My B2C company partners with multiple companies and sells their products on their behalf. Our specialization is distribution and sales across different US regions. Product focused companies outsource sales and distribution to us. The company receives a commission on final sales. Sales teams are regionally distributed and have a diverse portfolio of products to sell. Some are easy to sell. Others are difficult to sell. Price is not always a defining factor because of wide range of price-value propositions in our products.
My company asked me to come up with a good way to figure out how many products a sales team can sell in a week if they are focused only on one product. For example, sales team A currently sells 320 products a week (80 hair products, 200 beauty products, 30 electronic products and 10 luxury items).The company wants to understand how many products of only one type can a team sell. That is, the company wants me to tell that a team can be reliably expected to sell (xx hair products, yy beauty products, zz electronic products) if they sold products of only 1 type.
The data by team has a diverse mix of sales by week and I can't separate by more than team sales by week. The target variable of sales by team by week is not helpful because of wide range of products every week. How do I model this problem? Any help will be appreciated.
Things I tried:
- Using days to sell as a variable. But this is sometimes very dependent on delays and lags. For example, the sales team might work for 2 days and the sale might happen 12 days later. as a result, the count of sales is not in the same week as when the effort was made to make a sale. Maybe I am not thinking about this correctly
My data has a Poisson/Negative-Binomial distribution but I don't know if modeling a Poisson model is the best approach with strong product mix in each week's sales data