But that's not how we measure things, is it? We try our hardest to allocate orders to the advertising vehicle that caused the order, seldom considering a series of events.
For instance, take paid search as an example. Assume that a paid search campaign results in a 3% conversion rate and a $100 AOV. We run a profit and loss statement on the 0.03 * 100 = $3.00 demand generated by the campaign, factoring in the cost of the campaign.
But what about the 97% of visitors who did not purchase?
What if you had this data?
- Of those who are left, 50% will visit the website again within one week, with 3% converting, spending $100 each.
- Of those who are left, 50% will not visit again. Those who are left will visit again within three weeks, with 3% converting, spending $100 each.
- Of those who are left, 50% will not visit again. Those who are left will visit again within one month, with 3% converting, spending $100 each.
- Of those who are left, 50% will not visit again. Those who are left will visit again within four months, with 3% converting, spending $100 each.
- Of those who are left, 50% will not visit again. Those who are left will visit again within six months, with 3% converting, spending $100 each.
When I iterate through the five cases above, I calculate an additional $2.75 of future visitor value.
In other words, we measure the $3.00 generated by short-term conversion. We don't always measure the $2.75 of future conversions.
Now there may be additional expenses associated with the $2.75 number --- that customer might require additional paid search expense or might use a shopping comparison site, whatever. So we need to run a true profit and loss statement on the additional $2.75 generated by future visits.
If each first-time visitor (one that doesn't convert immediately) is worth $0.30 profit over the next twelve months, you think differently about attracting visitors, don't you? Now you're ready to experiment, like the folks at Drugstore.com and Zappos recently experimented.
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