Entries by Albert Sun

Some classmates of mine at Penn recently finished a class on Pricing Strategies in the Marketing Department taught by Professor Z. John Zhang who studies such things and they’ve written a paper named “From Print to Portal: Pricing Strategies in the Online News Realm.”

They’ve kindly given me permission to post it online and share it so go ahead and check it out here. (PDF Link) They give a history of the topic and discuss what many companies are doing now. In the conclusion they suggest that news sites should adopt hybrid subscription models.

The paper is a good qualitative treatment of the subject and a fresh take from some people not personally invested in the subject. This was a final paper for the class, and from what I know, none of the five team members have ties to or have worked in the industry.

I am officially a graduate of the University of Pennsylvania.

This infographic I made for the DP does a fair job of summing it up.

A Mixed Bundling Pricing Model for News Websites

Abstract: This paper outlines a method for finding revenue maximizing mixed bundling prices for news websites. This can help better understand paid content strategies for online news content. Drawing on work in the field of bundling information goods, I apply a two-parameter model of consumer preferences to web site traffic data and a roughly estimated willingness-to-pay curve. We can then calculate revenues for different price points and find the optimal one for any given site. This method is applied to a sample of ten sites. At revenue maximizing prices, the majority of paid revenue for these sites comes from the sale of individual articles, rather than subscriptions. Site traffic showing highly loyal consumers is found to correlate with higher subscription prices. This model suggests that while it is possible for overall revenue to be higher with a paid content plan, total traffic will certainly fall.

It can be found online here in PDF form.

I’m mostly happy with the way it turned out, though there were a lot of compromises and broad assumptions needed to bring it to a finished product. There’s so much interesting material in this field, I wish I could spend a few more years studying it. I guess that’s what graduate school would be, if I ever decide to attend.

Special thanks go out to Aleks Jakulin for supporting and encouraging me in this work.

I don’t post here nearly as much as I should because I’ve set a precedent of long posts that take a lot of effort and I don’t want to muddy up the stream with little stuff. I know I’ve also promised posts that I haven’t delivered on. They’re coming (I hope).

But meanwhile, I will blog in short-form, and a little more personally, at http://albertsun.posterous.com/ to keep things flowing.

After the New York Times announced its metered paywall last week there has been a lot of empty blather. Standing out from all the noise are two very good analyses. The first was by Felix Salmon for Reuters, analyzing a consumers decision of whether or not to pay. The second one was by Jonathan Stray on Nieman Lab, showing the effect of several different variables on revenue.

This stuff is right up my alley, and I’m currently working on a senior thesis in the field and so I’ll try to extend Salmon’s analysis a little bit. Later on, I’ll take on Stray’s model as well.

Salmon’s Analysis

Let’s say a reader in a given period reads articles from the New York Times. Then suppose the New York Times sets the paywall after a consumer has read some articles. In order to read the article, the reader must pay a fee of . If is the value the reader gets from each article, then he will only pay the fee if . This is a good simple model synopsis.

Article Values are Different

Let be as before. The first issue that jumps out is that the value of any given article is not constant. The value of articles over a period varies, so let’s arrange them in order of value from highest to lowest.

Let be a monotonically decreasing sequence of article values for our reader, with . Then the reader gets value,

The reader would clearly choose to read the articles he values most first, and after that only pay the subscription if the rest of the articles he has yet to read are still valuable enough. Only if will the reader pay the fee.

But this is not quite right either. There’s no way for a reader to know ahead of time which articles are most valuable to him.

Predicting future value

Now, instead of ordering the values of articles from highest to lowest, let’s say that the value of articles our reader reads are drawn independently from a probability distribution. Let the value of articles be a random variable with a normal distribution and the average value of an article. are the value of the first article read, second article read, etc.

Let the period of time for which the reader pays be represented as , and the moment when the reader has read free articles and must choose whether or not to pay the fee be at time . Assume the reader reads articles at some constant rate throughout the entire period. Then .

Now the reader must predict what the value of articles he will read will be to determine whether or not he should pay the fee. Up to point , he has gotten value and average value per article of . is also the sample mean of the distribution.

Result

Our reader will choose to pay the fee if . As goes up, so does and as goes up, goes down.

There are some interesting suggestions from this. When the New York Times imposes the paywall, they should carefully monitor the rate at which people read its articles. Those that have a low rate would be ideally suited for targeted discounts. Also, since readers make their predictions based on past articles they’ve read, the ideal time to convert non-paying readers is right after a reader reads a series of good articles. If the Times can be subtle about dialing up and down , then they can exploit variance in article value to increase sales.

Further work

This analysis is of course still incomplete. Problems I still see with it.

• Knowing that you’ll only get a limited amount of articles for free will change a reader’s behavior. If they’re still uncertain about whether or not paying the fee will be worth it, they will more carefully pick which articles they read before time t. This will bias upwards, but push downwards. At time , there will also be a back-log of articles that would have been read but weren’t influencing the decision of whether to pay or not.
• How will the reader decide whether or not to read an article before time ? He’ll have to depend on the headline and a summary if available to make a prediction. Before actually reading the article, the reader will predict some value and after reading the article realize some value . This average spread will likely affect predictions of future value.
• As is, the model says decreasing and increasing leaves the reader’s decision of whether to buy unchanged. But as this becomes a strict paywall, which the gut says people would be less willing to pay for. Another factor in the reader’s decision of whether or not to pay is their confidence about their decision. The larger is the more confident they will be about their value prediction since the sample mean’s standard deviation will fall, as .
• Paywalls, as described by the New York Times and as currently implemented by the Financial Times and WSJ, are easily bypassed. This can be done either by spoofing the referrer header, or by clearing cookies. This avoidance could also be modeled in in some way.
• Letting people in for free if they come via social media or links from other sites screws everything up. I think this may turn out to be such a huge gaping hole in the paywall that they severely restrict it, but if they don’t there are several ways it can be modeled.
  You could divide articles between different distributions of those that are primarily found through social media and those that aren’t. The reader would choose whether or not to pay based on the value of those that aren’t. Alternately, an article’s ability to be found through social media could just affect its .
• Print subscribers get free access as well. In Salmon’s post he looks at , the difference between print subscriber’s fee and online subscribers. If this is less than the value of getting the print paper then the reader will choose the print subscription.
• What if users can choose between a short period, and a longer period with a discount? What does the renewal decision look like?

There are undoubtedly more things that can be done with this model. One of the most obvious is to try and figure out what and should be set to.

Finding good values for F and n

Since it’s reader’s will not have the same distribution for it would be theoretically ideal to pick values for and individually for every reader. Realistically, the New York Times probably shouldn’t be that opaque about their pricing as it would cause confusion and a negative reaction among readers.

If forced to pick a single price, it would be necessary to find the average value of articles for all readers. That’s what Stray did with his paywall simulation. However, part of the reason that simulation has such wild swings in revenue from relatively small changes is because many of the variables are dependent on each other. For example, the percentage of people who pay for a subscription does not stay constant when or change.

I’ll tackle this issue more in my next post.

Special Bonus! A pricing algorithm for the FT

This part might still be a bit half baked, but working backwards from the consumer’s decision, it seems possible to figure out a demand curve for each individual piece of content if enough data is available. Since the Financial Times already has a metered subscription plan, if they’ve been good about collecting user data they should have what’s necessary to do this. Here’s an outline of the method.

It requires some change of notation from the above.

Let be an article, and be a reader. We will now represent the value of an article to a reader as a mapping with representing to the value of article to reader . The functions and replace and as the fee and rate for reader . is as before.

Define the set such that reads before deciding whether or not to buy.

So our former equation becomes .

Rearranging, we get .

The left side of the above equation is the average value of an article that a reader reads before making the buying decision. So if does buy a subscription, we then know that the average value was at least the right side.

Now that we have an estimate of a given readers average value for content we want to estimate that value across all readers. For any given piece of content, some fixed , to determine its value we sum the average value for content of all readers who read before purchasing, and then divide by the total number of readers (who aren’t already subscribers) who’ve read .

Define, .

Equivalently, .

This function is an estimator of the average has for an article.

Now define the set such that reads before deciding whether or not to buy a subscription. This set is all non-subscribing readers that read article in the current period, whether or not they’ve ultimately paid for a subscription by the end of the period or not.

If we take for each in the set , we have a distribution of estimated values for article . That might look something like this.

Finally, to come up with a set value for a specific piece of content, we sum over the entire set and divide by the number of readers.

With this value, you can now derive a demand curve for the entire site. Or you can dynamically set prices based on what articles a reader has viewed before hitting the paywall.

Exciting stuff, if actually implemented.

If you think I’ve screwed up the math in some way, or if anything isn’t clear, please please let me know. The thoughts in this post are still very much a work in progress.

Hope everyone has had a good holiday! Did you buy gifts for people? Or receive them? From a pure economic efficiency perspective you shouldn’t spend a second on holiday shopping and just give those around you cold hard cash. After all, lump sum transfers are most efficient in redistributing wealth, and there’s no way you could know a recipients preferences better than they do. (Exceptions for parents who’ve received letters to Santa of course)

Unfortunately this is empirically very unpopular (in the US at least). As Greg Mankiw explains, it’s about signaling.
Social interactions aren’t just about exchanges of economic value, they’re an intricate dance of signaling to others, posturing to others, projecting an image, fitting in with the cool crowd, and all manner of things a high-schooler would be embarrassed to admit to.

The content we consume becomes part of that intricate social game of posturing and positioning that we all play to navigate our social spheres. And luckily for content publishers, this all can be exploited.

Social Content

2009 may be remembered as the year that Facebook and Twitter topped the search engines in sites’ referrer logs. Increasingly, people are turning back to their friends and acquaintances to point them to accurate information, interesting news and entertainment. Search has become the victim of spam and gaming of algorithms, leading to online social spaces becoming a more reliable way of finding what is good online. Fred Wilson gives a clear summary.

For me, social has become the definitive content discovery mechanism. As this happens, content begins to look less and less like a commodity. In search engine land, content is ruthlessly and algorithmically ranked and rated in value and relevance. No one has a personal relationship with a search engine.

Information with social context becomes part of our relationships with other people. And no one is better at persuading us to do things we might otherwise not do than our friends.

For more evidence that social interactions destroy rational economic thinking, one need look no further than luxury goods that people consume and enjoy more simply because they are expensive and can signal to others that they are of high status. Demand for these goods actually rises as they become more expensive. Or take the vicissitudes of fashion, which require people to swap out their wardrobes on a continuous basis in order to convey their social status to others.

Applications

How to tap into this economic irrationality? Get people to consume goods in a social space that displays their consumption to others. People do this on the subway by holding open the covers of their magazine or book. People do it online by sharing links to their favorite sources and stories.

More specifically. Publishers could erect a paywall on a site that takes advantage of these social tendencies by giving paying subscribers the ability to share content by whisking their friends and followers past the paywall. With customized links, or a custom link shortening tool that meters the number who use the link it would be possible to set up multiple tiers of sharing and charge more to let someone share with more people.

This also lets people band together and buy subscriptions. It sounds bad for the bottom line, but in the economic literature, its been shown that allowing this kind of sharing, can actually improve profits for a content distributor. What causes this counter intuitive result? In a paper by Bakos, Brynjolfsson and Lichtman, they explain that this kind of social sharing works to aggregate widely distributed consumer valuations for content and present a more favorably shaped demand curve to the producer.

Content producers continue to struggle with monetizing their products online, and it looks increasingly unlikely that advertising will be the solution because content sites operate at the level of “intent generation” not “intent harvesting”. Perhaps by making the consumption of content more of a social experience, producers will have more success.

I’ve so far refrained from commenting on the Rupert Murdoch de-indexing comment and ensuing brouhaha. But Google’s recent policy change throws everything into the air.

Whether you like or dislike him, it’s time to stand up and recognize that Murdoch’s threat to pull News Corp sites from Google’s index has worked brilliantly.

Publisher unrest and the threat of a Bing deal and serious search engine competition on site indexing have pushed Google into a major concession. Google’s change to its First Click Free guideline is a bigger deal than many people realize. What appears to be a simple change in degree is actually a change in kind.

Google has now said that its okay with sites showing different content to its crawler than to a human following a search results link. There is no longer a guarantee that what shows on a search results page will actually be on the destination page. The Google search user experience will suffer slightly and publishers will now find it much easier to run a pay site.

A Little Background on First Click Free

I’ve seen First Click Free described by some bloggers as a Google “program” or “service”. It’s neither. It’s more accurate to call it a guideline or policy. Google has always taken a strict stance against “cloaking”, or showing different content to its crawler than to a human visitor. What First Click Free said to publishers of paysites was in effect that they had three options. (1) Opt out of indexing at all. (2) Let the crawler index all content, but direct a human reader to a sign-up page, and risk the wrath of Google, which could include de-indexing or ranking penalties. (3) Implement First Click Free, and check all incoming requests to see if they’re the Googlebot or have a Google referrer and show them the content for free. (This is what WSJ.com implemented)

Now with “First Five Clicks Free”, Google has given sites permission to not show a user the same content as the Googlebot sees after their fifth click.

First Click Free Created the Leaky Paywall

I had never understood the complaints about search engines “stealing” content that emanated from the top of News Corp. If anything, search engines were providing free advertising and new visitors to convert to paying subscribers. Pulling their sites from Google’s index wouldn’t hurt Google, and it wouldn’t help the site either. I thought that the Journal was allowing visitors from Google past the paywall voluntarily to increase traffic. Now its clear that the Journal was choosing between maintaining the loophole and violating Google’s rule against cloaking and risking losing Google derived traffic. In that context, the ire directed at Google makes much more sense.

Editors and staff at the WSJ are well aware of both the power of Google to drive traffic and visitors to the site, and the degree to which people were using it to circumvent their paywall. Every morning, an email report goes out to editors and staff detailing what search keywords were driving traffic to the site and what stories and trends are hot online. During my internship, compiling and writing this email report was one of my responsibilities. Visitors searching for the exact headlines of Journal stories often ranked among the top sources of Google referrer traffic.

That Google has so clearly and quickly reacted, means that some negotiating power is returning to the big publishers. Five free clicks per day is still probably too many to make them happy, though. But the more search share Bing gains, the more leverage publishers will have.

Predictions for the Future

I predict that we will soon see a future where major publishers will let search engines see and index the full text of a story, but show just a teaser and a “Purchase” button to users. In fact, paywalled sites could try it now, if they feel like playing chicken with Google. Would they actually follow through and penalize a sites ranking or de-index it? Especially for a site like the WSJ.com, it’s plausible that doing so would noticeably hurt the quality of web and news search results. If Bing doesn’t penalize a site for doing so, will their results look better in comparison?

By explicitly ignoring Google’s guidelines, publishers would throw the ball back into Google’s court to see how they’ll respond. “First Five Clicks” is a sign that Google may cave on this. My advice to Rupert Murdoch would be to patch that hole in the WSJ.com paywall (give away maybe one free click per day) and see what Google does.

Despite all the recent talk and speculation over whether content web sites should adopt a pay for access model, surprisingly little attention has been paid to the underlying economic theories behind any such move. Few new papers on the economics of digital information goods have been published since the late ’90s.

Many of those papers remain surprisingly relevant today and anyone interested in the field would do well to go back and read them. I’ve been doing just that recently in preparation for my senior economics thesis.

One particularly good paper was written in 1999 by John Chung-I Chuang and Marvin A. Sirbu, two professors of Engineering and Public Policy. The paper, “Optimal Bundling Strategy for Digital Information Goods: Network Delivery of Articles and Subscriptions”, provides many interesting insights into recent discussions of the topic. In it, the authors come up with an optimal pricing model for access to academic journals online. Their method applies just as easily to news articles and websites.

Results

The first conclusion of the paper is that the optimal strategy is always to offer both site wide subscriptions AND a micropayments plan for sales of individual articles. This pricing strategy will (with reasonable assumptions) always be more profitable than solely offering one or the other.

A second indirect point is that visitor loyalty will determine not only how many visitors can be converted into paying visitors, but also what proportion of revenue will come from subscriptions versus individual sales. The more loyal visitors are, the greater the fraction of revenue that will come from subscriptions.

Sidenote: This paper assumes that the publisher is acting as a monopolist. The publisher’s offerings must be sufficiently differentiated from competitor’s products that consumers will not switch, and any switching that does occur is not taken into account. If switching does occur, then the assumption is that a competitor offers a similar mix of products. Thus, the proportion of subscription versus individual sales revenue does not change.

Theory

The theoretical underpinnings of this paper are in bundling theory. In bundling, we examine the problem of a publisher offering multiple goods (articles). A consumer places some value on each article. If the price of the article is below the value they place on it then they will purchase it. Likewise, for some bundle of articles, if the value the consumer places on the sum of their individual valuations is less than the price of the bundle, they will purchase it.

Over a subscription period where a publisher produces N-articles, then there are 2^N different sub-bundles to sell. These sub-bundles could include content grouped by category, author or any other distinguishing feature. However, it is extremely computationally difficult as N gets large. To simplify things, only the entire bundle, and sales of individual articles are considered.

Customer Preferences

In addition to these two conclusions, the paper also illustrates how little hard data is available in this field with which to do research. Their model describes the preferences of consumers as a distribution on two factors. Willingness-to-pay and percentage of articles valued. Their willingness-to-pay for their most valued article and the percentage of articles with non-zero value. Without hard data on the actual value readers place on articles in journals or on news websites, the study assumes a uniform distribution. Data on percentage of articles with non-zero value comes from a study by researchers King and Griffiths showing the distribution of the number of articles read in a Journal.

A good analogue to this survey data for content websites would be to use traffic data on visitor loyalty. How many pageviews per unique visitor does a site have? What’s the distribution of this statistic? Nielsen Online has begun to put together a new statistic for newspaper websites, session per user per month.

Pricing Strategy

Based on the data for academic journal readers, the authors calculate that the optimal price for a subscription should be approximately 10 times greater than that of an individual article. With this pricing strategy, the content producer’s revenue stream is well balanced with 56% from sale of individual articles and 44% from that of subscriptions.

Some publishers have started to speculate that their best hope of monetization may be with their most loyal visitors and not with ever higher traffic numbers. Still, much is up in the air.

A recent BCG survey begins the task of gathering the necessary data to make intelligent decisions about whether or not to charge for news. Still plenty more to do though.

More Data Needed

To apply a model similar to Chuang and Sirbu’s to news websites two datasets are required. One, is the survey or experimental data needed to find the distribution of consumers’ willingness-to-pay for a specific article. The other is a data set on visitor loyalty. If anyone knows of an existing data set for either of these, please let me know. I’m also in the process of gathering these data sets. If you want to share analytics from your news website to help my research, please let me know too.

I’ll be summarizing a few more of the papers and studies in the field and looking at other theoretical pricing models for digital content.

The two sites that are constantly cited as success stories for paid online subscriptions are the Financial Times and the Wall Street Journal. What do these subscription systems have in common, other than a large base of paying users?

Both are stupidly easy to circumvent.

For WSJ.com, simply copy the headline of the paywalled article you want to read into Google and hit search. It’ll pop up as the first one and by following a search engine link, you skip over the paywall. For ft.com, once you’ve hit your limit, just clear your cookie from the site and keep on reading.

But despite how easy it is to get free access, lots of people pay anyways. They’re paying for the convenience of not having to use these work arounds. Where else are people willing to pay for convenience? Look at the proliferation of paid iPhone apps. Many (especially Twitter apps) just provide a nicer interface. Convenient.

When creating a premium product, make it more conveient and easier to use than the free one.

What would make a site more convenient? Alternately, what irritates you about a website currently that you might pay to avoid?

Since my post on price discrimination for newspapers has drawn some attention, a few people have responded with the argument that news online must inevitably be free because firms maximize profit by setting price equal to marginal cost.

And since the marginal cost of distributing one more unit of news through the internet is essentially zero, news should be free.

In my last post I looked almost exclusively at the demand side of the equation. This time I’ll look at the supply side a little bit.

Marginal cost pricing is not a trivial objection to charging for online news, and I used to be firmly in the “information wants to be free” camp. But something clearly seems wrong about that. Information is so valuable I just can’t imagine that it could all be free.

1. Infrastructure Costs

Marginal cost isn’t quite zero. Most estimates say that Google is losing hundreds of millions of dollars a year running YouTube. The IT infrastructure to run a complex or high traffic site costs a lot of money.

2. Pricing Power

Profit maximization only makes price equal marginal cost if firm’s don’t have pricing power. (i.e. they are in perfect competition). And while a lot of news content might be in perfect competition, most people still have strong preferences about which publications they do and do not like. By widening the gap in perceived quality and value, publications can make it easier and easier for themselves to charge for content.

3. Measuring the Wrong Thing

Most significantly, number of pageviews isn’t the right quantity to consider. The right quantity should be some abstract measure of how many pageviews can be attracted to the site. What’s the difference?

For most goods economists look at, the company can produce and sell identical copies and as long as they keep reducing the price, people will keep buying more to more and more people. With news articles, that’s not true. No matter how cheap an article about something someone don’t care about is, they won’t buy it. And no matter how cheap a second copy of a news article someone has already read is, they won’t buy it.

When someone makes the argument that the marginal cost of digital content is zero, they are thinking of the marginal cost of one more person reading the content, one more pageview of a website, or one more copy of a single piece of content being distributed.

But that’s not at all the supply side decision being made by media companies. A media company will decide how large a staff to hire, and that in turn will determine how much content is produced each day. Cost has labor as an input, and the marginal cost curve is the standard J-shaped curve.

In turn, the more content produced and the better that content is, the larger an audience can be attracted that will want to view it. The first pageview is easy to get, the millionth or the ten millionth is much harder and more expensive to get.

Instead of looking at the price of distributing one more copy of an article, we look at the price of creating content that will attract one more pageview. (Either more content, or better content — so that either more people will read, or the same people will read more)

All of a sudden marginal cost isn’t zero. But what significance does this have?

Marginal cost above zero means there’s hope for paid content online! Since content has value to consumers AND the cost of supplying content is not zero, the equilibrium price of content need not be zero either!

It also means that content companies need to lose their single-minded focus on expanding audience. To make money from paid content, growing an audience can’t come at the expense of the ability to price the product.

I’ve been kicking this last idea around in my head for quite a while and I’m still not sure that I’m thinking about it the right way. It’s certainly not as rigorous as it could be, and I haven’t tried to draw up some mock numbers with it to see if modelling things this way will work. In the fall, I’ll be starting to research this topic more rigorously for my senior thesis.

I’m finally done with a week of long hours and hard work in, of all places, Bowling Green, Kentucky. I was regularly working past 3am but I think it definitely paid off.

This past week was the 2009 Dow Jones Multimedia Workshop at Western Kentucky University, with a group of seven other awesome journalists preparing for our internships all around the country. Having well qualified instructors makes such a difference when learning new things. (Turns out a bunch of things I had learned by trial and error were just plain wrong) At the same time I was with other journalists with quite different backgrounds and learned a ton from the people I was working with.

The core of the program was reporting and producing a full multimedia package. We divided into two teams of four and went out into the city to find and create a compelling story. This kind of project is a first for me as all the multimedia I had done before was spot news done with super quick turn around for the next day. Having the time to go in depth and produce a full story is much more satisfying.

The project my team made was about female tattoo artists breaking into the industry. The other team produced a piece on refugees and immigrants settling in Bowling Green. (Here’s where I wish I had had more time. Our site leans heavily on javascript and jQuery for navigation and I didn’t finish making the site cross browser compatible. It degrades very badly in IE. It only works perfectly in Firefox. And I couldn’t figure out blocking for XHR requests. If I find more time this summer, I might try and figure out those issues and turn the layout into a general purpose template for multimedia stories)

Besides the new skills I learned and the new people I met, I had a revelation about how people learn journalism. I’ve always thought that the best way to learn journalism was through hands on experience, but….

Through the whole project there was a strong tension for all of us between doing what we were already good at and doing what we wanted to learn. I spent most of my time on web design even though I wanted to learn photo. Our best photographer spent most of her time on photo despite wanting to learn video. And of course this is kind of natural because people gravitate towards what they’re good at. And there’s almost a duty to your interview subjects to do a good job.

But it also means that once you start going down a particular skill path you are committed and it becomes hard to branch out into learning new things.

Now that I’ve been to one workshop, I’d like to go to one where we have explicit permission to do a bad job so that we’re not afraid to experiment and take on roles that are new to us.

There’s been quite a debate between free and paid content but it’s in quite a sorry state. On one side (mostly old school reporters and newspaper execs) people think that publishers need to charge people for content, block everyone else out, and sue the pants of Google for profiting off indexing stories. On the other side (mostly techies and copyleftists) are free content idealists harping on the line that “information wants to be free.”

But both sides are equally wrong, or at least misguided. Pretty much every company in the world has different products at different prices, why do journalists think they should have just one?

What you really need to do to monetize content is to take a page from the airlines and freemium web services and every other company with a product ever and learn how to price discriminate. Not only will you make much more money doing it, you will produce much better quality journalism as well.

Offer your basic and commodity content for free. Then create high value premium content and charge readers a monthly fee for it. [1]

Alan Mutter has some good ideas for types of premium content you can create. Here’s the economics that underlies it.

The Demand Curve

We have a standard downward sloping demand curve [2] with each point on the curve indicates how much value a consumer is getting from the product. Now if a site gives away all their content, the only revenue they get is from advertising, and the consumers who get the most value from the content pick up a nice chunk of consumer surplus.

Point D represents the smallest amount of value a reader gets from your site. This is likely in the micropayment range, likely just a few cents, but the quantity consumed is also huge. It doesn’t make sense to mess around with micropayments, because the site can monetize their traffic with advertising revenue. For simplicity’s sake we’ll say marginal advertising revenue is constant for any quantity. [3] While you could try charging with micropayments, it likely wouldn’t work. The mental cost of deciding whether or not to pay and then paying is a hefty tax on the process. (I’d argue that trying to charge anything less than $10 at a time or so becomes counterproductive, but the smoother the payment process is the lower this amount can go)

How can you increase revenue? By capturing consumer surplus.

Price Discrimination with Advertising

We would like to set several different price points at A, B, and C to charge people different amounts based on where they are on the curve. Businessmen and politicians and news junkies get much more value from a news site than casual readers do, but how can we charge them a higher amount than everyone else? Airlines are the classic example. They do it with such a pricing schemes that make sure that you almost certainly did not pay the same amount for your ticket as your seatmate. [4] To do it for a news site, you’ll want to create varied products such that the product offered at price point A appeals to all the readers left of point A. Each of the points A, B, and C need to be different slices of unique premium content that readers will pay different amounts for.

A few more points to consider

• Judging Value — You need to be able to accurately judge the value of all your content and spend a good amount of time researching and calculating what the proper price points are. I suspect for high quality business news, sites could get away with subscriptions costing upwards of a hundred dollars per year. Things like high end wine or restaurant reviews also don’t make much sense as free content. If someone is spending a hundred dollars a bottle, they’ll pay for a review backed by a renowned brand name.
• Subscriber value versus site traffic — For any given piece of content, you have a dilemma. You can put it in the premium content pile and restrict access to it, or you can put it up for free. Each piece of content that goes into the free pile increases your reach and traffic, but slightly erodes the value your premium subscribers get and makes them more likely to switch. This is a delicate balance. One way around it might be to delay the speed at which non-subscribers can access content. So that masterful investigative piece might be subscriber only for the first week, after which its open for anyone to read. The kind of high quality, high traffic pieces you want to grow traffic will be the exact same pieces of content you want as premium content to get people to pay.
• Archives are terrible premium content — A lot of newspapers have their full archives online behind a paywall. Sometimes a very expensive paywall where per article access can cost close to $10. What audience is this supposed to attract? People who place enough value on old news articles (lawyers, academics, other reporters, students writing papers etc.) to pay for them, likely all have access to LexisNexis subscriptions already and won’t pay. And the average reader won’t care enough to pay for an article they stumble across. It seems like they’ve made a terrible subscriber value/traffic trade-off here.
• What am I buying? — Transparency and convenience are important. With your premium content make it explicitly clear what types of content and privileges a subscriber will get. Any uncertainty here means fewer paying customers.
• Piracy will be an issue — Remember that when digital content has a price, piracy is going to be a problem. Look at the troubles the movie and music industries are having. You’ll have to be willing to vigorously defend your copyright against sites which spring up and paraphrase all your premium content.

Notes

[1] DO NOT just repackage your current content as free content. It’s almost certainly not good enough. Thinking hard about what sorts of content readers will pay for, you’ll produce better content too.
[2] Different pieces of content have different value to different consumers. Together they form a downward sloping demand curve. A few consumers are willing to pay a lot for content, but this amount drops off quickly. On the right side of the graph, by decreasing the price towards zero, the quantity consumed can be expanded almost to infinity.
[3] In truth, marginal revenue from advertising is probably downward sloping as well, but the market for that is so much more inelastic than that for traffic that it doesn’t matter. That line should also extend all the way to the left.
[4] Nearly every economics textbook has an example about the price of airline seats to explain price discrimination. For them point A are the business travelers that need comfy seats and need to buy them at the last minute. Because the value they get from travel is much higher than average they are willing to pay much more. Point B might be the families on vacation, they’re willing to book well in advance but they might want their tickets refundable in case things change. But they’re also price conscious. If the ticket costs as much as at point A, they’ll just stay home instead. Point C would be the budget travelers and backpackers, who are willing to fly standby on whatever plane has a few empty seats. In the end they all get the same service, transportation from one point to another, but by varying the conditions and terms of each ticket, the airline is able to make much more money.

Looks like this post has gotten a bit of attention. If you found it interesting, you might also enjoy my earlier post 9 ways that newspapers can make money that aren’t advertising.

I’ve also given a brief talk on this topic at the Information Valet conference hosted by the University of Missouri’s Reynolds Journalism Institute. Martin Langeveld did a great write up of it for the Nieman Lab, making some of my points better than I did myself. A video is here.