Customer Analytics: A Guide to Getting Started (part 7)
November 19th, 2008 by Doug Bright(Read the entire Customer Analytics: A Guide to Getting Started series)
In our last installement we tested offers to likely and unlikely buyers to gauge reaction. Let’s now use the results of this test to determine if we are being too timid or aggressive with our probability to buy cutoff percentage.
We fill out our response matrix 2×2 with the resulting clickthrough rates. Let’s assume we see these results:
| Discount | No Discount | |
| Likely Buyers | 4.8% | 3.9% |
| Unlikely Buyers | 3.7% | 2.0% |
What conclusions can we draw from this result?
- Our predictions worked. We clearly see that people we identified as likely buyers respond much more favorably to the offer than those we deemed unlikely.
- Likely buyers who did not get a discount still respond more favorably to the offer than unlikely buyers who receive the offer. This suggests that we can do even better by getting more aggressive with the probability to buy cutoff. This would allow us to reclassify the cream of the unlikely group as likely, thereby reducing the ultimate number of customers receiving a discount.
- Unlikely buyers are almost twice as likely to buy if given a discount. If we have a lot of stock to move and our margin can handle it, it might be worth exploring giving an even deeper discount to these customers. It’s important to test this first though because response to discounts is rarely linear — that is, you don’t get twice as many customers from a 20% discount as you do with a 10% discount.
We can make adjustments and retest to optimize our results even further if time permits. If not, we can tweak our parameters based on these results and finally pull the trigger on our campaign. A lot of extra revenue for a little extra work — that’s what customer analytics is all about.
Tags: Customer Analytics, getting started
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