In the last couple of postings, I have referenced a metric called “revenue per reader” which I picked up from a Newsweek article.  This post and future posts will refer to Revenue Per Visitor (RPV) as a more generic measurement and consistent with Nielsen, Comscore, and other reporting regarding uniques.  Interestingly, RPV is not new (Web Analytics Demystified definition and SAI Chart), but it has not been leveraged much in tracking performance.  The RPV metric measures the gross revenue efficiency of a publisher and reflects the fact that audience engagement is the source of ad and subscription revenue. 

TechTarget (TTGT) reported their financial results for Q2 2010.  They produced $25.1M in revenue which was 15% YoY gain, and they had net income of $0.5M compared to a net loss in the previous year and quarter.

In the May 2, 2010 BtoB Media Power 50, TechTarget reported that they had 16.5M uniques per month.  TechTarget generates revenue from display advertising to these 16.5M uniques as well as lead generation and event revenue from 8.5M registered members (note: the site reports 7.5M and 5M as well). 

The gross revenue efficiency for Q2 of TechTarget was $1.52/visitor/quarter or a run-rate of $6.08/visitor/year.

My goal in sampling these ratios of different publishers is to find where the number of uniques and revenue per visitor intersect to make a viable business model – more to come.

ARPU ARPU

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

I did a little math last night to look at the gross revenue efficiency of an FT subscriber vs. a HuffPo reader.  Currently, the FT has 149,000 individual subscribers.  Using the US price of $4.25/week (lower rate than the UK rate), the 149k subscribers generate $32,929,000 per year (the actual number is likely higher).  HuffPo is generating about 30M per year with an average of revenue of $1/reader/year.

Now which model is more efficienct?

The FT’s digital operations is on pace to generate about $200M in 2010.  This comes from the 149k subscribers, 2.5M registered users, and 1,000 corporate subscriptions – a base of somewhere between 2.7 and 2.8M readers.  In the HuffPo model, they need 200M readers or about 40% of the Facebook population to achieve the same number.

FT’s overall gross revenue is over 100 times better than the HuffPo model.  This is the power of the metered model.

Metered Model Metered Model

Posted by: Matt Shanahan

One of the hot topics with our customers right now is the metered model.  The metered model blends advertising and subscription revenue by recognizing some visitors have higher or unique demand for the content and are willing to pay for the right to consume it.

The metered model assumes an audience can be segmented according the the value they derive from the publisher’s content.  In the metered model, the publisher utilizes different pay points to package the content in the right monetization model for the right audience member.  These pay points can be the following:

• High Demand: Visitors can view a certain amount of content within a particular time period.
• Premium Content: Some content is free, other content is paid.
• Premium Services: Visitors pay for personalization, access to commenting systems, tools to share content or other services.
• Time-Based: For a particular time period after content is published, it’s available for a fee; later, it’s free.
• Location-Based: Visitors pay based on where they are. For example, international visitors may have to pay because their visits don’t provide ad revenue.

The metered model works well because it creates a personalized exchange of value between the publisher and the audience.  Audience members that don’t value the content as much (i.e., they don’t consume much) don’t have to pay for the occasional times they want it.  Paying audience members are the ones that consume the content frequently because it is uniquely suited to their preferences or needs.

The metered model also works well because it can be gradually dialed in.  The pay points can focus first on smaller segments of the audience with the highest demand or that use the most unique content.  Rather than creating an all or nothing paywall, audience segments can be identified, targeted, and monetized to create incremental revenue without creating revenue risks.

HuffPo is generating $1/year/audience member compared to $4/week/standard subscriber at the FT.  In part 2, I will run some math to begin understanding the lifetime-value of audience members in a metered model vs. an ad-only model.

Advertising, Metered Model, Subscriptions Advertising, Metered Model, Subscriptions

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

Twitter recently began a new service called @earlybird.  The service allows Twitter to deliver offers – think ads – directly to a user’s client.  People are proactively signing up to receive ads!  In the FAQ for @earlybird is the question “what if I’m only interested in offers for a specific category, let’s say fashion or music?.”  The response is “We’re thinking about you, too.”  The performance of advertising is high when you combine opt-in and intent.  Twitter understands that and will be asking the audience “what ads do you want?”

So why not do this for display advertising?  Personally, I like a good ad (e.g., Super Bowl ads).  It’s the bad ones that I would like to filter out.  If a publisher gave me an ad “tuner,” I’d use it.  If the ad tuner allowed me to opt in to skiing and my other interests, I would get value versus what I currently get.  Additionally, I would like to specify ads to be either funny or have great photography/cinematography.  But alas, I don’t have that control.

Yet, my predicament could be changing.  This week the NY Times published an article on Bynamite.  Bynamite wants to put control of advertising back in the hands of users (FAQ).  They want to become the control center of permission advertising on the Web.

@earlybird and Bynamite are examples of asking an audience what ads they want.  So why shouldn’t publishers do the same, getting a clear understanding of intent and improving response rates would seem like excellent foundations for ad revenue optimization.

Advertising, Attention Economics Advertising, Attention Economics