Archives for “paid content”

My Senior Thesis

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.

1 Comment. Posted by Albert Sun on Tuesday, May 4, 2010 at 9:34 pm.

Filed under Journalism, My Life, economics, paid content.

Economic Analysis of the New York Times Paywall

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 $N$ articles from the New York Times. Then suppose the New York Times sets the paywall after a consumer has read some $n<N$ articles. In order to read the $n+1th$ article, the reader must pay a fee of $F$ . If $v$ is the value the reader gets from each article, then he will only pay the fee if $v\left (N-n \right ) > F$ . This is a good simple model synopsis.

Article Values are Different

Let $n,N,F$ 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 $\{v_i\}_{i=1..\infty}$ be a monotonically decreasing sequence of article values for our reader, with $v_i = 1 \:\forall\: i>N$ . Then the reader gets value,

$u(v)=\left\{\begin{matrix}\left (\sum_{i=1}^{N}{v_i} \right ) - F &if\;\sum_{i=n+1}^{N}{v_i} > F\\ \sum_{i=1}^{n}{v_i} &if\; \sum_{i=n+1}^{N}{v_i} \leq F \end{matrix}\right.$

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 $\sum_{i=n}^{N}{v_i} > F$ 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 $V \sim N\left ( \mu,\: \sigma^2 \right )$ with a normal distribution and $\mu_x$ the average value of an article. $V_1, V_2, V_3,\cdots$ 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 $\left [ 0,1 \right ]$ , and the moment when the reader has read $n$ free articles and must choose whether or not to pay the fee be at time $t\in \left [ 0,1 \right ]$ . Assume the reader reads articles at some constant rate $r$ throughout the entire period. Then $t= \frac{n}{r}$ .

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 $t$ , he has gotten value $\sum_{i=1}^{n}{V_i}$ and average value per article of $\overline{V}= \frac{\sum_{i=1}^{n}{V_i}}{n}$ . $\overline{V}$ is also the sample mean of the distribution.

Result

Our reader will choose to pay the fee if $\left ( 1-t \right ) r \frac{\sum_{i=1}^{n}{V_i}}{n} > F$ . As $r$ goes up, so does $F$ and as $n$ goes up, $F$ 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 $n$ , 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 $\overline{V}$ upwards, but push $r$ downwards. At time $t$ , there will also be a back-log of articles that would have been read but weren’t influencing the decision of whether to pay $F$ or not.
How will the reader decide whether or not to read an article before time $t$ ? 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 $V_{i}'$ and after reading the article realize some value $V_i$ . This average spread $\frac{\sum_{i=1}^{m}{V_i-V_{i}'}}{m}$ will likely affect predictions of future value.
As is, the model says decreasing $n$ and increasing $F$ leaves the reader’s decision of whether to buy unchanged. But as $n\rightarrow 0$ 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 $n$ is the more confident they will be about their value prediction since the sample mean’s standard deviation will fall, as $\overline{V} \sim N\left ( \mu,\: \frac{\sigma^2}{n} \right )$ .
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 $V_i$ .
Print subscribers get free access as well. In Salmon’s post he looks at $P-F$ , 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 $n$ and $F$ should be set to.

Finding good values for F and n

Since it’s reader’s will not have the same distribution for $V$ it would be theoretically ideal to pick values for $n$ and $F$ 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 $n$ or $F$ 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 $a_i \in A \;\forall i\in\mathbb{N}$ be an article, and $x_i \in X \;\forall i\in\mathbb{N}$ be a reader. We will now represent the value of an article to a reader as a mapping $V: A\times X \mapsto \mathbb{R}$ with $V(a_i,x_i)$ representing to the value of article $a_i$ to reader $x_i$ . The functions $F(x_i)$ and $r(x_i)$ replace $F$ and $r$ as the fee and rate for reader $x_i$ . $n$ is as before.

Define the set $R(x_i)$ such that $a_i \in R(x_i) \textsl{ iff } x_i$ reads $a_i$ before deciding whether or not to buy.

So our former equation $\left ( 1-t \right ) r \frac{\sum_{i=1}^{n}{V_i}}{n} > F$ becomes $\left ( r(x_i)-n \right ) \frac{\sum_{a_i \in R(x_i)}{V(a_i,x_i)}}{n} > F(x_i)$ .

Rearranging, we get $\frac{\sum_{a_i \in R(x_i)}{V(a_i,x_i)}}{n} > \frac{F(x_i)}{r(x_i)-n}$ .

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 $x_i$ 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 $a_i$ , to determine its value we sum the average value for content of all readers who read $a_i$ before purchasing, and then divide by the total number of readers (who aren’t already subscribers) who’ve read $a_i$ .

Define, $\overline{V(x_i)}=\begin{Bmatrix}\frac{F(x_i)}{r(x_i)-n} &,\:if\: \frac{\sum_{a_i \in R(x_i)}{V(a_i,x_i)}}{n} > \frac{F(x_i)}{r(x_i)-n}\\ 0 &,\:if\: \frac{\sum_{a_i \in R(x_i)}{V(a_i,x_i)}}{n} \leq \frac{F(x_i)}{r(x_i)-n}\end{Bmatrix}$ .

Equivalently, $\overline{V(x_i)}=\begin{Bmatrix}\frac{F(x_i)}{r(x_i)-n} &,\;\text{if x buys}\\ 0 &,\;\text{if x does not buy}\end{Bmatrix}$ .

This function $\overline{V(x_i)}$ is an estimator of the average $x_i$ has for an article.

Now define the set $S(a_i)$ such that $x_i \in S(a_i) \textsl{ iff } x_i$ reads $a_i$ before deciding whether or not to buy a subscription. This set is all non-subscribing readers that read article $a_i$ 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 $\overline{V(x_i)}$ for each $x_i$ in the set $S(a_i)$ , we have a distribution of estimated values for article $a_i$ . That might look something like this.

Article values

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.

$P(a_i)= \frac{\sum_{x_i \in S(a_i)}{\overline{V(x_i)}}}{\left | S(a_i) \right |}$

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.

0 Comments. Posted by Albert Sun on Tuesday, January 26, 2010 at 6:40 pm.

Filed under Uncategorized, economics, paid content.

Visitor Loyalty Will Make or Break Paid Content Plans: A Look at Optimal Pricing

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.

1 Comment. Posted by Albert Sun on Wednesday, November 18, 2009 at 10:14 am.

Filed under Journalism, economics, online news, paid content.

Paying for Convenience, not 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?

1 Comment. Posted by Albert Sun on Tuesday, October 13, 2009 at 7:55 pm.

Filed under Journalism, economics, paid content, user experience.

Archives for “paid content”

My Senior Thesis

A Mixed Bundling Pricing Model for News Websites