Subscriptions are no longer just for newspapers. The consumer product landscape, particularly among e-commerce firms, includes a bevy of subscription-based business models. Internet and mobile phone subscriptions are now commonplace and joining the ranks are dietary supplements, meals, clothing, cosmetics and personal grooming products.
Standard metrics to diagnose a healthy consumer-brand relationship typically include customer purchase frequency and ultimately, retention of the customer demonstrated by regular purchases. If a brand notices that a customer isn’t purchasing, it may consider targeting the customer with discount offers or deploying a tailored messaging campaign in the hope that the customer will return and not “churn”.
The churn diagnosis, however, becomes more complicated for subscription-based products, many of which offer multiple delivery frequencies and the ability to pause a subscription. Brands with subscription-based products need to have some reliable measure of churn propensity so they can further isolate the factors that lead to churn and preemptively identify at-risk customers.
This post shows how to analyze churn propensity for products with multiple states, such as different subscription cadences or a paused subscription.
Unpacking our box
Assume we have an online subscription-based product that can be bought at set delivery intervals: monthly, quarterly and biannually. The customer also has the option to pause the subscription for any reason.
In our hypothetical example, a customer journey involves five states:
State 1: Starts a subscription for the first time
State 2: Unsubscribes from receiving promotional emails from the brand
State 3: Pauses subscription because the supply from the previous delivery is not depleted
State 4: Unsubscribes from receiving promotional emails and pauses subscription (combination of States 2 and 3)
State 5: Cancels the subscription because no longer has a need for the product
Customer transition matrix
Like any relationship, that between a brand and customer passes through many states and phases. The transition between states can be represented in a transition matrix. This transition matrix presents an example of transitions between states:
This plot below conveys the various transitions in a graphical format. The corners represent transition states and each arrow represents the direction of a possible transition journey. Inevitably, one can see that it is possible to move to State 5, churn, from every possible state. However, to move from State 1 to State 4 requires passing through States 2 or 3. This is just one hypothetical customer journey in our subscription-based product.
Putting our data to use
Let’s also assume the brand has a variety of data points about each customer’s journey. Each customer’s files has:
A unique ID
The time since an event occurred within a state measured in the number of days (columns St2, St3…)
Occurrence of an event within said state (columns St2.s, St3.s…; 0: did not occur; 1: occurred)
Demographic and sales data such as year, age, discounts, gender
To generate a measure of size of the number of customers in each state, we can simply calculate the transition frequency and proportion of transitions across the full customer base. Here we see that there are 533 churn events.
We can also see that among customers who started their journey in State 1,640 customers moved to State 2, 777 to State 3, and 160 to State 5. Furthermore, 332 stayed in State 1, which is classified as a non event.
The most probable transition is from State 1 is to State 3, which is shown below as a proportion. We see that 46% of customers that were in State 3 end up in the State 4, and of those, 25% end up churning.
Building a time model
A common approach to modeling time is the Cox proportional hazards (PH) model. It identifies the effect of several variables on the time a specified event takes to occur. In other words, what is the likelihood that a particular customer will be exposed to a transition event (eg. moving from State 1 to State 2)?
Don’t forget, however, that the subscription model has more than just an active and inactive state — there are many possible states that need to be assessed for risk. With a Cox PH model, each transition (trans) is modeled separately and takes into the account the time since entering a transition state.
R has a very useful engine to calculate separate input statistics for each transition in a Cox PH model. We use a stratified Cox model where the strata is determined by the transition variable. This means that we have 8 separate models for each transition.
The following code models just time and does not (yet) include the impact of any other variables:
c0<- coxph(Surv(Tstart, Tstop, status) ~ strata(trans), data=msdata_exp, method=“breslow“)
To incorporate input statistics, we apply the msfit workhorse, which calculates the probability of being in a state given the original state and time spent in each subsequent state. This is the first approach we use when modeling churn across multiple states.
msfit(object=c0, vartype=“greenwood“, trans=trans_mat)
So what did we find?
This next plot shows the probability that a customer moves from one state to another (also known as the cumulative hazard) with respect to time since the initial subscription began. Keep in mind, however, that this plot is an aggregate of all customers and does not show the impact of a specific variable, such as gender or product type, on the final hazard slope.
From this plot we see that at 1,000 days, customers who initialize their journey in State 1 have a 75% probability of transitioning to State 2 and a 70% probability to State 3.
We can also explore the probabilities of a state-to-state transition by creating a probability matrix. This snippet shows the probabilities of customers who started in State 1 at the earliest days of their journey (days 0-6) and the end (days 4560 onward).
By day 5 of the customer journey, there is a 99% likelihood the customer will be still in State 1 and a less than 1% probability of being in State 3. As the journey progresses, however, there is only a 16% probability of still being in State 1 come day 4,787. Of crucial importance for the brand is the likelihood of moving to State 5, the end of the relationship: we see a 33% probability of this occurring.
Here’s one more way to visualize the same trend:
The distance between two adjacent curves represents the probability of being in the corresponding state with respect to time.
Adding more precision with covariates
A full model can be calculated with the workhorse coxph function by introducing stratification by transition and including all additional explanatory variables. From this model we can extract the relevant covariates that explain the likelihood of moving between states for a given demographic or behavioral variable.
In this table, a positive covariate indicates an increase in the hazard of moving from one state to an end state. In other words, the larger the covariate, the more exposed a customer is to the “risk” of transition.
With this model we can predict the distribution of states for a given time since beginning the subscription while taking gender, age or discounts into account. For example, imagine two customers with the following profiles:
Age: Younger than 20
Given these customer profiles, Customer B is more likely over the course of its journey with the brand to churn (State 5) or delay the subscription once and reject promotional emails (State 4). Comparatively, Customer A has a 30% likelihood of remaining an active client with no subscription pauses over the course of 10 years, while client B has a 20% likelihood.
Multistate models are common in business applications. They allow decision makers to see, literally, how states are distributed across a customer journey for a diverse variety of customer segments. Armed with this intelligence, brand decision makers can focus their outreach and acquisition efforts toward customers that have a higher probability of remaining in an active state for a longer period of time. It also shows where in the customer journey customers are vulnerable to churn — which can then be used to implement a strategy to preemptively mitigate vulnerability before it manifests.
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