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How to make the most of cohort analysis

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With third-party data going the way of the dodo bird, digital marketers are looking for ways to do without cookies. Call it “data dieting.” But something must replace those bits and bytes of third-party sweetness. If you can’t drop a cookie, track a cohort.

Any group of customers engaging your web site can be counted as a cohort, provided you are tracking what they do. Are they just going as far as the landing page? Are they filling the cart, but not checking out? Did they buy something before, but haven’t shopped lately?

Churn, drop-off, customer lifetime value — all can be tracked as cohorts. But the online vendor must know what measures are most relevant to their business to make the most of cohort analysis.

(Segment [cohort]): Get it?

“Segments” and “cohorts” are terms sometimes used interchangeably, but that would be incorrect.

Google defined cohort this way: A cohort is a group of users who share a common characteristic. “For example, all users with the same Acquisition Date belong to the same cohort. The Cohort Analysis report lets you isolate and analyze cohort behavior.”

In contrast, segmentation means organizing groups of users around common characteristics, like demographics, geography, personality, or value. It can also group customers using more than one characteristic.

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“A cohort is a form of a segment. All cohorts are segments, but not all segments are cohorts,” said Eric Sloan, director of strategy at performance marketing agency Thrive Digital. Cohorts can be understood as “time-based segments”, for example, a set of users signing on at a web site at a particular time.

Sometimes the two terms get mixed up because of the analytical tool being used by the vendor or analyst, noted Adam Greco, product evangelist for digital optimization platform Amplitude. A cohort is “a group of like users based on interest,” he said. Segmentation “is like a filter,” Greco continued. A segment is an activity. Cohorts are people. And a “cohort depends on identity resolution”, he said.

Simply identifying a cohort is not enough. The analyst will have to drill down further to identify cause-and-effect. “It’s the only way to make cohort analysis meaningful,” Sloan said. The biggest pitfall is just assuming “the time-based cohort caused what you are looking at,” he said.

Asking the right questions

Which leads to the data. There is an answer in there somewhere, provided you ask the right question to get it.

“We spend time using data to figure out the cohort that is meaningful for the business,” Greco said. Take the example of a cohort defined by behavior — customers going through a multi-step process to complete a transaction.

“You need the right data to build the right cohort,” Greco said. You don’t need to worry about tracking people who add items to the cart. “Just because you can track something does not mean you should.” He added. “Too few companies start with the end in mind.” If you start by listing the relevant cohorts you want to track, and work backwards, then you are more likely to be successful, he pointed out.

For Sloan, the cohort is part and parcel of root-cause analysis. “[When] you see KPIs change, you look at all the different factors that caused the change.” Again, correlation is not causation, he stressed, but you keep drilling down through the cycles and ask intuitive or logical questions, finding the data that answers the question.

“Start with a cohort. See if it is time-based.” Sloan said. Spot the drop-off from period to period. Include new visitors as older ones drop off. Look at the face value of all behaviors and events, going through the initial period, then to 30, 60 and 90-day increments. “A cohort is the first step in eliminating some of the noise,” he said, as the analyst tries to measure the customer experience with the web site.

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Greco offered other paths. One approach uses the data to isolate groups of identified users so that groups can be compared. He called this a “persistent cohort.” For example, tracking the number of online shoppers for a seven-day period. New users will naturally enter this cohort while others exit it after the time set. Those who purchase are counted while those who don’t buy are tallied as drop-offs.

Then Greco outlined the “predictive cohort”. One example is looking at the number of shoppers who visit the web site to make another purchase. There may be a group that is 90% likely to buy something next week; another that is 80-90% likely to make a purchase, yet another group that is 70-80% as likely to acquire an item. The marketer can use that data to offer discounts to each cohort, only increasing the discount to entice shoppers in the next group less likely to buy something. “You use the cohort in combination with marketing and promotion to get people to convert,” he explained.

Making the most of data

Cohort analysis is an approach that requires marketers to change their thinking to make the most of their data. Our experts have the same starting point, but pursue their goals along different lines.

To use cohort analysis, “start with the question.” Sloan said. “Tie back to business results that are possible to answer…Understand where and how to drill down…Make sure the KPIs are meaningful.” Make sure the data you are analyzing reflects reality, he added. Data can skew if the same online shopper is accessing the same web site using different devices at different times, he cautioned.

Greco framed cohort analysis as a competitive necessity. In the e-commerce realm, every online shopper is just a “click or a swipe away”, he noted. The burden is on the marketer “to figure out how they are losing people and bring them back.” The faster problems are solved and fixed, the more likely an online web site will be successful.


About The Author

William Terdoslavich is a freelance writer with a long background covering information technology. Prior to writing for Martech, he also covered digital marketing for DMN. A seasoned generalist, William covered employment in the IT industry for Insights.Dice.com, big data for Information Week, and software-as-a-service for SaaSintheEnterprise.com. He also worked as a features editor for Mobile Computing and Communication, as well as feature section editor for CRN, where he had to deal with 20 to 30 different tech topics over the course of an editorial year. Ironically, it is the human factor that draws William into writing about technology. No matter how much people try to organize and control information, it never quite works out the way they want to.

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MARKETING

8 major email marketing mistakes and how to avoid them

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8 major email marketing mistakes and how to avoid them

As email marketers, we know we need to personalize the messages we send to subscribers and customers. I can’t think of a single statistic, case study or survey claiming an email program of one-to-everyone campaigns outperforms personalization.

Instead, you’ll find statistics like these:

  • 72% of customers will engage only with personalized messages (Wunderkind Audiences, formerly SmarterHQ)
  • 70% of consumers say that how well a company understands their individual needs affects their loyalty (Salesforce)
  • 71% of customers are frustrated by impersonal shopping experiences (Segment)

But what marketers often don’t understand, especially if they’re new to personalization, is that personalization is not an end in itself. Your objective is not to personalize your email campaigns and lifecycle messages. 

Rather, your objective is to enhance your customer’s experience with your brand. Personalization is one method that can do that, but it’s more than just another tactic. 

It is both an art and a science. The science is having the data and automations to create personalized, one-to-one messages at scale. The art is knowing when and how to use it.

We run into trouble when we think of personalization as the goal instead of the means to achieve a goal. In my work consulting with marketers for both business and consumer brands, I find this misunderstanding leads to eight major marketing mistakes – any of which can prevent you from realizing the immense benefits of personalization.

Mistake #1. Operating without an overall personalization strategy

I see this all too often: marketers find themselves overwhelmed by all the choices they face: 

  • Which personalization technologies to use
  • What to do with all the data they have
  • How to use their data and technology effectively
  • Whether their personalization efforts are paying off

This stems from jumping headfirst into personalization without thinking about how to use it to meet customers’ needs or help them solve problems. 

To avoid being overwhelmed with the mechanics of personalization, follow this three-step process:

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  • Start small. If you aren’t using personalization now, don’t try to set up a full-fledged program right away. Instead, look for quick wins – small areas where you can use basic personalized data to begin creating one-to-one messages. That will get you into the swing of things quickly, without significant investment in time and money. Adding personal data to the body of an email is about as basic as you’ll get, but it can be a start.
  • Test each tactic. See whether that new tactic helps or hurts your work toward your goal. Does adding personal data to each message correlate with higher clicks to your landing page, more conversion or whatever success metric you have chosen?
  • Optimize and move on. Use your testing results to improve each tactic. Then, take what you learned to select and add another personalization tactic, such as adding a module of dynamic content to a broadcast (one to everyone) campaign. 

Mistake #2. Not using both overt and covert personalization

Up to now, you might have thought of in specific terms: personalized subject lines, data reflecting specific actions in the email copy, triggered messages that launch when a customer’s behavior matches your automation settings and other “overt” (or visible) personalization tactics.

“Covert” personalization also employs customer preference or behavior data but doesn’t draw attention to it. Instead of sending an abandoned-browse message that says “We noticed you were viewing this item on our website,” you could add a content module in your next campaign that features those browsed items as recommended purchases, without calling attention to their behavior. It’s a great tactic to use to avoid being seen as creepy.

Think back to my opening statement that personalization is both an art and a science. Here, the art of personalization is knowing when to use overt personalization – purchase and shipping confirmations come to mind – and when you want to take a more covert route. 

Mistake #3. Not maximizing lifecycle automations

Lifecycle automations such as onboarding/first-purchase programs, win-back and reactivation campaigns and other programs tied to the customer lifecycle are innately personalized. 

The copy will be highly personal and the timing spot-on because they are based on customer actions (opting in, purchases, downloads) or inactions (not opening emails, not buying for the first time or showing signs of lapsing after purchasing). 

Better yet, these emails launch automatically – you don’t have to create, schedule or send any of these emails because your marketing automation platform does that for you after you set it up. 

You squander these opportunities if you don’t do everything you can to understand your customer lifecycle and then create automated messaging that reaches out to your customers at these crucial points. This can cost you the customers you worked so hard to acquire, along with their revenue potential.

Mistake #4. Not testing effectively or for long-term gain

Testing helps you discover whether your personalization efforts are bearing fruit. But all too often, marketers test only individual elements of a specific campaign – subject lines, calls to action, images versus no images, personalization versus no personalization  – without looking at whether personalization enhances the customer experience in the long term.

How you measure success is a key part of this equation. The metrics you choose must line up with your objectives. That’s one reason I’ve warned marketers for years against relying on the open rate to measure campaign success. A 50% open rate might be fantastic, but if you didn’t make your goal for sales, revenue, downloads or other conversions, you can’t consider your campaign a success.

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As the objective of personalizing is to enhance the customer journey, it makes sense then that customer lifetime value is a valid metric to measure success on.  To measure how effective your personalization use is, use customer lifetime value over a long time period – months, even years – and compare the results with those from a control group, which receives no personalization. Don’t ignore campaign-level results, but log them and view them over time.

(For more detailed information on testing mistakes and how to avoid them, see my MarTech column 7 Common Problems that Derail A/B/N Email Testing Success.)

Mistake #5. Over-segmenting your customer base

Segmentation is a valuable form of personalization, but it’s easy to go too far with it. If you send only highly segmented campaigns, you could be exclude – and end up losing because of failure to contact – many customers who don’t fit your segmentation criteria. That costs you customers, their potential revenue and the data they would have generated to help you better understand your customer base.

You can avoid this problem with a data-guided segmentation plan that you review and test frequently, a set of automated triggers to enhance the customer’s lifecycle and a well-thought-out program of default or catch-all campaigns for subscribers who don’t meet your other criteria. 

Mistake #6. Not including dynamic content in general email campaigns

We usually think of personalized email as messages in which all the content lines up with customer behavior or preference data, whether overt, as in an abandoned-cart message, or covert, where the content is subtly relevant.

That’s one highly sophisticated approach. It incorporates real-time messaging driven by artificial intelligence and complex integrations with your ecommerce or CRM platforms. But a simple dynamic content module can help you achieve a similar result. I call that “serendipity.”  

When you weave this dynamic content into your general message, it can be a pleasant surprise for your customers and make your relevant content stand out even more. 

Let’s say your company is a cruise line. Customer A opens your emails from time to time but hasn’t booked a cruise yet or browsed different tours on your website. Your next email campaign to this customer – and to everyone else on whom you have little or no data – promotes discounted trips to Hawaii, Fiji and the Mediterranean.

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Customer B hasn’t booked a cruise either, but your data tells you she has browsed your Iceland-Denmark-Greenland cruise recently. With a dynamic content module, her email could show her your Hawaii and Mediterranean cruise offers – and a great price on a trip to Iceland, Denmark and Greenland. Fancy that! 

An email like this conveys the impression that your brand offers exactly what your customers are looking for (covert personalization) without the overt approach of an abandoned-browse email.

Mistake #7. Not using a personal tone in your copy

You can personalize your email copy without a single data point, simply by writing as if you were speaking to your customer face to face. Use a warm, human tone of voice, which ideally should reflect your brand voice. Write copy that sounds like a one-to-one conversation instead of a sales pitch. 

This is where my concept of “helpful marketing” comes into play. How does your brand help your customers achieve their own goals, solve their problems or make them understand you know them as people, not just data points?  

Mistake #8. Not personalizing the entire journey

Once again, this is a scenario in which you take a short-sighted view of personalization – “How do I add personalization to this email campaign?” – instead of looking at the long-term gain: “How can I use personalization to enhance my customer’s experience?”

Personalization doesn’t stop when your customer clicks on your email. It should continue on to your landing page and even be reflected in the website content your customer views. Remember, it’s all about enhancing your customer’s experience.

What happens when your customers click on a personalized offer? Does your landing page greet your customers by name? Show the items they clicked? Present copy that reflects their interests, their loyalty program standing or any other data that’s unique to them?  

Personalization is worth the effort

Yes, personalization takes both art and science into account. You need to handle it carefully so your messages come off as helpful and relevant without veering into creepy territory through data overreaches. But this strategic effort pays off when you can use the power of personalized email to reach out, connect with and retain customers – achieving your goal of enhancing the customer experience.

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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

Kath Pay is CEO at Holistic Email Marketing and the author of the award-winning Amazon #1 best-seller “Holistic Email Marketing: A practical philosophy to revolutionise your business and delight your customers.”

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