Reporting: email profitability, comparison and segment trends

27 Oct ‘16

Email profitability

Open rate, click rate, buy rate and profitability – all in one place.

We calculate profitability using the «last indirect transition» attribute. This is also GA’s default. But we’ve made two important improvements:

  • we exclude cancelled and returned orders
  • we have two modes – «online only» and «online + offline» – allowing us to account for offline orders
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You can choose any mailing list: automatic, manual, tag-based. And, of course, you can apply any customer segment, just like in all our reporting.

People often ask «how do you account for offline sales?». Our answer:

  • we store all orders in one table, in one format, so it technically doesn’t matter to us which orders to use – we can do it either way
  • the attributes are the same – the screenshot below shows that there are times when a person opens an email, visits the site, views a product, and then buys it offline a few days later
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Let’s compare a group of emails in GA and in our report using both modes

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And here are a couple metrics in one table for convenience

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Why does GA show higher conversions? – Because it is calculated using transitions, not emails sent. This means that GA doesn’t show the whole picture and can mislead you into thinking that everything is OK.

Why are there fewer orders in «online only»? – There could be two reasons here. The first is that we exclude unsuccessful orders. The second is that we use our own tracker, and two different trackers might not always work completely the same.

If you account for offline, the conversions can be 3.5 times higher? – Yes. And there’s no reason not to account for these sales.

Segment comparison

The report can compare all segments included by gender, age, turnover, average check and number of purchases.

Use cases:

  • Searching for interesting patterns
  • How do iOS and Android users differ?
  • Is there a difference between «viewed kitchens but didn’t buy» and «viewed and bought»?
  • Evaluate which strategy works best, if you have different approaches for different segments. For example: We randomly split customers into two groups – «send 2 emails a week» and «send 4 emails a week» – and then we compare them. There were no differences by gender, age or region. Based on average check, amount and number of purchases, you can understand which approach was more effective.
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Segment trends

The report shows monthly trends for turnover, number of orders, average check and number of customers for each segment that you create.

Use cases:

  • See whether bad and good segments are increasing or decreasing in RFM segmentation
  • Monitor the trends of an interesting target segment, like «women aged 25-35» or «subscribed to emails about business»
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