How to make your email marketing smarter - PC Retail

How to make your email marketing smarter

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True data-driven marketing is about refusing to compromise on pseudo-accurate metrics and standard customer analytics. Here are two examples of measuring campaign success.

The first example is email open rates, which don’t even adequately measure email open rates, let alone campaign monetary success.

The second is email revenue attribution metrics, which are, admittedly, a bit more accurate, in terms of evaluating the monetary success of a particular campaign. They still, however, fail to take into consideration customers who might have purchased the item without receiving that particular email. The best indication of the success of a particular retention campaign is the amount of monetary uplift generated as a result of it.

Unfortunately, many methods of measuring retention campaign uplift are inaccurate as well. Marketers often compare the week a particular campaign ran to the same timeframe for the previous week, month, or year. They then measure the difference in revenue between the two time periods and – voila! “Campaign success,” they declare.

These methods of measurement, however, fail to take into account the myriad of different circumstances between the two time periods.

Before marketers can even get to the point of testing and measuring, they have to acknowledge true customer data analysis. Although most companies today are using some form of customer analytics, it is important to distinguish between standard analytic practice and true customer analytics.

Standard analytics fail to take a holistic approach to customer behaviour. If registrations are up this year by over 50 per cent, that’s great, but what percentage of customers were active after registration? That increase isn’t so impressive if you’ve done deeper analysis to see the bigger picture.

Standard analytic practice often ignores the dynamic behaviour of a customer, such as the different customer lifecycle stages. The activity of a new customer should be understood differently than the activity of a long-term active customer.

We often see our client’s disappointment when they take their old marketing plan and copy it into our retention automation platform. It’s not just that their marketing campaigns aren’t generating the results they thought – it’s that with deep customer data analysis, they’re now forced to see it with their own eyes.

Only once you give up those quasi-metrics and start real customer data analysis can you start to learn from your past customer marketing campaigns and improve them in the future.

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