. Our predictive model leverages insight from past transactions and helps the team spot the best prospects – both in terms of their propensity to buy (based on existing data for similar customers), as well as geography (sales reps can now optimise their trips when they visit customer sites).
4. Visualising the best opportunities. We built a colour-coded dashboard that made this insight super accessible and easy to grasp – as well as personalised to each sales director’s region and remit.
A realistic idea of their addressable market. Duplicates in the data had been distorting our client’s view of their actual market (size and opportunity). Now they can forecast realistically and have validated new leads for their sales pipeline.
Better use of their team’s time. Sales directors can now focus on customer and prospect interactions, nurture, and in-person conversations (instead of manually reconciling data).
A focused sales approach. The predictive model helps them tailor the way they address each customer and prospect.
Better forecasting. For example, churn at customer and prospect level; it also helped them replace their speculative production forecast with a data-based one (and removed the need for manual reporting).
Better data quality. The number of non-matches that come up has allowed our client to spot data quality issues – i.e., an independent metric on the quality of their own and the external data sets.
Could you create more value with your data?
Talk to our team to find out how.