Note: Not sure which subforum this should go in - guess I'll put it here, since although it doesnt require any interest rates, discounted cash flows, etc., it is a business question.
Background:
I'm creating an extremely simplistic rough model of expected customer conversions from introducing widgets into my product assortment, for a trial period.
Assumptions:
Question:
How many customers convert? I see two possibilities:
Arguments for Calc A:
Arguments for Calc B:
I'm extremely inclined to believe Calc B is the correct approach. Beyond a simply "A is right / B is right" answer, can you please provide a simple way to explain why? Thanks a bundle!
Background:
I'm creating an extremely simplistic rough model of expected customer conversions from introducing widgets into my product assortment, for a trial period.
Assumptions:
- 1 in 5 people will buy a widget in any given year (20% of total population)
- I will be running an in-store test program, selling widgets, for 3 months
- 100,000 people will visit my store and see the widgets during these 3 months
- I expect 1% of impressions (customers that see widgets for sale) will yield purchases (conversions) from my store
Question:
How many customers convert? I see two possibilities:
Calculation A | Calculation B | |
Impressions during 3 month test | 100,000 | 100,000 |
% Total Population that buy widgets | 20% | 20% |
% Buying widgets during test period | 25% (3/12 months) | |
Conversion rate | 1% | 1% |
Expected Conversions | 200 | 50 |
Arguments for Calc A:
- The 100,000 people already only reflect the customers that will visit during the 3-month test period. Thus, multiplying by 25% again will mean Calc B is double-filtering.
- If we imagine the test was for an entire year, then we would correctly expect 20% of our 400,000 customers to buy widgets during the year - after the 1% conversion rate, we'd therefore (again, correctly) expect 800 people to buy widgets from my store. Calc A gives us precisely 25% of 800, which is logical for a quarter-year test - it adds up to the yearly expectation if we multiply by 4 quarters!
Arguments for Calc B:
- While we only have 100,000 customers during those 3 months, this doesn't mean that 20% of them will buy widgets during only these 3 months - it should imply that 20% of the 100,000 customers will buy widgets within a 12 month period. Thus, we should multiply by 25%, despite already only having 1/4 of the yearly customers represented in the model.
- If we imagine the test lasted for only 1 day (to stress test the model), then it doesn't seem logical to use Calc A - indeed, since 20% of people will buy a widget in any given year, why would we assume 20% of a single day's customers would all buy a widget on that very day?
I'm extremely inclined to believe Calc B is the correct approach. Beyond a simply "A is right / B is right" answer, can you please provide a simple way to explain why? Thanks a bundle!