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What’s the value of a user on your site? Why it’s hard to calculate lifetime value for social network audiences

Who is this, and where can I find more pics??
For those of you who aren’t familiar, the photo above is of Christine Dolce, aka Forbidden, who is a famous MySpace celebrity (and will surely get her chance to star in a VH1 reality TV show). I can hear a rush of clicks googling her for pictures, so I’ll just provide you the link to her MySpace profile here. Let’s get back to Forbidden in a second, since she fits into a larger discussion.

LTV and the goal of infinite segmentation
The core of many marketing programs is segmentation – you take your core audience, identify differences between their motivations, spending patterns, and behaviors, and tailor your messaging to hit that audience. The better defined your segments are, and the more granular they are, the more opportunities you have to personalize your message when you reach out to them.

One way to do this segmentation is to look at "Lifetime Value" (LTV). Calculating lifetime value (LTV) of your customers is a great way to understand how they fit into the core of your business. Typically, your best customers will represent a significant amount of revenue, and you want to make sure they’re happy. Having a granular LTV calculation where you plug in a user’s historical data allows you to come up with infinite segmentation in terms of how you want to differentiate the experience high-value customers get versus low-value ones.

LTV for retail sites versus social sites
For retail sites, the calculation of LTV is pretty clear. In plain English, you might define it as:

The stream of all previous and future profits that a user generates from their purchases

So for a given user, you’d add up all their previous transactions and then add that to whatever model you’ve created about their likely future transactions. Part of what makes this work is that:

  1. Transactions in a retail setting are unambiguous
  2. Each individual makes an isolated impact on the system, in the form of a transaction
  3. Retail buying has a long established history of data, both online and offline

Now let’s look at social properties, particularly ones that have the characteristics that they are ad-supported, are heavily based on UGC content, and incorporate viral marketing. If you were *just* to consider the advertising portion, then it might be easy – the LTV of a user would be defined as:

The stream of all previous and future ad impressions that a user generates from their usage

So that seems pretty clear – if you’re a user who generates 100 ad impressions a day, you are worth more than someone who generates 10.

The problem is when you try to incorporate the value of the UGC that a user generates, or the users they help acquire (or retain!) as part of the LTV calculation. And for this discussion, let’s go back to talking about Forbidden.

Forbidden as an LTV outlier
The problem with a user like Forbidden, and possibly even more so Tila Tequila, is that only a small amount of value that they create comes from their actual usage of the site. Instead, they provide additional value through user acquisition, retention, and content creation that is poorly measured by the definition above.

Another way to think of this is that if you were to remove these users from MySpace, you would not simply be subtracting their LTV from your overall site’s value. In fact, it would be an outsized decrease in value, since users like Forbidden and Tila Tequila bring many millions users onto MySpace, and entertain millions of people, keeping them on the site.

A couple commenters of my LTV in casinos blog post said as much:

QDub writes:
"Great post! I’d add that for online
businesses, LTV is further complicated by a user’s role as a
net-promoter and a content creator. Otherwise, LTV should be easier
than ever for online businesses vs offline–you have direct access to
customer demographic and value data, and creating differentiated
outreach is easy as pie.

Problem is, given the state of the web today, we’re still struggling
with finding value, period. Segmenting by non-existent LTV may be a
moot point for many startups."

Douglas Galbi writes:
"LTV value calculations are a good
tool, but they tend to place in the background the "social value"
that’s central to a lot of new internet businesses. So perhaps you and
others who know a lot about social software have some knowledge that
you in turn can share with casino business folks.

Perhaps gambling in casinos is highly individualistic. But I’d guess
that participants prefer the room to be crowded but not too crowded,
and to have some beautiful people who are part of the crowd (not just
marked performers). Playing games with others who one finds interesting
and fun, word-of-mouth marketing, the negative social value of
particular personality characteristics (dour, whiny, griefer) might
also be factors with business significance to casinos but that might be
overlooked in an individual LTV calculation. Someone in the business
should be able to get some data and do some analysis for insight into
the quantitative significance of these social factors."

These are great points, and I agree with these issues as difficulties in applying LTV. The overall gist is that when you have interactions between users, all of a sudden the dependencies that are caused become difficult to measure.

Let’s talk about how to think about assigning credit based on those dependencies.

Assigning credit in LTV calculations is the hard part
Even in the most simple case for assigning credit in the LTV calculation, there are problems. Here’s the smallest example, in which one might ask:

If a User A views a piece of content uploaded by User B, who get credit for that pageview?

Well, the answer to this question is actually quite complicated. First off, it depends on whether or not User A is likely to have that pageview anyway. That this, even if this content uploaded by User B didn’t exist, perhaps User A would be bored anyway and would have consumed that piece of content. In the opposite scenario, if User A came to the site for the express purpose of viewing User B’s content, then User B ought to get a lot of the credit.

Similarly, a case like this exists on the user acquisition side. The question in that case is:

If User A invites her friends B, C, and D onto the site, should she receive any credit for their pageviews?

You think they would, at least a bit, but it ultimately depends on whether or not B, C, and D were going to end up on the site anyway. If your acquisition is great, and you would likely have gotten them through some other acquisition scenario, then it doesn’t seem like A should get much credit. But if they are incremental users, then it’s great, and A should be rewarded.

The point is, a lot of this exercise becomes about figuring out the incremental value. You’re trying to extricate the value that would have already been there versus the new value that gets created by a user.

If Forbidden didn’t exist on MySpace, would users simple go to a different trashy blond girl to look at their pictures?

That’s a very philosophical question, I know ;-)

Conclusion
To summarize the blog post above:

  • Lifetime value calculations can and should be used to value your audience
  • It’s pretty clean to calculate in retail, and much harder to calculate on social networks
  • The value of content creators and social linchpins gets mixed up in the calculations
  • And finally, what we all knew already: MySpace girls are much hotter/trashier than Facebook girls

Suggestions and comments are always welcome.

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