by Matt Shanahan on April 11, 2012 in Advertising, ARPU, Audience Development, Behavioral Analytics, Digital Revenue Optimization, Loyalty, Revenue Optimization
Through 2015, much of the B2B media revenue growth forecasted by Veronis Suhler Stevenson shows a substantial portion will come from events — both live and virtual. Most notably, over 70% of clients are increasing investment in geographical events that can be replicated in multiple locations – geo-cloning, as one client called it. And because events require longer lead times, extensive production and marketing investment, optimizing event revenue becomes dependent on trade-offs – specifically, trade-offs between the revenue potential of various events. So how do you measure revenue potential of an event? Event revenue mainly comes from two sources: vendors and attendees. In the case of vendors, their revenue directly correlates to the number of attendees to which they gain [...]
by Matt Shanahan on March 19, 2012 in Advertising, ARPU, Digital Revenue Optimization, Loyalty, Metered Model, Paywall
From following the paywall hashtag on Twitter over the last 18 months, there has been a steady increase in the debate about paywalls, both pro and con, but mostly without any data or revenue models. Our previous research (http://blog.scoutanalytics.com/advertising/the-digital-drop-off/) showed that the move from print to digital significantly reduces the revenue capacity of a publisher. The reality is this: Without a fundamental change in digital ad units and their revenue production, publishers have no choice but to pursue alternate revenue streams. So where do publishers go when advertising revenue becomes unsustainable? Higher-margin marketing services and subscription revenues are quickly becoming the answer, also known as a metered paywall. Not all publishers will be able to implement a full subscription model. [...]
by Matt Shanahan on January 6, 2011 in Digital Revenue Optimization, Metrics
Posted by: Matt Shanahan Managing capacity, load and yield are critical to optimizing revenue and profits. Airlines, hotels, rental car agencies, and others have employed revenue optimization strategies for years to remain competitive and in business. Facing radical change and deregulation, it was the airline industry’s ability to manage revenue capacity (number of seats), load (percentage sold), and yield (revenue per passenger per mile) that allowed incumbents to fight off upstarts. To optimize digital media revenues, publishers must adopt and adapt similar capacity, load and yield practices within their revenue operations. For digital publishers: Revenue capacity is the number of impressions for sale on a monthly basis. Load is the percentage of impressions sold. Yield is the average revenue per [...]
by Matt Shanahan on January 4, 2011 in ARPU, Digital Revenue Optimization
Posted by: Matt Shanahan In Q4, Scout Analytics published research that showed publishers on average have 20-30% untapped revenue potential with their existing users. How attractive is that untapped potential? To examine that question, we did further analysis on the relative value of those revenues compared to increasing sales volume or cutting sales expense. In each scenario we modeled, increasing average revenue per user (ARPU) increased profits more than the other tactics. While each publisher’s model will vary slightly, here is an example scenario to illustrate the point. Let’s assume a publisher has a $100M online business with a 15% profit margin and 40% cost of sales. In this model, the publisher is generating $15M in profit annually. Impact of [...]
by Matt Shanahan on November 17, 2010 in Digital Revenue Optimization, Subscriptions
Posted by: Matt Shanahan This is the second in a series on the importance and use of unit cost of engagement. In paid content, a pricing disparity is defined as a subscriber paying too much or too little for the content compared to their peer subscribers. Pricing disparities are often hidden because fees are charged based on the contract period or quantity of users neither of which account for actual consumption (i.e., engagement). Because subscriber value is directly correlated with engagement, the unit cost of engagement can be used to uncover pricing disparities and opportunities for digital revenue optimization. One of the easiest ways to visualize pricing disparities is to plot each subscriber according to their subscription fee and their measure of engagement during the term [...]
by Matt Shanahan on November 16, 2010 in Digital Revenue Optimization, Subscriptions
Posted by: Matt Shanahan Last week, Scout Analytics announced research concluding that aligning engagement and revenue provides a 20-30 percent uplift potential for paid-content publishers. After fielding a few questions on the research, I thought it might be worthwhile to explain some of the methodology here in the blog. This blog entry will address the following questions: What is engagement? What is unit cost of engagement and how do you calculate it? Why is calculating unit cost important? First, what is engagement? In paid content, engagement is consuming content (e.g., reading an article, downloading a report). Engagement can be measured at an individual level (e.g., number of articles read by a single user) or at an organizational level (e.g., number [...]
by Matt Shanahan on November 16, 2009 in Digital Revenue Optimization, Firmographics, Subscriptions
Posted by: Pete Horadan So what is knowable? The health of the recurring-revenue business really revolves around a full understanding of customer demand, so the critical metric needed is a measure of that demand, or a demand rate. There are three, interrelated components of demand—usage (session data), contract data (terms of the customer relationship) and firmographics (the demographics of an organization.) Usage data, in the subscription world, describes what is consumed and happens over time. It’s not about a single user visit, but about aggregated groups of users on different devices, at different locations. It’s a highly dynamic longitudinal view of the customer, so logging and measuring it is tricky. Contract data is the information about the business relationship—what licenses [...]
