The same team of strategic consultants created a recommendation / lead generation algorithm that was intended for the customer service representatives to use to increase share of wallet by cross selling related products to appropriately identified target customers.
The challenge: The recommendation engine only consumed data from a single product system – but made product recommendations across all product systems
In our cross system analysis, many of the recommendations generated were for products that the target client may have already owned.
Analytics engines are only as good as the data they receive. With missing or inaccurate data both the recommendations and the underlying algorithms are highly suspect.
Once again, a reasonable idea prematurely executed cost the client a great deal of money without delivering useful results.
The road to a better customer experience lies through better customer data.