Posted by: Matt Shanahan
Increasingly, Scout Analytics is performing paywall analysis for publishers – specifically paywall opportunity analysis. The trick pursuing a paywall (audience-based revenue) strategy is to find a unique value proposition (see previous post), a monetization opportunity. Unique value propositions allow publishers to create revenue streams beyond advertising, but rarely does one value proposition apply to the audience as a whole. Consequently, a paywall strategy also relies on packaging that value proposition and targeting the right segment.
Paywall opportunity analysis is critical as it helps the publisher move away from debates on philosophical merits to actual identification of value positions and targets using the existing data from their audience. At a high level, the analysis has 4 simple steps: classify, identify, discover, and correlate.
Classify Demand. The 4 types of audience consumption are fans, regulars, occasional, and drive-bys. Fans are the ones that provide an audience-based revenue stream. So what level of consumption defines a fan? In our analysis, fans are 2 standard deviations above the average audience member on consumption (e.g., page views). By looking at the audience as a whole and plotting each member in a distribution, the definition of high demand becomes very clear.
Identify Segments. Once the audience has been divided up into fans vs. others, a fan profile can be analyzed. Is there a link between fans and location, demographics, firmographics, or technographics? Identifying segments by what fans have in common provides needed targeting information.
Discover Patterns. Human behavior is far more predictable than people want to admit (note: recent study on human movement). When and why do fans come back? Is there a specific time of day or day of week when visits occur? Is the visit a response to a specific topic or author? Is the visit for personal or professional use? Discovering the patterns in fan behavior is the key to tapping a value proposition.
Correlate Value. The final step in paywall analysis is to compare behavioral patterns and audience profiles to correlate a value proposition. It is a sort of reverse engineering of personas within the audience. This is the ah-ha moment for most publishers as they are immediately connecting the dots as to why a particular segment of visitors behaves with a specific pattern. If the pattern of use is based on unique content, capabilities, or connections, the publisher has found a unique value proposition, which is usually monetizable.
The goal of paywall opportunity analysis is to remove uncertainty about the economics of a particular paywall approach. Armed with a value proposition, the publisher can begin the next phase with much higher odds of success and increased ARPU.
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