Using data to determine audience price points, increase reader revenue – October 2017

By: Xavier van Leeuwe, Matthijs van de Peppel, and Matt Lindsay
Director of Marketing and Data, Manager of the Data Intelligence and Customer Relationship Management Team, and President
NRC Media and Mather Economics

Companies with subscribers have several advantages over companies that do not. One of the most significant is they can differentiate pricing across their customers to a degree not possible in many other industries because they have one-to-one communication with each customer when they renew their subscription or reach the end of their promotional offer.

Other industries, such as consumer packaged goods, have prices observed by everyone in the market. Publishers also face this issue when they are acquiring new customers. Having multiple acquisition prices in the market at the same time can cause confusion and frustration. But renewal pricing can be done individually with each customer, or at whatever level of granularity each publisher decides to use, because the renewal price communication is not public. (NRC, for example,chooses not to differentiate renewal pricing by customer but to differentiate pricing by product.)

For more on the economics of this subject, there is a vast literature on pricing theory and application. For the purposes of this blog post, we will focus on the application of pricing differentiation to the news media business.

In 2002, I (Matt) was working as an independent consultant for Knight Ridder, the second-largest newspaper publishing company in the United States at the time. The company had hired me to analyse its subscription pricing strategy following several years of declining audience volumes and revenue despite aggressive discounting of subscription prices.

In retrospect, these were the first signs of digital disruption in the industry due to free online content.

Knight Ridder sent me data from the subscription system at one of its newspapers, the St. Paul Pioneer Press, to begin my analysis. I noticed the retention behaviour of subscribers was remarkably similar to data I had seen on patient lifespans in healthcare economics in graduate school. The similarity suggested survival analysis as a possible approach for modeling subscriber retention and price elasticity.

Survival analysis is the field of statistics used to analyse patient data to estimate the remaining life span. This same type of econometrics can be used to predict failures in machinery and the time to significant events in other fields.

In the application of this approach to the publishing industry, I was interested in predicting how long a subscriber would remain active as a customer. The “failure event” to be predicted in healthcare economics is a patient’s death. In my models for publishers, it was a cancelled subscription.

Using survival analysis, it was clear price increases played a role in a subscriber’s likelihood of stopping, but the effect of price was very different across the customer base.

Certain customers were 20 times more likely to stop after a price increase than others. It quickly became clear that a one-price model was not the best strategy for publishers. Much like other industries, such as airlines and hotels, publishing could use sophisticated pricing analytics to increase revenue and operating margins.

Our first few tests of this insight were targeted pricing adjustments to groups of subscribers who appeared to be paying much less than they would be willing to pay for the product. Indeed, the first tests showed some customers showed very little reaction to price increases while others had significant reactions.

Eventually, we developed a recurring process where subscriber data was received from the Pioneer Press and other Knight Ridder newspapers every week, and we returned a suggested renewal price for each home delivery account receiving a renewal notice.

Statistically representative control groups were used to measure the effect of prices on retention and revenue, and we adjusted the recommended prices based on the observed incremental stop rates across customer segments.

Several publishers felt this type of pricing was not acceptable due to being deceptive or dishonest. However, gradually more publishers observed this type of pricing benefitting their customers and the publishers themselves.

Without the flexibility to target price increases, publishers would have to raise subscription rates for many of their least-affluent, price-sensitive, and youngest subscribers at the same level as they do for their wealthiest, least-price-sensitive customers. And these vulnerable customers would stop their subscriptions while the regular customers would probably have paid more.

We now support this kind of pricing strategy for about 500 publications in 12 countries on four continents. These clients provide us with subscriber data on about 30 million households every week.

You may wonder how charging different prices for the same product works in a relationship economy. It really depends on local customs and what feels right for the organisation and for your customers.

Some compare differentiated subscription pricing to progressive taxation. Countries do not expect everyone to pay the same amount of tax every year because individuals with greater income levels are affected less, at the margin, by tax rates. Similarly, a US$500 per year newspaper subscription is a much greater percentage of disposable income to a school teacher, on average, than it is to an investment banker.

Arguably, investigative journalism is a public good that benefits everyone once it is produced. Differentiated subscription pricing benefits society by providing greater financial support for journalism and greater access to independent news.

Survival analysis and A/B testing

A powerful aspect of regression models, including survival analysis, is that it can measure the effects of important factors on subscriber retention in isolation. Once these models have been developed, predictions can be made on the number of accounts that will stop due to a renewal price increase and who is most likely to stop.

Using A/B testing with no-increase control groups to validate the accuracy of the model predictions is the best way to establish a closed-loop pricing and retention optimisation process where insights from price changes are incorporated into future changes, thus improving the efficiency and performance of the pricing strategy.

With digital subscriptions, the number of variables that can be included in a survival model of retention increases substantially due to the availability of data on customers’ engagement with the content online. We can identify who has accessed content online, what they read, on what platform, and at what time.

As expected, subscribers who are more engaged with the content are less likely to stop their subscription following a renewal price increase.

Several predictive metrics are consistently important in our models of print and digital subscriber retention. We can classify these metrics into consumption, interaction, attitudinal, time, and socio-economic categories, and there are other metrics that do not fall into these groups:

  • Consumption metrics describe the quantity, frequency, and time spent with the content.
  • Interaction metrics describe actions taken by customers while on the site or while they are engaged with the content.
  • Attitudinal metrics are those that measure the level of enthusiasm or loyalty an individual has for a topic or community.
  • Time metrics reflect when events occurred during a subscriber’s lifecycle, and the overall time of activity on the account, often called the account tenure.
  • Socioeconomic metrics include factors characterising an individual’s demand for the product, such as disposable income, price sensitivity, age, gender, macroeconomic indicators, education, and household type.

Using survival analysis and other types of customer analytics, we have adjusted retention strategies and operational processes in addition to renewal prices:

  • We have found data-supported retention campaigns can reduce churn by 15% in the first few weeks, and the performance usually gets better as publishers learn from their initial efforts.
  • Pricing strategies can reduce price-related customer losses by as much as 75%.
  • Using data on call outcomes, an analysis of customer service representatives to identify the best performers and training others to replicate their success can increase the yield from those call centres by 30%.

Given the changes to the advertising industry, it is likely most revenue for publishers will come directly from their audiences for the foreseeable future. Using analytics and testing to manage customer acquisition, retention, and pricing strategies will make the difference between those publishers that survive and those that do not.

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