CLV (Customer Lifetime Modeling) for Retail (supermarket and grocery chain)

First challenge is identifying customer over visits. There's some entity analytics that can be done: you associate loyalty cards with creditcard/bank account numbers and so you can even identify when the same customer changes cards. Don't expect 100% identification here.   Divide your purchases in loose baskets vs identified customers and provide a separate treatment for both.  ‎For the former you can only provide margin details per basket.   

Revenue= easy, sum of purchases -returns. 

Costs = tricky. Top down approach. Get the details from finance and collaborate with finance on this. If they sell say fresh good and electronics, likely, finance has a p&l for them separately. If you find the costs on department level are 35% of the total revenue, you use that number for every product in that category.   Try to get as deep as possible. Likely you cant do product level.   Important distinction is own brand vs foreign brand, so you can have several factors you can account for. Likely you only have cost data for the main effects (own brand vs foreign brand and category A vs B rather‎ then for each of the 4 combination). Use the raking (iterative fitting) for it to get at the 4 combination level. (raking is used in reweighting surveys to make the research group having similar properties to the population). Available in SPSS.   

Now revenue - cost is margin. On product group level very interesting to ‎visualize. (revenue vs margin%, revenue vs costs, spot the outliers, do some segmentation, color the scatterplots with the segmentation and various other characteristics you have).  

Now roll up to customer level. Use 1+ year of purchases, provide numbers on a year basis.   Again, show that customer margin is not just‎ a matter of taking say 15% of the revenue, but differs for everyone. Segment customers on margin and per resulting segment profile the product groups they buy from.  

Next lifetime.  ‎Properly divide your available time line into parts and use the purchase data from say month 1 to 6 to predict the purchase amount for the next six months. Validate this model by back testing it on the data the year before. Depending on the structure in the data, the model can try to predict 1) are you returning (0/1) 2) what will be your revenue class, or 3) what will be your revenue.    You can try to do the same for margin in order to get a sense of customers that change their buying habits.  

Now you can future tell the customer value. You can properly try to account for net present value etc, but I believe the models will never give you enough resolution to justify going through that additional logic.  

Use the future revenue, costs, margin in relation with the current ones and segment to store level (store type, province, area characteristics etc).  

You can use the results as follows:

1) predict overall revenue and margin for next 6 months (interesting for finance in order to determine strategy, specially on store level)

2) spot customers who are going upward or downward (interesting for campaigning purposes).  

‎3) understand the effects of promotions of article categories in the light of those newly obtained kpi's (interesting for category managers).