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See below an example of an Analyticbridge email campaign that was monitored over a period of about 600 days. It clearly shows that 20% of all clicks originate after day #5. Yet most advertisers and publishers ignore clicks occurring after day #3. Not only 20% of all clicks occurred after day #3, but the best clicks (in terms of conversions) occurred several weeks after the email blast. Also, note an organic spike occurring on day #23 in the chart below - this could be due to our vendor (iContact) featuring the newsletter in question on their website, without charging us an extra fee.
This brings interesting points:
Thus, there's a systemic methodology flaw and bias when measuring half-life of your campaign (unless you use ad-hoc statistical methodology): the data is right-censored. How can you be sure that 97% of all clicks occur in the first 3 days? Maybe as many clicks will arrive between day 11 and day 300. But since your window of observation is only 3 days (or at best 14 days), you just can't answer the question. You can compute good estimates for half-life though, if you use a statistical model based on (say) exponential distributions, together with statistical inference, and integrate the fact that the data is right-censored, in your statistical model.
Below is a chart showing that even 60 days worth of historical data covers only 95% of your campaign in terms of clicks - and indeed much less in terms of revenue:
Here's an example where the data and conclusions are biased and wrong due to ignorance of the "right censorship" principle that applies to time series: http://blog.bitly.com/post/9887686919/you-just-shared-a-link-how-lo...
Conclusion: You don't need to track your email campaign for 600 days to measure ROI, you can monitor your campaign for 28 days, and then make interpolation using statistical models. Of course, if you started your campaign just before a great event (like Christmas shopping), then you need to take into account seasonality. That's where a statistician can help you. The above chart represents a campaign generating about 6,000 clicks.
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I am creating a post that belongs to the tail of a similar distribution if the original post plays a role of email blast :-).
In any case, what might be interesting here is to produce the plot of of "Days after the beginning of email blast" vs. "Days between click and conversion". It should give a good indication of relationship between the "Days after the beginning of email blast" variable and conversion. If there is enough data, one could produce regression curves for different customer segments to show difference in behavior (if any).
On another note - I arrived to this thread from the Analytic Bridge Data Science book. Can anybody post a link to some good review or a book on Life Time Value models? Thanks in advance.
Yeah, this is just 'brilliant'; I think its fair to say that a similar analysis could be applied to a blog post also;
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