Taming your customer lifecycle metrics

Taming your Customer Lifecycle Metrics

In recent years, the idea of “pirate metrics” has gained wide adoption. Pirate metrics stand for AARRR: Acquisition, Activation, Retention, Referral and Revenue. The precise definition of the metrics may differ based on your business. For example, for some e-commerce businesses, acquisition could mean getting a visitor to sign up to your newsletter, activation (or reactivation) could be measured as the first purchase in the last 6 months or since joining, retention as repeat purchase, revenue as total sales, and, and referral could be measured as the number of friends invited by a user who signed up to the newsletter or made a purchase; for a subscription commerce business, retention could be measured as the churn rate (the percentage of customers who cancel their subscription).

The work of lifecycle marketing or CRM typically begins after the initial acquisition, and is about optimizing the activation, retention, repeat revenue and referral rates. How can you achieve marketing success and improve these metrics?

Here is a 5-step guide to taming these metrics:

  1. Construct a “state diagram” of the lifecycle stages for your business: The pirate metrics map to changes in the lifecycle state of the user: e.g. the activation rate metric calculates the change between the new user state and the active customer state. As a first step to optimizing the lifecycle, draw the state diagram of how users could transition between these states. Define active customer based on activity within a time window (e.g. at least one purchase in the last 3 months). Customize the diagram to include the states that makes sense for your business, to define additional states like “core users”, who might not only be active, but making frequent purchases.

    A sample state diagram

    A sample state diagram

  2.  Calculate the percentages along the “edges”: Every month, look at the new/core/active/lapsed users from last month, and understand what new states they have transitioned to. Calculate the percentages of these transitions. The following 2 tables illustrates this.
    Counts:

    Count_t0Active_t1Core_t1Lapsed_t1
    Count_t0Active_t1Core_t1Lapsed_t1
    New_t010030565
    Active_t05030515
    Core_t010712
    Lapsed_t0406035

    Percentages:

    Active_t1Core_t1Lapsed_t1
    New_t030%5%65%
    Active_t060%10%30%
    Core_t070%10%20%
    Lapsed_t015%085%
  3. Assess opportunities by benchmarking and monitoring over time: By looking at the percentages along the edges, you discover where your opportunities and challenges lie. For example, you may discover that only 60% of previous month’s active users stay active in the current month, and that might be a good metric to try and improve through a targeted effort.
  4. Construct targeted experiments for each step: Once you have assessed the opportunities, you can create experiments that might improve the metrics. For instance, in a subscription commerce environment, you might have a hypothesis that you could increase the retention rate by focusing on the edge between following 2 states: customers who have subscribed for 3 or more months, and lapsed customers. In order to improve this metric, you might come up with multiple experiments; an example of an experiment could be to give the users who have subscribed for 3 months a heavy discount to sign on to an annual plan. You could communicate this discount over email, and measure if the email improved the metrics on the relevant edge.
  5. Measure, and iterate: Once you start experimenting, you need to measure how well the experiments are working, and iterate. Successful experiments are

How does this approach compare with the analytical approach known as cohort analysis? Analyzing cohorts a great tool for a couple of analytical use cases:

  • Calculating the lifetime value of a user
  • Understanding if more recent cohorts are performing better than older cohorts

However, white cohort analysis is a great analytical tool, it doesn’t by itself does not provide you the actionable insights you need to improve your lifecycle metrics. The key difference is that cohort analysis primarily classifies users by when they first signed up, rather than their current activity level. In our state transition model outlined above, we group all active users together, irrespective of when they joined, making it easier to see just a few metrics of interest that line up well against experiments you can create.

Once you get started on this approach, there is no limit to the number of experiments you can run to optimize the metrics between all the edges in your state diagram, except limitations imposed by the lack of right tools for measurement, and running the experiments. Creating an experiment might involve some form of messaging that includes stitching together content and offers, delivering these messages, and measuring the impact.

At Blueshift, we are building tools that will enable marketers to monitor user states and create the right experiments easily. Stay tuned for updates from us, but in the meanwhile, we would love to hear how you think about driving your lifecycle metrics.

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.