# How to analyze repeated measure data for prediction?

In my work, we collect sales data of our products. We have a set of 1st level customers (lets call that group as jacks) with whom we do we business. These jacks then sell our products to end customers (let's call that group as roses).

These sales data contain fields such as product,product type, market segment (like APAC, EMEA etc),qty requested (by jacks),order booked qty (by jacks),cost price (our selling price to jacks),revenue (our expected revenue based on qty requested and price) ,date of purchase order, sold price (jacks selling price to roses).

Please note that order requested variable is captured before itself (during initial negotiation) but just appended to this csv

Now, with this data, our business objective is to increase our revenue (which means the order booked qty by jacks (for them to sell it to roses) should be high).

However, in real time we see that there is a huge discrepancy between order requested and order booked quantities.

So, if we can predict order booked qty in advance and if it is found to be less, we can make sure that we don't over produce or follow up with the jacks to place more orders (or know reason why aren't they booking enough orders).

Should I do linear regression for this? But my data contain repeated measures. Meaning, a customer from jack group can appear multiple times. Meaning, once he would have sold product A where order requested and booked kind of match (less difference) but the same customer would have sold product B where the difference between order requested and order booked is really huge (affecting our revenue and unnecessary backlog in our inventory).

Can linear regression work for repeated measures?

As you can see each customer may or may not have multiple records. So, which algorithm would be better for this scenario?

If you think this problem has to be framed differently, do let me know please