Posted by: Matt Shanahan

The New York Times reported their second-quarter results on July 22nd.  One topic of conversation on the analyst call was the metered model under development.  Another topic was Internet revenues — specifically advertising.  It sparked a question for me, about what the conference call might sound like in July 2011 with a metered model in place.  How would the ad and subscription revenue compare?

I decided to do some of my own analysis.  The goal here is not to replace Alexia at J.P. Morgan, rather to examine the impact of a combined subscription and ad revenue model.  To model the New York Times metering, I needed to develop some assumptions: number of readers, revenue per reader, and subscription revenue per reader.  With that data in hand, I could look at 5%, 10%, and 15% subscription conversions and get a high-level assessment of impact. 

Between the press release and the call transcripts, the News Media Group did $50.4M in advertising revenue during Q210.  This is split between nytimes.com (20M uniques/month – 80% of visitors) and boston.com (5M uniques/month).  Assigning 80% of the revenue to nytimes.com, the site did $40.32M in ad revenue during Q210 (no subscription revenue currently).  This puts the revenue per reader at $2.02/quarter or $8.08/year (NOTE: erroneously, I tweeted a higher number earlier today).

From recent news reports about New York Times reader surveys, my analysis assumes an incremental $15/month in revenue per reader for subscribers.  This assumption is the lowest figure presented in the survey.  The final could obviously be a higher or lower average increment per subscriber, but my choice was to use the lower bound of any publically stated number from New York Times.

Properly dialed in, the metered model does not materially impact number of unique readers.  It should on the other hand, monetize the most loyal readers.  So what would happen at 5%, 10%, and 15% subscription rates?  Here is a summary of the current baseline:

Here is the incremental impact of a metered model. Note in particular the impact on average revenue per reader per year to increase the topline.

In the scenarios painted here, the average revenue per reader increase by 2-4X.  The above scenarios do not take into account any possible changes in CPM which could come about either due to better targeting or better ad units.  Nor does the analysis include any affiliate network revenue associated with bloggers to drive traffic.  So in addition to the subscription revenue other revenue sources could continue to boost the topline.

Caveat Emptor
This was a quick and dirty analysis.  For example, The 20M used as the reader count is the average per month is not precise.  It is certainly the case that some readers are only drive-bys and therefore the actual readers for the quarter is higher than 20M by some percentage.  That being said multiple drive-by readers equal the effect of one normal reader so the 20M average seemed reasonable. 

Another example is that the revenue of nytimes.com and boston.com are probably not split 80/20, but that is the best guess I cold make.  It should be within the ballpark.

And finally another example is that bloggers may be some of the most loyal readers but may be unlikely to pay.  The New York Times may run a special “blogger” program as part of an affiliate arrangement and not charge them.  This would lower the number of conversions but likely by a small percentage.

The point of the exercise was not to create a perfect model but to show the merits of the metered model can be substantial if appropriately leveraged.

Metered Model, Revenue Optimization Metered Model, Recurring Revenue Optimization

Posted by: Matt Shanahan

This week, Marion Maneker wrote a thoughtful piece called “The Case Against the Case Against Paywalls.”  In it, he examines the economics of abundance and scarcity in the media space.  Well worth the read.

Revenue Optimization, Subscriptions Recurring Revenue Optimization, Subscriptions

Posted by: Matt Shanahan

I’ve recently been hearing a lot of chatter about scarcity in the publishing world. If you listen to studies (e.g., Pew), twitterers (e.g., paywall), and bloggers (e.g., Jeff Jarvis), manufacturing scarcity sounds impossible – even unethical. But in reality, publishers have the absolute need to manufacture scarcity to drive profitable revenue.
 
From the business ethics perspective, it is common business practice to manufacture scarcity for profitability.  Take a look at how the airline industry is finally returning to profitability after a decade of losses; most of this profit is generated because of the reduction in capacity (a.k.a., artificial scarcity).  Auto manufacturers often retain pricing premiums by limiting production (a.k.a., artificial scarcity).  In the entertainment industry, movie releases first go to theaters, then to purchase, then to rental (a.k.a., artificial scarcity).  Remember limited edition iPods?  All kinds of products have limited production/editions to create artificial scarcity.  Simply put, artificial scarcity creates profitable revenue.
 
In terms of the viability of the idea in the publishing world, of course publishers can manufacture scarcity.  While scarcity based on distribution (e.g., print) is gone, manufacturing scarcity in the digital world is not impossible, only different.  Kevin Kelly’s blog post, Better Than Free, speaks to eight value generating qualities for manufacturing scarcity on the commoditized web (good read although he overlooks scarce content). It supports the idea that scarcity must now be based on differentiated value to the audience.
 
Here are a few quick examples of both B2C and B2B publishers that manufacture scarcity.  Consumer Reports has always relied on the limited availability of their content to generate profits.  BabyCenter creates a unique experience through personalization of content to match the stage of pregnancy and throughout childhood.  Rolling Stone is leveraging its archive to create scarcity and new revenue.   TechCrunch and GigaOM are good examples of building revenue from physical events that complement their content.  The FT’s use of a paywall shows how scarcity can be dialed in for specific audience segments.
 
Benchmarking other publishers, experimenting with user experience, and evaluating paywalls are some options for figuring out how to create differentiated value (i.e., manufacture scarcity) and drive profits. Scarcity is a concept that we all need to get comfortable with.

Advertising, Revenue Optimization Advertising, Recurring Revenue Optimization

Posted by: Matt Shanahan

My last post got me thinking about a mantra I frequently hear in the adtech community.  The mantra is “advertisers have all the money.”  It’s a bit distorted.  It is like saying, Ford doesn’t have any money only their consumers do.  This mantra affects everything from venture investments (e.g., emphasis on demand side vs. supply side platforms) to business models (e.g., revenue shares rather than fixed costs).  In particular, the revenue share philosophy has created some toxic business model structures that sooner or later need to be fixed. 

To illustrate the issue, imagine a manufacturer that needs different software systems to produce cars.  Those systems might be computer-aided design, factory order management, and supply chain management.  Let’s suppose the manufacturer is currently using CATIA, SAP, and I2 to perform these functions with each of systems vendors taking a percentage of revenue from each car sold (i.e., revenue sharing).  At steady state, everything is working because prices and demand are stable and they are making a profit.

The manufacturer finds out about a new solution for quality control which could help improve their product.  To implement the new solution will require another revenue share decreasing the margin that can be made because prices are fixed by a competitive market.  Overtime and because of other needed solutions, the manufacturer enters enough revenue sharing agreements to drives their margin to 0.  That is what revenue sharing does when applied to non-revenue producing activities.

The source of profit and margin for manufacturers lies in production.  Manufacturers are successful when they can manage their margins on the production side (i.e., fixed costs).  The model is always to have fixed costs in development, production, and operations and revenue sharing (a.k.a., commissions) associated with sales and marketing contribution.

Publishers are manufacturers.  They manufacture impressions from audience members that can be purchased by advertisers.  Revenue sharing on solutions such as order management, ad servers, verification, and others to help produce those impressions does not make sense in the long run and ultimately makes the business model toxic. 

Revenue sharing does make sense if the publisher has outsourced their ad sales and are paying commission to the channel for revenue delivered.   Even there the incremental contribution has to be clear to make revenue sharing viable.

Profit potential exists from the production of impressions.  Optimized profit in the publishing model will come by making sure to split where to incur fixed costs for production and use revenue sharing in sales.

Advertising, Revenue Optimization Advertising, Recurring Revenue Optimization

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 were purchased, when. It includes information regarding the number of users that are licensed, the cost of those licenses and the time period that is covered. 

The final component—firmographics—is what is knowable about your customer.  Information such as the size, growth and location of the company are all included in this realm. Firmographic information come from public sources such as corporate websites, private sources such as Hoovers, and is increasingly available from social networking sites such as LinkedIn. 

Combining these three sources of data is where things get powerful.  Using firmographic data to create segmentation and creating a ratio of actual to contracted usage, a customer demand rating can be established.  As discussed by Matt in his Moving Target series and according to a new Forrester survey, determining a willingness to purchase is becoming a critical key to success for content providers.  With a quantifiable, comparable measure for fulfillment of customer demand organizations can produce an actionable, revenue opportunity for each customer.  But that’s a topic for another entry.

Firmographics, Recurring Revenue Optimization, Subscriptions Firmographics, Recurring Revenue Optimization, Subscriptions

Posted by: Pete Horadan

Last week, Matt reflected on how the changing business environment has made customer demand metrics derived from usage data mission critical.  So I thought that I’d pick up where he left off in terms of what is missing from current web analytics.
 
Web Analytics one piece of the puzzle.  Web analytics are an importance piece of the analytical puzzle.  Through logfile, page tagging and click analytics technology, organizations can get a good understanding of the popularity of the content they are providing to their customers.  Hits, Page Views, Visits, Sessions, Bounce Rate, Click-paths  and % Exit all give insight into the product offering itself that helps organizations refine them. 

For product managers, web analytics are like an automated, ongoing and real-time usability test for their product offering.  They can sit behind the mirrored window and watch how the world navigates their site, see which pages are useful and which are not, and identify when users give up and go home.  Web Analytics give product managers a critical measure of the popularity of the content that is provided, but Web Analytics, while critical to online businesses, only tell part of the story—providing providing information about the offering, but not about the customer himself.

Content to Customer About Face.  Subscription analytics is the rest of that story.  More like a marketing focus group than a product usability test, it answers the questions the marketing and sales team would ask such as:

• Is this user from a large company or a small one? How many companies in the world are like this one? How does this segment of customers perform against my other segments?
• What percentage of my customers is using my product?  To what degree?
• Are all of my customers users licensed? Is this customer at risk?  Ripe for Up-Selling or Cross-Selling?
• How active is this customer versus my other customers? Is this customer revenue and/or usage growing or shrinking? More than my other customers or less?
• How does my opportunity in this company compare to opportunities at my other customers?

Subscription analytics utilizes usage data to provide new insight into the customer dependence for a product.  It’s not about understanding the content; it’s all about understanding customer behavior.

Behavioral Analytics, Recurring Revenue Optimization, Subscriptions Behavioral Analytics, Recurring Revenue Optimization, Subscriptions

Posted by: Matt Shanahan

Measuring demand has moved. The broad transition from product offerings to service offerings has driven the need for new means of measurement.  In a classic product business, where product delivery is a discrete event, the health of the business can be accurately determined through the analysis of purchase and fulfillment data.  But in a service business, with the offering delivered over time and on-demand, it’s not nearly as simple.   In a service, the purchase behavior is decoupled from the fulfillment process, introducing a whole new set of longitudinal variables into the equation and making purchase data as the single indicator of customer demand, obsolete. 

The elemental question shifts from “Was it shipped?” to “How is it used?” and with this shift usage data and the new analytics associated with it comes to the forefront.  This new category of behavioral analytics doesn’t revolve around purchase patterns, but instead determines the demand of the service provided through customer usage patterns.  Termed Subscription Analytics, this new set of customer demand metrics gives organizations insight into demand for the service from patterns of use. 

Subscription Analytics answers a new set of questions for organizations:   How does the size of my customer affect demand? Has demand changed over time? What other customers have similar demand? Is there untapped demand such as shared subscriptions? Where is a subscription underutilized? In danger of churn?   With Subscription Analytics, the answers to these mission-critical questions on demand are now knowable. 

In today’s service-oriented world, determining demand is no longer as simple as measuring and analyzing purchase data, and success is no longer as good as your crack salespersons intuition.  Customer demand is hidden inside the usage data and Subscription Analytics is the new requirement for determining it.

Behavioral Analytics, Recurring Revenue Optimization, Subscriptions Behavioral Analytics, Recurring Revenue Optimization, Subscriptions