The Holistic team and I discovered something unexpected recently while analyzing activity in a client's customer database. This surprising information shed new light on how we segment customers and how we message them based on what our data is telling us.

Although our program is still a work in progress, I'm sharing my thinking with you now because it explains why you should always keep an open mind whenever you investigate your data. Shedding your preconceived expectations is essential if you want to succeed in using data to guide or rethink your segmenting and messaging strategies.

The background

Deliverability is a key focus of our clients right now, so one of the ways we segment this particular customer base is by recent opens so we can send via the Halo Sending Strategy. This metric is important to ISPs, and thus important to our clients getting into the inbox.

Our client sells subscription consumer products, which customers buy regularly, typically on a monthly buying cycle. Given that cycle, which differs from other ecommerce models, and given that customer behavior is always shifting a little, we also update these segments continually.

The segments update dynamically: Segment 1 has customers who opened 1 to 45 days ago. Segment 2 is 46 to 90 days, Segment 3 is 91 to 120 days, and Segment 4 is 121-180 days.

When we compared each segment on web activity, purchases and revenue, we found something that shook up our assumptions.

Unexpected results

Conventional thinking about customer activity holds that the most recent openers are the most active ones. So we weren't surprised to discover that Segment 1 was the most active, delivering most of the revenue. 

Segment 2 was the runner-up. Again, no big surprise there. Then came the twist.

Segment 4 – the people many marketers would write off as inactive – outperformed Segment 3 on activity, even though they’re similar sizes. Remember, we're measuring activity as web activity, purchases and revenue.

That certainly upended a few assumptions!

Now we have to delve into the data to learn why this is happening and then form some hypotheses to test. We will be looking for answers to questions like these:

• Why do customers who would otherwise be considered inactive, reactivate faster than more recently active customers?
• What's going on with Segment 3 customers who appear to have gone quiet three to four months after their last activity?
• What’s the AOV of customers who are often in segment 3? Is it higher than the average AOV, and if so, could this mean they buy in bulk every few months instead of monthly?
• What messaging changes might persuade Segment 3 customers to become active again?
• Do variations among products or buying cycles affect customer activity?
• We’re just planning our lapsing and lapsed programs. Given this finding, should we start our lapsing-customer program earlier - or later?

Our research dovetails with findings from our work with another client. In our discovery audit, we learned the client was primarily sending email campaigns to customers in Segment 1, the most engaged segment based on opens. But, when looking at all the data (not just email data), we found that customers tended to buy more often when they were in Segment 2.

This meant customers were buying later in their journeys than the brand assumed. Without our audit, our client most likely would still be leaving money on the table by not messaging the highest converting segment more regularly and risking losing those customers to a competitor.

Mismatching messaging and segments has consequences

The most serious consequence is that you lose customers, and their purchases and subsequent revenue, because you aren't sending them the right kind of messages - at the right time.

You could, for example, sacrifice a perfectly good customer if you arbitrarily move them out of your regular database when they hit your magic inactivity date, such as 180 days after their last measured email activity.

Case in point: A "We miss you" message to customers you assume are inactive, without checking for other activity, could be baffling or annoying to a customer who just bought from you or visits your site regularly, even without purchasing. It could even turn off paying or long-time customers who still buy from you but not on your preset buying cycle.

It's fine to build simple segments based on data, as we have done. The danger lies in not looking at your data to see if your assumptions about activity hold true. If you simplify your segmentation too much, you could overlook significant variations that affect how receptive customers will be to your messages.

Disclaimer: Your results might vary

Let me be very clear about this. These findings apply only to this particular client. I'm not suggesting at all that you will find similar patterns among your own customers.

Your results will vary by your brand, the kinds of products you sell or services you offer, your customer buying cycles, customer shopping patterns and more.

That's why it's so important to keep an open mind, to keep all options open and see what your numbers are telling you.  

You could learn that your Segment 4 customers really do meet your inactivity standard, providing you measure them on something besides opens and clicks. Or, you could find out, as our other client did, that your Segment 2 customers are more likely to buy.

How to use our experience to guide your own data deep dive

Our findings on activity in a supposedly inactive segment has prompted us to go deeper into the customer data to understand why it's happening and identify what we need to change so our email campaigns reflect what's actually happening with customers.

There's no formula to this investigation. Look for the buying cycle associated with each product, the quirks and anomalies that are unique to your brand or products, and line up these findings with your current customer segments.

Besides creating more accurate or useful segments, you could find ways to improve or create your customer lifecycle messages and convert more customers.

This will help you learn more about your customers and base activity on more than email habits. You're not using just opens and clicks to measure or define engagement. You're also incorporating transactional activity like registrations, information downloads, page visits and the like.

This will generate informative insights for your entire company – another reason why your email program generates so much hidden value as well as the numbers you see in your spreadsheets.

Photo by Natasha Connell on Unsplash