August 4, 2015 Last Updated 12:58 pm

On the value of Customer Lifetime Value in the digital publishing industry

Guest column: Matt Lindsay, president of Mather Economics, discusses how digital publishers can maximize return on their investments in data and analytics

Digital publishing is a tough business. Monetizing content is done through the dual streams of advertising and audience revenue (paid subscriptions) that have supported offline publishers for centuries, but new middlemen and platforms have disrupted both of these revenue categories, with each new entrant to the value chain claiming their piece of the digital revenue pie.

Digital advertising is rife with new technology vendors that promise to increase revenue through targeting and extending reach, but the effective cost-per-thousand (eCPM) for digital impressions continues to fall. Audience revenue is challenged by the plethora of free content that competes with paid offerings. Although improving, pure digital audience revenue is still in its infancy as a category, and the volume of pure digital subscribers is still small relative to historical print levels. The question facing publishers is how to maximize total revenue from their digital distribution channels across both primary revenue streams. An important tool available to publishers trying to answer this question is dynamic customer lifetime value scoring.

Mather-1Customer lifetime value (CLV) is a (relatively) old concept. Calculating the expected operating margins received from a customer has been done long before the birth of the Internet. For a digital publisher, measuring CLV requires knowledge of the revenue received from a customer and the revenue received from advertising delivered to that customer. In most cases, the direct costs for a digital customer are close to zero, which is the part of the magic of digital delivery platforms, and thus digital CLV is really a revenue measurement and active lifetime forecasting exercise. Where most digital publishers run into trouble calculating CLV for their audience is obtaining data on both revenue streams for each customer and forecasting a customer’s active lifetime. Let’s talk about the data challenge first.

Digital advertising revenue data typically comes from the advertising server and the billing system. Many publishers use Google’s DFP advertising server to deliver digital impressions and their DART sales manager to record revenue. Other ad servers and billing systems are similar in their capabilities. The digital impression delivery data is organized in a manner consistent with how the impressions are sold and delivered.

Digital audience traffic data often comes from Google Analytics, which has both a paid and free version. Omniture is another common source for digital audience traffic data. The data from both of these products are organized by how the site is tagged, which is rarely consistent with the way the advertising data is structured. Merging data from these sources often requires aggregating both data sets to a common level of reporting, often to the day, not a very granular level for analysis. To address this problem, Mather Economics has developed a tagging system that captures all the data needed for CLV and other analysis for each unique visitor. We call this product ListenerTM.

The second challenge for measuring digital CLV is forecasting the expected active lifetime of a subscriber. In many cases, publishers use historical retention as a guide to future behavior, which is a reasonable approximation in most cases. A best practice is to develop a forecast algorithm using survival modeling, an econometric technique developed in the health care field, that can adjust active lifetime forecasts for subscribers in response to changes in factors that affect retention, such as price changes, retention campaigns and product enhancements. If a CLV score is calculated using a forecast algorithm instead of a static historical retention curve, we call it a “dynamic CLV” score.

CLV can inform publishers as they decide what content should be only for paying customers and what content should be offered for free

Once digital publishers have an effective CLV metric in place, they can monetize that investment through their customer acquisition efforts, which is an increasingly important component of the digital publishing business model (see linked article on digital publishers eschewing ad tech). An effective customer acquisition tactic for digital publishers is to offer content for free to prospective subscribers. This can be done through a “freemium” model or metered access. In addition to these models on their own site, social media platforms are now offering publishers a revenue share on advertising impressions delivered adjacent to content publishers provide to the social media companies. CLV can inform publishers as they decide what content should be only for paying customers and what content should be offered for free.

To walk through a sample CLV calculation, let us evaluate the revenue from a non-subscribing visitor that reads an average of 50 article pages a month, each with four advertising positions at an average eCPM of $8. This visitor is generating $1.60 in digital advertising revenue per month (50*4*$8/1000.) If this publisher limits access to 20 articles per month and charges $9.99 for monthly access, they are putting 120 advertising impressions at risk, or $0.96 per month. If there is a 3 percent conversion rate for subscription offers at a 20-article level of free access, the publisher is putting $0.96 in advertising revenue at risk for $0.30 in expected monthly subscription revenue ($9.99*3%.) We can add a time dimension to this analysis by estimating how many months of active subscription life this publisher can expect from the subscriber versus how likely the non-subscribing reader is to continue coming to the site in the future.

To illustrate an application of CLV by a publisher, we can review the case of a digital publisher in a major metropolitan market in the United States with two major league baseball teams. This publisher has a large digital sports audience, and we helped them analyze the audience to evaluate the potential for a digital sports content product. We found that the audience was large enough to support a digital sports product as an add-on to their core publication. The most interesting finding from the project was how much the economics differed for the two baseball team fan bases, which determined how much content should be offered to each group for free to maximize total digital revenue.

One of the teams had a digital audience that was largely national in its distribution, while the other team’s digital audience was almost exclusively local. From an advertising revenue perspective, that meant that the team with a local audience generated much more traffic that could be sold through the direct sales force to local advertisers at higher eCPMs. The other team had about half of their digital traffic from fans living outside the local metropolitan area, which was sold through programmatic channels at a lower eCPM.

From an audience perspective, the team with the national audience had a higher propensity to subscribe, in part because the out-of-town audience was eager to have access to the coverage, in part because they were outside of the print distribution area and their local sports coverage likely does not cover this team in detail. They also had demographic characteristics that were found to be indicative of subscription buyers. The other team’s fans could read the coverage through the print platform or through other local coverage, so they had less demand for access to digital coverage. Also, they tended to have characteristics indicative of a group less likely to subscribe, such as a younger age profile and a greater share of mobile content consumption.

So, how does CLV help this publisher decide what to do?

The CLV calculation demonstrated that fans from the team with more local audience should get more free content than fans from the team with a more national audience. We found that the opportunity cost of lost advertising revenue from a more restrictive access policy to the local audience outweighed the likely additional subscription revenue that would be realized. The opposite was true of the more national team’s audience.

Of course, the level of free access to the sports content is not set by team affiliation. It is possible for this publisher to determine the level of free access to this enhanced sports coverage for each unique visitor (grouped into segments) and for the level of free access offered to be a function of whether the visitor is in-market or out-of-market, what platform they are coming from, their overall digital engagement and other characteristics. The revenue-maximizing level of access also can be determined by what their likely retention would be once they were acquired as a subscriber and how they would likely react to future price increases once they reached the end of the promotional offer.

Mather-featureAs we like to say, data by itself is worthless. What you do with the data makes it valuable. A dynamic CLV calculation is a robust analytical approach for making profit-maximizing strategic and tactical decisions. The incremental profit created by these decisions should yield a substantial return on the investments in data and analytics made by digital publishers.

Matt Lindsay is president of Mather Economics LLC, a global consulting firm that applies analytical tools and hands-on expertise to help businesses develop and implement pricing strategies

  • Marcelo Ballestiero 1 year ago

    CLV is indeed a very important KPI to understand monetization performance and ideally it should be provided at customer level. But the aggregation and normalization of the data needed for this is the most difficult part of this process. An extra level of difficulty is added when dealing with mobile Apps and Games. Data usually comes with several different layouts from multiple AdServers, multiple ad types, different countries, different platforms… Sometimes provided via APIs sometimes files extract from manual reports. Once you are able to overpass this data normalization road block then you’ll have the ability of understand better your customers’ behavior and the value they can provide.