Profit and ROI Use Case

This use case depicts how to interpret results for profit and ROI calculations.

Suppose you run 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, then the cumulative costs for Quantile 90 is $10,000 and for Quantile 100 is $11,000. The budget is $10,000. If you look at the graph for profit in Oracle Data Miner, then you should see the budget line drawn in the profit chart on the 90th quantile.

In the Profit and ROI Example, the calculated profit is $600 and ROI is 80 percent, which means that if you mail catalogs to first 20 quantiles of the population (400), then the campaign will generate a profit of $600 (which has ROI of 80 percent).

If you randomly mail the catalogs to first 20 quantiles of customers, then 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.