Break Through the Barrier of Common Business Practices
The Consumer Goods Industry depends on Field Sales teams to support brands in retail stores and drive sales. Traditionally, the most common practice is to allocate time, manpower and resources to the outlets that are producing the largest amount of sales. EPoS data makes it easier to identify these stores as well as determine potential issues and the steps needed to correct them, but what happens if a channel doesn’t have EPoS data available, such as emerging and Traditional Trade markets?
20:20 RDI Predictive Analytics® uses a wide variety of data sources to estimate the likely sales value in any given outlet, despite the absence of sales data across the outlet universe.
These models use intelligent algorithms to identify, from a wide variety of store characteristics and variables, those that have the closest relationship to sales, building a store-level forecast that segments stores according to their estimated sales. By supplementing the Brand Owner’s view of a market’s store universe with the actual store universe, 20:20 RDI Predictive Analytics® can also be used to investigate stores the Brand Owner may be unaware of.
Armed with a reliable forecast of sales per outlet, Brand Owners can construct a contact strategy for their sales forces that closely mirrors the approach taken in markets with EPoS data.
20:20 Retail Data Insight helps organizations break through the barrier of established working practices with Predictive Analytics, enabling them to improve their ROI when their current results aren’t enough.