Maximizing Information From Your Data
One reason that B2B sales dynamics are so difficult to measure is that the multitudes of customer and product combinations coupled with the diversity of buyer-seller relationships produce relatively little data density. For example, say a company with 10,000 products and 5,000 customers executes one million sales transactions per year, both directly and through channels. There are 100 million combinations of product and customer and channel, which means that they have only sold 1% of all possible combinations, making segmentation, price response, and wallet-share measurement very difficult.
In order to deal with this challenging form of ‘big data,’ our data scientists have developed advanced data mining techniques to extract the maximum information available in such sparse transaction data. This information is normalized and aggregated in such a way that it can be reliably shared with other similar segments, providing insights across the widest possible realm of system dynamics.