Data. Everybody’s talking about it, but how many publishers are walking the walk?
At this year’s Digital Publishing Awards, The Independent won Best Use of Data. And what set their data strategy apart? The use of propensity modelling. Modelling which saw their readership figures grow to an impressive 98 million readers per month.
In an age where data is often cited as the most valuable asset around, the use of propensity modelling to learn more about your customers cannot be avoided. It’s a valuable tool for the modern digital publisher, and nothing short of essential if you want to truly understand your readers’ behaviours and leverage this information to maximise revenue.
And whilst market segmentation has long been used to target certain customer groups and promote certain behaviours, propensity modelling is the next step forward. But what exactly is it? How does it enable digital publishers to retain and build on their subscriber base? And how does it improve upon existing market segmentation strategies?
What is propensity modelling?
Propensity modelling uses data to predict the future behaviour of customers. For example, this modelling can help you identify how likely a reader is to subscribe or churn, providing accurate foresight into future actions. Each reader is allocated an individual, personalised propensity score, making identification for the likelihood of a certain action simple- e.g. the likelihood of them converting.
How does it work?
Propensity modelling can take various forms. However, the main way it can be so accurately predictive is through Propensity Score Matching. The model looks at the behaviours of previous readers and customers and compares them to those of new ones. Their future behaviour can be determined because groups with similar propensity scores mimic one another. If new readers share the same behavioural patterns and characteristic backgrounds as past ones, they are likely to act in the same way.
Why is propensity modelling useful for digital publishers?
Crucially, propensity modelling is a tool that can help digital publishers both convert new subscribers and retain existing ones.
On the conversion side, propensity modelling looks at the behaviour of the reader and feeds them appropriate content and offers so that the likeliness of subscription is maximised. For example, a reader who is found to be unlikely to convert by the model could be sent a personalised promotional email with an offer to entice them. On the other hand, customers who are likely to convert and don’t need further persuasion should be shown an effective paywall straight away so that they don’t fall at the last mile. Hence, propensity modelling acts as a tool to ascertain the most effective methods to grow your customer base, increase revenue and scale your subscription business without decreasing your profit margin through unnecessary price cuts.
On the retention side, propensity modelling observes how engaged individuals are with your product. Modelling allows your company to discern which customers have a high propensity towards renewing their subscription and those that don’t. This allows you to maximise your time allocation and budget by focusing on genuine churn risks.
Propensity modelling enables your strategy to be targeted to the behaviour and needs of each individual reader - moving away from a one size fits all strategy. Whilst some readers will subscribe regardless of how many articles they read or however much targeted marketing they receive, others will require unique personalisation.
In anticipating the actions of both known and unknown readers, propensity modelling takes the uncertainty out of strategic planning. With greater confidence in customer retention and conversion, investments can be made in high-ROI campaigns, maximising revenues.
What types of propensity modelling can digital publishers take advantage of?
Propensity modelling can predict many differing aspects of your users’ behaviours. Here are some of the main types you need to be making use of in digital publishing:
Propensity to engage: The likelihood that a reader will click on any promotional material they receive.
Propensity to convert: The likelihood a reader will become a paying customer - guiding you in identifying which readers require more persuasion before converting, maximising their chances of subscription.
Predicted lifetime value: The likelihood a reader remains subscribed for a long time and therefore, how valuable they are to your business over their lifetime. Marketing efforts should be concentrated on high-LTV customers as they will bring the most revenue in the long run, generating a larger return on investment. Your most loyal subscribers are likely to be your loudest advocates, too.
Propensity to churn: The likelihood that an active customer will unsubscribe from your business. Churn risks need to be identified as soon as possible and re-engaged if there’s an option to do so.
How does propensity modelling fit with segmentation?
The use of propensity modelling can help you take market segmentation to the next level. Traditionally, market segmentation involves splitting up your target market into groups with similar characteristics and behaviours, such as by age, geography, or user status. This is done under the assumption that customers who are similar in profile will act in a similar way and can thus be targeted by the same methods. The more data points you collect on an individual, the higher the level of similarity between customers can be gauged.
Propensity modelling elevates the segmentation strategy, providing an additional, critical, data point. Publishers need to consider not only who their readers are but also how they act and not just take for granted that similar people will act in similar ways. Segmentation is a valuable strategy and propensity modelling can propel it by adding a mathematical source of certainty.
For example, when targeting a group of 18–24 year olds from south London, use of blanket messaging that you believe applies to the group is an easy starting point. However, using propensity modelling to understand which individuals within the sub-group are genuinely likely to convert is far more valuable.
While propensity modelling does consider customer characteristics to generate propensity scores, segmentation can also support propensity modelling. For example, take two customers who both have a low conversion propensity score but one of them is from a high-income background and the other from a low-income one. The conversion strategies for these two readers will differ. While for the low-income reader conversion may require discounts, for the high-income reader it might be more effective to demonstrate premium value, such as making sure interest tailored stories pop-up for them first when they open the site
Fundamentally, propensity modelling, coupled with segmentation, allows you to efficiently allocate resources in your acquisition and retention efforts.
It is essential for modern digital publishers to move beyond just segmentation if they are to maximise their revenues and retain a lively customer base. With so much data available which can be capitalised upon, predicting the future of your customers to an accurate degree is now a reality, and might prove essential to securing the future of your business.
A powerful subscription experience platform can take care of segmentation and future prediction. For example, user segments are built into Zephr’s subscription platform under the rules builder, allowing for simple, code-less personalisation, while “Optimize” (Zephr’s new powerful analytics) monitors your customers’ behaviour to spot critical behaviours early.
Want to provide your readers with deeply personalised subscription journeys? Read Zephr’s latest insights on how to improve digital experiences.