What’s My Comp Index?
In today’s frantic digital media environment (how many times have you heard, “I need it yesterday”?), we often get asked by media planners and sellers, “what’s the comp index?” or “what’s my comp index?” For those who may not be familiar with this term, “comp index” is industry slang for the Composition Index metric.
So what is this thing called Composition Index and why should I give a hoot? (Well, for one, because I’m writing about it, of course!) In its most basic form, Composition Index measures the concentration of a particular target group of consumers on a given website or ad network, compared to the concentration of that target in the total Internet population.
Let’s run through an example together. Oh come on, indulge me… this will be fun, I promise.
Let’s say that twitter.com/jefhack (wink, wink, that’s my Twitter profile) reaches ten people each month and five of these kind folks (or shall I say, “followers”) falls into your target audience. In industry speak, comScore computes a Percentage Composition of 50% (i.e. 5 out of 10) for that target. Now, let’s also say that for the U.S. Internet as a whole, there are 200 million visitors and 50 million of them fall into your target audience. The Total Internet Composition % is 25% (50 million out of 200 million).
To compute the Composition Index for visitors (remember that key piece, “for visitors”, because we’re coming back to that shortly), we divide 25% into 50%, giving us 2, and then multiply that result by 100. So, the Composition Index for the target audience among twitter.com/jefhack followers is 200. Wow, 200! As Steven Tyler would say from the American Idol judging seat, “that’s beautiful, just beautiful”.
But, is it beautiful? Well, yes and no. Oh sure, a 200 Composition Index can be a wonderfully beautiful thing if Steven Tyler says it is, and if it is truly measures what matters for the objective at hand. But what is the objective at hand here? Well typically, media planners and sellers are working together to match up an advertiser’s message with the right audience and the right content with the best combination of target reach and frequency.
So what’s my point? The exercise above assumed that the best way to measure Composition Index in the larger context of matching ads to consumers is to use visitors as the most atomic level of measurement. If this were a conversation about magazine readership or TV viewership, my ever-so-engaging style of writing would quickly come to a stop here. But don’t fret. I think you see where I’m headed… and this job isn’t quite finished.
After all, this is the world of digital media where every event can be measured, reported, and analyzed. Time and time again, we must ask ourselves if there is a better way to be looking at something. Are we analyzing the most meaningful metrics? In this case, there may be something better…
Nestled inside of the comScore MMX planning suite are two reportable measures that go by the names of, % Composition Pages and Composition Index PV. Think of these as analogous to what we just described above, except that their atoms are the pages consumed by the target audience. Why does that matter? Why might this be a better metric in this instance? Because we’re trying to place ads in front of those consumers most likely to purchase a particular product.
Let’s get back to my Twitter profile one last time (I swear that this posting is not solely intended to drive up my number of “followers”). We learned above that five (or 50%) of my profile visitors fell into the target audience. But, what if we could learn that my page was viewed 20 times during that same period, and only five of those page views were seen by the target audience. Hmmm. 5 divided by 20 equals 25% Composition PV. Suddenly, my 50% composition of visitors just dwindled to 25% composition of pages.
Why should you care? Well, we know that ads are placed on pages (or in video streams, but we’ll save that discussion for another day). So, the chances of the target audience seeing the advertiser’s message is directly tied to the number of pages they consume. Therefore, 25% is the more relevant measure in this instance, since we’re interested in understanding exposure to advertising.
So now you ask… that all sounds like it makes sense, but can you prove it? I’m glad you asked, because we sure can.
I took the liberty of tapping into comScore’s Ad Metrix service to answer this final question. Ad Metrix is comScore’s leading competitive intelligence tool for understanding the display advertising market. With Ad Metrix, we can learn just how many ads were actually seen by the target audience and tie that back to both the visitor comps and page comps. Let’s take a look at how this plays out for the audience target of Females Age 25-54 for two sample web properties: Everyday Health and iVillage.
They say that the proof is in the pudding. (I should not be writing this piece before breakfast, clearly).
You’ll notice that for both properties, Females 25-54 make up approximately 40% of the unique audience. And if we just stopped at the % Comp measure for visitors (“UV”), you might assume that there is just a 4 in 10 chance of reaching the specified target with ads. But, if we take it a step further and look at the % Comp Pages measure, the odds improve to about 5 in 10. I’m not a big gambler (realized early on that losing wasn’t fun, especially on a college budget!), but even I know that this is not an insignificant change. Another way to look at it is that you can actually have a 20% greater efficiency (50% divided by 40%) in delivering ads against your desired target simply by looking at a better metric.
Now how can we be sure that this Pages-based measure matters more and is truly the better metric? Take a look at the last column in the table. Because of Ad Metrix, we can quantify exactly how many ads were seen by this target audience on each site (and isn’t that the whole point of this exercise in the end?). I’m also no fancy mathematician, but even a quick glance at the numbers will tell you that the pages comp and ads comp values line up pretty darn close… in fact, strikingly close. The proverbial pudding tells us that the theory of pages consumed indicates likelihood of seeing an ad is quite true.
So my message to media buyers and sellers: Take the extra time to think about whether or not you’re measuring what matters most. And if you’re not, maybe give this PV comp metric a try. It really works!
I hope you enjoyed this ride and will come back again. And oh by the way, my Twitter handle is @jefhack in case you missed that. :-)