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Which metrics make you a smart marketer?

Lindsay Tjepkema
Lindsay Tjepkema , Marketing Director, Americas , Emarsys

Over the past 13 years we’ve worked closely with some of the top ecommerce companies around the world, and it’s safe to say we’ve learnt a lot. Marketing is no longer an art, it’s a science; measuring performance against KPIs and key metrics has become a key aspect of every marketer’s life. But to really influence their business, ecommerce directors and marketing managers need new types of hard numbers, hard numbers that can truly influence their strategies. We’ve compiled and visualised metrics that really inform you, and most importantly help you to decide what your next actions should be. We’ve called them Smart Metrics and they play a fundamental role in our customer intelligence platform, Smart Insight. Want to see more? Take a look at the video below for a sneak peak of what to expect.



In short, Smart Metrics are built with predictive modelling algorithms and are typically categorized into 3 groups:

Predictive revenue

This category is calculated on revenue related metrics such as the future lifetime value of contacts in each lifecycle stage. This is particularly beneficial as you can evaluate how much you need to spend in order to convert/retain/win back each customer. It also provides the predictive gain and loss for the last 30 days, combining the revenue generated by conversions and the future changes to customer lifetime value, so you know how the conversions, or lack thereof, within each group will affect your bottom line in the future. This metric is instrumental when deciding your next best move.

Predictive modelling allows you to forecast the financial implications of sticking with the same strategy and campaigns. For example, you could see a defecting buyer is 15% likely to convert, and an inactive buyer is 2% likely to convert, making it clear  that what you are currently doing for inactive (for example, to keep sending them the same weekly newsletter), is no longer a good idea.

As you change and improve your program these figures change too, soon you will have automated the optimisation of your customer engagement strategy – time and money well spent.

These are only predictions, but they are accurate enough to use as guidelines, especially when comparing different strategies for different segments. They give you a wide ranging view of the revenue impact of your actions (or lack of actions) presented in a single dashboard.

Purchase patterns

These metrics look at purchase related behaviour, such as which products and categories are most often involved in conversions specifically from each group, allowing you to target specific contacts more accurately. They also provide details of the time lapsing between purchases so you will know the appropriate timescale to approach your customers with incentives in each of the different stages.

Here is an example: predictive analytics could say that if your first-time buyers do not make a second purchase within 47 days, they are more likely to defect than to buy a second time. You had previously guessed 90 days, but thanks to real-time data you now know where the watershed lies, so you bring your second purchase incentive program forward from 85 to 42 days.

All of these metrics are based on sophisticated learning algorithms, meaning they are constantly evolving. Who can say if the watershed will move over time, or shift during the year? With smart metrics you can keep tracking the results against your business goals and make appropriate changes to your program schedule whenever necessary.

Engagement patterns

These metrics quantify engagement related metrics, showing which of your customer engagement strategies are the most successful in terms of responses and conversions. They also track the time between online behaviours such as email or website activities. This allows you to, for instance, determine the optimal sending times for each segment.


What is the ratio of first-time purchases in your industry? In the ecommerce world, industry data consistently shows that the majority of revenue is still coming from first-time buyers that have not bought before and most likely will not buy again. We are constantly analyzing our customer data and on average only 30% of revenue comes from repeating customers, so if you are above this figure you are doing better than average but make sure you know where you stand, these metrics will tell you exactly how well you’re doing and also how the efficiency of your marketing changes over time; and wherever you are, there is always room for improvement! Can we really accept that the majority of ecommerce customers will never come back?!

A good retention automation platform offers reporting that highlights this and lets you measure the impact of retention automation for your business. More importantly, it shows you whether or not you are getting better at it over time. Download the 7 steps to retention automation guide for practical advice on how to maximize revenue from your customer data in 2015.