Averages lie to you. One of our publishing clients looked at the average sell-through rate of their online advertising inventory and noted it was 70 percent.
“We can launch a metered paywall, and as long as we do not lose more than 30 percent of our inventory, the lost advertising revenue should be minimal,” they thought.
What the average sell-through rate did not tell them was their best inventory was sold out, while their mediocre and poor quality inventory was only 30 percent sold. The 10-article metered paywall reduced their inventory across the site, affecting both high- and low-quality inventory by about 20 percent, which reduced their digital advertising revenue by several hundred thousand dollars. They removed the meter and re-evaluated their paid content strategy.
Know Yourself on the Margin
Averages can distort your understanding of your business, particularly if there are shared costs and revenues. As an example, a company had business and residential customers. Their business customers had high-density deliveries, meaning several items were ordered and delivered at the same time. Residential customers typically had one or two items per delivery.
In estimating customer profitability, they divided the cost of a delivery stop by the number of packages and determined that residential business was not profitable. “Let’s stop making residential deliveries to save the costs associated with that activity,” they thought.
Before they implemented the plan, they realized that the costs associated with those residential deliveries did not go away if the business was no longer there since the costs were shared with the commercial deliveries. The relevant cost metric for the residential deliveries was marginal cost, not average cost. Once the delivery driver was in his territory, the incremental costs of a residential delivery were minimal.
Seek Granular Insights
Granularity, defined as the level of detail present in a data set, is important for understanding the dynamics of a business. In economics, we focus on the margin, the last unit sold, hour worked or dollar earned. In practical application, marginal effects can be approximated by looking at a business in greater detail and moving away from grand averages.
In digital analytics, data-capture tools are often designed for overall audience measurement for advertising applications and not for customer analytics. Audience measurement for advertising does not require the level of detail that customer analytics does, and the application of audience measure data to customers can lead to wrong conclusions and bad outcomes.
In one of our favorite examples of the importance of granular detail in customer analytics, we found the economics of baseball fans in a city can vary dramatically.
We worked with a major-market newspaper and digital publisher to develop the business case for a sports-only digital product in their market. We initially grouped customers by their preferences for certain sports and estimated advertising and digital audience revenue of each customer segment. We estimated the likelihood of each customer group to subscribe and the potential for lost digital advertising revenue if the sports product was paid versus free.
When we focused on the baseball fans, we noted that the results for the two major league baseball franchises in that city were very different. One team’s fans were national in their distribution, while the other fan base was predominantly local to that market.
The advertising value of the fans outside the market was much lower since local advertisers, who purchased advertising through the direct salesforce at much higher effective CPMs relative to programmatic channels, did not value non-local digital impressions as much as in-market advertising inventory. In addition, local fans were much less likely to subscribe to the digital sports product due to their ability to read coverage of the team from other local outlets without paid access.
As a result, the optimal business strategy was for one team’s fans to receive the product free and the other team’s fans to purchase subscriptions. This type of insight would not have been possible if customers were grouped by all sports fans or even all baseball fans.
Get the Right Data at the Right Level of Detail
An important level of detail that is lacking in most digital data is the combination of digital advertising revenue with content consumption by individual customer. This is a result of the way advertising impressions and content consumption data are captured on most websites.
Google Analytics, both paid and free versions, capture content consumption, typically the number of page views and unique users. Both versions of the product do not offer complete user-level detail for all visitors to the site, but the premium version will offer a sampled set of this level of data if requested. Google DFP and other advertising servers will capture and report data on delivered impressions, CPMs and click-through rates.
The challenge for analysts is that these data sets do not merge easily, if at all, at the level of the individual visitor. The data must be merged at a “lowest common denominator” that is typically aggregated to a day-site section or hour-site section.
As the baseball story above demonstrates, this level of data aggregation can lead to some significant mistakes in determining the best revenue strategy for a digital publisher. My company, Mather Economics, in collaboration with a few of our clients, has solved this advertising and content-consumption data-merging problem through considerable research and development.
So, Get Granular!
Do not make the mistake of using aggregated data to make detailed decisions. Your baseball friends and residential delivery fans will thank you.