Apteo's new repurchase segments and key factor analytics for repeat purchasers will help brands more effectively manage and optimize their retention.
Growing ecommerce brands need a healthy, long-term relationship with their customers to build their business, that's why repeat purchases are worth so much more than one-time purchasers. But it's not always easy to know what's driving your customers to come back, even if you have a subscription business. Sometimes customers that were healthy purchasers may stop buying without notice, and you might have no clue as to what happened.
If you had some head's up or deeper insights into what causes customers to come back, you might have been able to keep them around with a pre-emptive offer or an email.
That's why we're rolling out two key features to help you better understand and manage your repeat purchasers:
In the rest of this post, we'll break down what these features mean for you and how you can use them to improve your retention.
If you've got a subscription business, or even if you have several products that you know your customers will buy again and again, you know that it's not easy to identify which customers might drop off their normal buying routine and disappear forever. Different customers have different buying patterns, and while you might be able to forecast things at a high-level, it's not always easy to identify what's going on a per-customer basis - until now.
Today we're rolling out several new, automatically generated segments, that help you identify which customers are likely to repurchase, which customers are overdue, and which customers are likely to drop off, all without you having to lift a finger. As part of this rollout, we've expanded the "Repurchase Recommendations" segments in your segments page, and have added a new way to filter to the specific type of repurchase segments you're looking for.
You can now choose between all your repurchase segments, customers likely to buy a particular product in the next 30 days, customers likely to buy a particular product in the next 30-90 days, customers overdue for a repurchase, and customers likely to not repurchase again.
Using your customer data, we automatically forecast which customers will make a repeat purchase of a product they've already purchased, and how long it will be before they're set to make another purchase. Based on the results, we can group them into one of the segments mentioned above, making it easy for you to take proactive action to help avoid churn or to increase retention.
The best way to use these segments is to stave off any potentially overdue customers with a special offer to get them back in the saddle. Customers that are overdue for a purchase are within a tight window where they were expected to make a repeat purchase within a few weeks before today, but might still be interested in purchasing again because not so much time has gone by that they're considered dormant.
If you filter to customers that are Overdue For A Purchase, you'll be able to dive into the specific products and customer segments that are off their purchasing schedule based on when they were expected to buy next. If you send a special offer, or even a quick questionnaire to these customers, you'll be able to get some of them back in the fold, while learning about what influenced their decision to hold off. A great approach to getting these customers to come back is by using a discount ladder to send a sequence of emails, each with an increasingly large discount, to incentivize them to come back.
In addition to getting your overdue customers back, you can also try to get many of your existing customers who are unlikely to repurchase any product back by offering them a discount or by highlighting new products that they might like.
Our repurchase segments, just like all of our other segments, come with a full suite of product analytics to help you understand key purchasing patterns and buying behavior among your customers. With these segments, you can now get the head's up that you need to keep your customers coming back.
And if you're curious as to what's driving our A.I. to forecast which customers are are likely to come back, you can use your new key factor analytics to dive deeper into your customers' mindsets.
One of the most common things we hear about A.I. is that it can be too much of a black box. Even though it can lead to better business decisions, it's not always easy to know what's driving an A.I. to report what it does. With our new key factor analytics, that's all changing.
We're now rolling out a suite of new analytics to help you get deeper insight into what's driving our A.I. On your Retention Analytics page, you'll see three new sections, each of which will provide a breakdown of the key factors that influenced some of your A.I. models for customer lifetime value forecasts, repeat purchase forecasts, and cross-sell forecasts.
In each section, you'll get a breakdown of the most important data attributes that were taken into account when making each type of forecast. For example, you might have been wondering what are the characteristics of customers that have the highest forecasted customer lifetime value, but you never had a data scientist to dive into that information for you. With these new key factor analytics, you might find that the city where someone ships their products makes a big difference, and by diving deeper, you can see that customers in certain cities are either more or less likely to buy from you again.
You'll be able to dive deeper into any factor that's associated with your customer lifetime value predictions, and you'll be able to sort and glance at any factor that's relevant in forecasting repeat and cross-sell purchases. Just head on over to your Retention Analytics page to learn more.
The best way to use this new information is to identify the most relevant patterns that are associated with your repeat purchasers and then do whatever you can to positively influence customers that have desirable attributes. For example, if you find that female customers that live in Seattle and have made at least 2 purchases before are the most likely to buy from you again, or that they have the highest customer lifetime value, you can proactively email them with information about your loyalty program, or offer them a buy-one-get-one free deal, or otherwise figure out a way to make them really loyal to your brand.
With this data, you'll now have a better handle on what's causing your customers to come back and buy more.
As part of our new updates, we've also excited to announce that we're now incorporating all of your customer tags into our predictions and analytics. Now any tag that you or any other app applies to your customers will be used as a key factor that can help our models learn more about your customers. Whenever your tags provide additional value to our models, you'll see them listed as part of the key factors for that model. This will be especially helpful for customers that are using apps like Recharge to tag subscribers, as well as any brands out there that heavily rely on tags to identify key attributes for their customers.
We're continuing to roll out new features that will help you drive sales by more effectively using your customer data. Our new repurchase segments and key factor analytics will help every brand better market to their customers, but will likely be especially effective for brands that have a subscription component to them. We'll be rolling out these new features to all customers over the coming weeks, so keep an eye on your account to be sure to see them when they're ready for you.
Shanif Dhanani is the co-founder & CEO of Apteo. Prior to Apteo, Shanif was a data scientist and software engineer at Twitter, and prior to that he was the lead engineer and head of analytics at TapCommerce, a NYC-based ad tech startup acquired by Twitter. He has a passion for all things data and analytics, loves adventure traveling, and generally loves living in New York City.