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This example shows how to interpret results for Profit and ROI calculations.
Suppose that you are running a mail order campaign. You will mail each customer a catalog. You want to mail catalogs to those customers who are likely to purchase things from the catalog.
Here is the input data from Profit and ROI Example:
Startup Cost = 1000. This is the total cost to start the campaign
Incremental Revenue = 10. This is estimated revenue that results from a sale or new customer
Budget = 10000. This is the total amount of money that you can spend.
Population = 2000. This is the total number of cases
Therefore each quantile contains 20 cases:
total population /number of quantiles = 2000/100 = 20
The cost to promote a sale in each quantile is (Incremental Cost * number of cases per quantile) = $5 * 20 = $100).
The cumulative costs per quantile are as follows:
Quantile 1 costs $1000 (startup cost) + $100 (cost to promote a sale in Quantile 1) = $1100.
Quantile 2 costs $1100 (cost of Quantile 1) + $100 (cost in Quantile 2).
Quantile 3 costs $1200.
If you calculate all of the intermediate values, the cumulative costs for Quantile 90 is 10000 and for Quantile 100 is $11000. The budget is $10000. If you look at the graph for Profit in Data Miner, you should see the budget line drawn in the profit chart on the 90th quantile.
In Profit and ROI Example we calculated profit is $600 and ROI is 80%. This means that if you mail catalogs to first 20 quantiles of population (400), the campaign will generate a profit of $600 (which has ROI of 80%).
If you randomly mail the catalogs to first 20 quantiles of customers, the profit is
Profit = -1 * Startup Cost + (Incremental Revenue * Targets Cumulative - Incremental Cost * (Targets Cumulative + Non Targets Cumulative)) * Population / Total Targets Profit = -1 * 1000 + (10 * 10 - 5 * (10 + 10)) * 2000 / 100 = -$1000
In other words, there is no profit.