Reduce Churn for Subscription Service

Top 4 Campaigns that Reduce Churn for Subscription Service Companies

This series of blogs goes into detailed campaigns that growth marketers can run for specific industries. These campaigns are tailored towards goals and revenue that growth marketers are responsible for. Our second industry deep dive takes a look at the subscription service industry and campaigns for growth marketers to reduce churn and increase customer loyalty.

The subscription service model is unique compared to the conventional retail sales cycle. They measure their business with different metrics and have different goals. Some metrics that growth marketers at subscription companies are held accountable for are churn, up-sell, and win-backs. Much can be done to impact each of these metrics at different stages of the customer lifecycle. Here is a breakdown of personalized emails and push notification campaigns to use for reducing churn and increasing revenue for a subscription service company.

Churn Intervention: Marketing teams can use churn score rates to create a segment of at-risk customers based on their behavior or low engagement with the product. These customers can be sent personalized offers or incentives based on their purchase or browse history to continue the subscription on day 1, 7, and 30 days after they qualify for the churn list.

Subscription Upsell

Subscription upsell example from Birchbox. Prompting customers to gift a box for valentine’s day.



Subscription Up-sell: AI driven scoring can highlight customers with high up-sell propensity based on high engagement volume with the product. These highly engaged customers are great for incentivizing to switch to the next subscription tier since they are satisfied with their current tier. These messages can be sent out 1, 7, and 30 days after they show behavior of high up-sell propensity.


Sense of urgency for customers who have not signed up




Abandoned Cart: For the visitors on your site who have shared their email address but not made their first purchase or were in the middle of making a purchase but abandoned the session can be reached out to with very specific product they were looking at. Since these customers have not yet made a purchase it is imperative that the outreach be fast and timely (1, 3, 7 days after abandonment) or else they lose their intent to make a purchase or reason for considering the product in the first place.

Offering a free snack and discount to churned customers



Win-back: For those hard to convince churned customers, growth marketers can offer personalized promotions based on an uplift strategy. The cadence can be 1, 2, and 3 months after churn since you don’t want to annoy these customers who are already out of the buying cycle. They no longer see the value in the service and it’s very hard to change their mindset while not putting them off.





Watch out for more posts about growth marketing, and check out our comprehensive guide here for everything you need to know about the subject.


The Case For Predictive Segmentation – Part 1 of 2

Philip Kotler Segmentation Quote

Retention & Growth marketers are often interested in taking action on a segmented base of users. Classic segmentation methods include

  • Lifecycle based segments: new, active, lapsed etc.
  • Behavioral segments based on user behavior on the website/app
  • Demographic: Age, gender, location, household income, education based
  • Traffic source based
  • First purchase product/category etc.

Given all these ways of segmenting, how should any marketer approach segmented marketing, for the purpose of improving their core retention metrics? Some of the metrics CRM or growth marketers might be interested in improving through a segmented marketing strategy may be around activation rates, repeat purchase rates, or churn/retention rates.

Here are 2 steps for how you can use segmented marketing to drive higher response rate on any metric, say, repeat purchase rate:

  1. Use criteria that lead to a big spread in response rates in the steady state: Researchers on segmentation, have pointed out the need for segments to be  identifiable, substantial, accessible, stable, differentiable and actionable.  In the digital world, the tests on identifiable, accessible, and actionable are often easy to meet with segments. However, what really separates useful segments from the rest is the differentiability of the segment especially in response rates, their stability (i.e. whether the same criteria continue to map to differentiated responses over time), and whether the segments are substantial in size. In other words, you need to identify large segments of users whose response rates are substantially (3-10X) different from the average
  2. Test what happens to the steady state when you introduce a message or an offer: Once you have identified your segments in a steady state, you want to be able to test how you could increase the response rates further by introducing new variables like product features, offers, or content.

We will focus this first post in a 2-part series on the 1st of these challenges:large segments of users whose response rates are substantially (3-10X) different from the average. While this sounds simple enough, in practice, quickly figuring out the criteria that lead to a big spread in response rates could take some work. For example, in a typical setting, your gender based response rates might not look very dissimilar for repeat purchase: Let’s say your average repeat rate is 50%, and that men have a repeat purchase rate of 45% and women 55%. Now, there is clearly a difference in repeat purchase rates, but not enough for you to build compelling campaigns around it. At the other end of the spectrum, you might be able to identify outlier users, whose response rates are very high – but these outlier users may not meet either the stability test (e.g. will they continue buying the same way), or may not be a substantial enough segment.

This is where predictive segmentation & machine learning have a role to play. Machine learning can quickly figure out variables that are important, and combine them to come up with models that give you a 3-10X separation on large buckets of users. At Blueshift, we make this easy by making predictive segmentation scores available “on tap” for you to use.

Blueshift Predictive Repeat Purchase Score

Visualization of predictive score percentile with corresponding repeat purchase rates

For instance, in this graph, you can see how multiple variables have been combined to generate a repeat purchase score that gives you a nice spread in predicted response rates. In this example, users in the top 10 percentile of scores have a 90% repeat rate, whereas users in the bottom 10 percentile of scores have a repeat purchase rate of only 9%.

Now that you have identified a way to find segments of users who have a much higher response rates than the average in the steady state, how do you test the actions that will help you improve these metrics? Our second post in this series will look at that.

Using Behavioral Messaging to Drive higher response

Using Behavioral Messaging to Drive Higher Response Rates [Infographic]

Marketers have known that timely and relevant messages, tailored to a user’s behavior, are more impactful than batch and blast emails. But just how effective can these behavioral messages be? And how can you incorporate them into your marketing plan?




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Relationship marketing to the perpetually connected customer

Relationship marketing to the perpetually connected customer

With multiple devices at their disposal, a customer’s path to a digital purchase now often spans both a mobile device and a desktop.  As an NYT article pointed out, “85 percent of online shoppers start searching on one device — most often a mobile phone — and make a purchase on another.” Comscore has declared that 2013 was the first year when the majority of users became “multiple platform”, i.e. they were accessing the same digital properties across multiple desktop and mobile devices. Indeed, Forrester has coined the term “perpetually connected customer” (PCC) for someone who owns and uses at least 3 connected devices, and thinks that close to half of online adults are now PCC.

These multi-platform perpetually connected customers are also becoming addressable in new and interesting ways at large scale:

  • As of last year, Apple had already sent 7.4 trillion push notifications through its iCloud service on iOS and Mac
  • Due to high social media consumption on mobile devices, Facebook’s FBX was already serving billions of retargeting ad impressions as of mid last year

Gone are the days when digital relationship marketing could be equated with just email, and the expectation that the email would be opened on the desktop. Today’s customer is engaged through variety of messaging techniques on multiple devices, and relationship marketers have to catch up to that reality.


Today's hyper connected shopper

Today’s hyper connected shopper

But therein lies the challenge: how is the marketer to unify all the information about a customer, and engage with the customer with the right message and deliver it on the right platform, using the right channel? Take something as basic as an abandoned cart reminder, which in the old days was simply a triggered email. How do you scale a simple campaign like that to target the perpetually connected customer on the right channel, intelligently switching between email, push notifications and retargeting?

We have 3 tips for marketers to adapt to this brave new world:

  1. Stop treating platforms and channels as silos: In this multi-platform world, the article goes on to point out, platforms and channels cannot be a silo anymore. As the NYT article points out, Jason Spero, director of mobile sales and strategy at Google, slowly came to the realization that you can’t ‘put mobile in a silo. It’s also about the desktop’. Similarly, you can’t simply treat channels like email, retargeting and push notifications as silos in themselves; the optimal strategy would be to find the right channel and the right platform for each user.
  1. Move away from list pulls to personalized triggered communication: Relationship marketers have long been to a model of defining target audiences, and “pulling lists” that match these criteria, and setting up campaigns that get delivered in batch mode to the entire list. However, in the perpetually connected world, the customer values personalized communication in context. Moving towards personalized real time triggered messages, instead of audience based batched communication, is the best way to engage the customer throughout their lifecycle.
  1. Develop unified attribution and testing to measure true lift:  In the multi-platform, multi-channel world, testing new strategies requires superior discipline on measurement and attribution. As a simple example, if you spend dollars on display retargeting on a mobile device, how can you truly understand whether the customer would have purchased anyway, through other free messaging channels like email or push notifications? Despite knowing that techniques last click attribution have deep flaws, marketers have often lacked tools that help them measure the true lift.  A/b testing methods that only show channel level metrics in a silo need to be replaced with techniques that measure the impact of various levers on treated and holdout populations across channels and devices.

At Blueshift, we are building solutions to address these challenges (sign up to stay updated on our launch). But in the meanwhile, we would love to hear how you have been solving them.