Trade-offs between personal data privacy, customer analytics – August 2017

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

A fundamental question has arisen from the ability of publishers to capture data on their customers: Where and when do the costs of data capture outweigh the benefits?

One of the costs from capturing data includes slower page load times, which have been found to alter customer behaviour in ways that lower the revenue of the publisher. Lost privacy is another cost of ubiquitous data capture.

What are the costs and benefits of collecting and using customer data? An answer may be observed in differences between European companies and their American counterparts in their willingness to use information they collect about their customers in their operations.

According to newspaper association WAN-IFRA, in the five years from 2011 to 2015, news media companies in Europe lost 21.3% of their print circulation, while in the United States the loss was 8.7%. There are several factors that affect these numbers, including the greater reliance on single copy sales in Europe. But a significant factor has been the more rapid adoption of customer analytics to segment and target customers for pricing actions, retention efforts, and bundle offers.

American companies are rapidly adopting customer analytics to target pricing changes and take other actions targeted to particular customer groups. This use of customer data by publishers has arguably retained millions of newspaper subscribers who would otherwise no longer be customers had U.S. news media adopted European standards of data usage.

So, what choice do publishers have when it comes to using customer data?

A compromise between American and European approaches is possible, and a few hybrid strategies have been employed at European publishers already.

For instance, at NRC, pricing differentiation is done at the product level. At other publishing companies, the pricing differentiation is implemented by geography. In both these cases, the analysis is completed at the customer level, and the pricing strategies are applied at an aggregate level the publisher is comfortable implementing in the market.

Mather Economics worked with a publisher who was trying to decide what data on its customers should be included in a renewal pricing strategy. We modeled the efficiency gain from the use of different types data in pricing decisions. In doing this, we found including household income to the pricing decision added about 20% to the net incremental revenue yielded from a price increase compared to a traditional across-the-board pricing action.

This is similar to the gain in net revenue yield from including tenure in the pricing decision, so customers with different tenure (length of active service as a customer) would receive different prices.

Using household income and tenure in the pricing decisions added about 30% to the net yield (these two effects are not additive due to correlation between the two types of data). Using household income would yield millions more in revenue to this publisher, which makes the decision on whether or not to use the data a quantifiable choice between revenue and customer data privacy.

In our experience, customers do not want everyone to be treated the same. Instead, they are most interested in fairness. They do not want to pay more simply because a company figures out they are willing to pay, but they are OK paying more than another customer who is unable to pay the same price.

Ironically, the data needed to determine who needs a lower price is the data that is often considered the most private, such as household income. Subscription tenure, which many consider less sensitive information than income, is not as helpful in this context. Tenure is best for measuring willingness to pay due to customer engagement.

It appears a significant cost of the loss of privacy may be a loss of trust. If customers no longer trust news media companies with their data, they may also lose trust in their journalism.

What is important to realize is that the decision to use data is not often clear cut, but one that requires careful consideration of the organisation’s objectives and values. As with many difficult topics, understanding the true costs and benefits is critical to finding the right strategy.

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