Earlier in the year, we employed a consulting company to provide us "loyalty scores" four our customers. Just so you're aware, the loyalty score measures the percentage of grocery spend that a customer allocates to "ABC Grocery" vs. our competitors.
Unfortunately, the consulting company was only able to match around half of our customers to their loyalty database - so while we now have loyalty scores for those customers, we're missing them for the other half.
We hypothesise that this loyalty score could be related to some customer metrics that we have in our database - but we don't know for sure. If we could find a way to predict missing loyalty scores this would add huge value to our business as it would enable us to target all our customer with specific offers and discounts based on their loyalty to "ABC Grocery".
Build a model based on the customers that were able to be matched that will look to learn a relationships between loyalty score and some customer behavioral and transactional metrics in the data that we have access to.
If we can build a good model we can use it to predict a loyalty score for the remaining customers that the consultancy couldn't tag.
This will provide immense value to their business as it will enable them to target all their customers with specific offers and discounts based on their loyalty to "ABC Grocery".
