A leading consumer goods firm was challenged by the sheer number of possible customers their field sales team could target in the retail space: independent grocery stores, convenience stores, corner shops, and petrol/gas stations. They needed to identify accounts to target and prioritize those more likely to convert to profitable customers. This would help their sales teams optimize their efforts as they visited customers and prospects.
Our customer connected their data to Explorium’s External Data Platform to create net new business leads to prioritize. Machine learning models were created that assessed the likelihood to convert and profitability of the businesses. They combined their internal data with external data signals such as:
The net new business leads and enhanced lead scoring models from Explorium’s External Data Platform led to improved efficiency for their sales teams, enabling more effective territory planning and targeting businesses more likely to convert. It resulted in stronger customer relationships and revived past relationships while reducing customer churn. The end result was a 43% growth in net new business and a 20% gain in existing and former customer accounts. Following these results, the same methodology and solution was applied across multiple markets globally, with similar results. Their sales teams also reported a marked increase in the quality of leads.