@andrewchen

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Ad targeting talk from Community Next: People Not Pages (updated x2)

I updated the Community Next sldies from last time with an audio track, so I thought I would publish these slides again. Pardon the intrusion if you feel like you’ve already grokked all the details!

CommunityNext: People not Pages

View SlideShare presentation or Upload your own. (tags: advertising sns)

Intro
Hi everyone. My name is Andrew Chen and I’m a blogger and entrepreneur, focused on consumer internet products here in San Francisco.

I recently gave a talk called People Not Pages at the Community Next, which I’ll share today.

This year, the online advertising industry has reached roughly 20 billion dollars, that’s a big number. And a big chunk of those dollars are here because of the fact that advertisers can target consumers in ways that are simply not possible in traditional media.

Talking about ad targeting
So today we’ll talk about the business of ad targeting, and in particular 3 things:

First off, how can publishers generate more revenue from ad targeting? How does this actually happen?

Secondly, how do advertisers evaluate the effectiveness of their targeted campaigns? By understanding how they think about the question “does it work?” publishers can serve them better as customers.

And finally, we’ll discuss a topic that’s near and dear to my heart, which is: Why do social networks suck so much at monetization?

Note that this talk will mostly focus on the publisher-centric view, since the startups here in San Francisco tend to be creators of ad inventory rather than buyers of it.

About me
So quickly, before we begin, a quick bio about me. Again, my name is Andrew Chen and I’ve spent the bulk of my career in online advertising. I was most recently an Entrepreneur-in-residence at Mohr Davidow Ventures, a time I greatly enjoyed, evaluating deals in ad infrastructure as well as companies that are ad-supported. Before that I spent 4 years running product at Revenue Science, a behavioral targeting ad company in Seattle, and before that in the automotive vertical at Cobalt Group.

About Revenue Science
Most my experience will be drawn from my time at Revenue Science, so I’ll talk briefly about the company. Basically, as a behavioral targeting ad provider, they work with some of the largest brands online – Reuters, NY Times, ESPN, AOL, and dozens of others – to help them increase their revenues via targeting. While I was there I worked on their brand publisher products as well as getting their ad network off the ground.

Generating more revenue as a publisher
On to our first topic: How does targeting generate more revenues for publishers?

To understand this, I think you have to go all the way back to the newspaper days. This is an article from the early 1900s, costing one cent, documenting when the Titanic struck an iceberg. You have to think about how advertising was bought and sold at that time to understand why things work the way they do online today.

Now imagine this: You’re a newspaper company, which means you actually have to print physical copies of your text. That means that if you’re going to sell advertising, you must also print your ads on that physical copy, next to the editorial that’s relevant. That means that as an ad sales professional, your job is to take an audience that an agency specifies, let’s take “small investors” as an example, and translate that into a particular section in your paper. And so you sell them the Finance section. This means the agency asks for people, but you sell them pages.

The modern day online publishers are not much different. For a publisher like NY Times, you’ll have a bunch of sections on your site. You have the homepage, which makes a good CPM because it gets the most traffic and is the most prestigious placement on the site. Then you have high-value sections like Technology, and Finance. Then you have low value sections like Opinion and World News, which are harder to sell because what advertiser wants to be next to a story about the War in Iraq? So all of the low value stuff gets classified as “remnant ad inventory” and is lumped together.

So, this means that you have a bunch of different sections, and you can sell each for different prices. Again, the agency asks for a specific kind of person, let’s say IT decisionmakers, and you give them back the Tech section.

But wait a minute here, the IT decisionmaker reads the tech section, but also browses around the site. Is it right to value them highly on one pageview, and then drop them on the next? This is really a vestige of the old newspaper mindset.

If the agency is fundamentally targeting an audience, and you can deliver this audience regardless of where they are on the NY Times. or potentially even across the internet, you can deliver them more value. And lucky for the publisher, they are then converting their low-CPM remnant inventory into high-value targeted inventory.

This process of finding the qualified audiences in the remnant inventory is really what a publisher strives for.

Targeting techniques
So all the different targeting techniques that are out there, be it Geo, SIC, Demographic, Content, Daypart, or Behavioral, is all about finding these valuable audiences.

Once you have your targeting toolset in place, then the value of your ad inventory is increased. You find the IT decisionmakers in the opinion section, and convert low-value ads into high-value ones.

Evaluating targeted ad campaigns
Great, now let’s move to the second topic. How do advertisers evaluate whether or not targeting campaigns are working?

To understand this topic, you have to understand that there are really two major groups of advertisers out there: Brand advertisers, and direct response. Let’s talk about each in succession.

Brand advertising metrics
Brand advertisers are the guys you see all over the place, like Coke, Budweiser, Honda, United Airlines, and others. For many of them, their biggest goal is to drive awareness of their brand messages regardless of strict ROI. Obviously the ads are meant to eventually sell product, but it’s often not accountable from a strict ROI basis.

This brand spend means big dollars. Take a Budweiser NASCAR vehicle, for example. These cars can cost up to $20MM to sponsor. This year, Facebook is rumored to gross $300MM in revenue, which only translates to 15 NASCAR sponsorships.

So what kind of metrics are they looking for?

Well, here’s a case study that I worked on many years back. American Airlines and WSJ worked together on a campaign which was evaluated using Dynamic Logic, an ad effectiveness company. They went and surveyed users who had seen the campaign and who hadn’t, and asked them questions around message awareness and purchase intent. And it led to some positive results, which I’ll discuss.

First, there was a metric called Audience Composition. The campaign was targeted at frequent business travelers, so the survey qualified who fit the bill. The targeted campaign had a higher % of business travelers than the untargeted one, in fact by more than 2X.  Same for the definition of heavy travelers, people who took more than 5 trips a year, where it was 145% better.

The other set of metrics were brand and persuasion metrics, like Aided Brand Awareness and Message association. For example, those questions might revolve around things like, “Of the following brands, which do you associate with higher comfort” and would name a series of airline brands. The ones in the campaign were correctly associating the brand message.

Direct response advertising metrics
Now let’s go to direct response advertising. I used a picture of a cheesy infomercial because that’s the kind of advertiser that really cares about their results. Every dollar they spend must generate more dollars. They care a lot about ROI, as a result.

The example case study here, which I did not work on, is from Yahoo. In their example, they created an audience segment of mobile users who searched for ringtones, specific mobile providers, and browsed relevant pages. For the advertiser targeting this audience, the campaign generated higher CTRs and conversion rates, which are good ROI metrics. The better the increase in conversions, the more you as the advertiser can spend on this audience to get them to purchase your products.

Social network monetization (and why it’s hard)
OK, and the final topic for today, a discussion of why social networks suck so much at generating revenue. This is a topic I’ve written a lot about on my blog.

First off, this slide shows what you get when you search for “myspace addict” in Google images. The fact is, people find MySpace insanely addicting, which is great for them on a pageviews and engagement perspective. Problem is, this same engagement is what makes MySpace users pretty uninterested in what you’re trying to sell them. They are simply not in the mood to buy stuff, and when social network audiences are in their “flow state” clicking around, the CTRs can be quite low. This is exactly what you see, with CTRs often 0.1% and below, whereas search CTRs are often in the 10s of percentage points.

Social network data = sucks for targeting
The next issue is the type of data social networks are amassing. I included a real profile from a friend of mine, Andrea, which states “I have one interest: Dane Cook. I’m kidding I have more: Movies, music, soccer” etc. This tells you that Andrea’s interested in these things, but not that she’s in the transactional window to buy something. This is completely different than the kind of data you get from users when they are searching for dane cook movies on Google, or searching for dane cook on Amazon.

This issue is really the distinction of Interest versus Intent, and it’s a really hard area to break through. There’s a lot of social advertising companies working on this problem, and good luck to them, because it’s very hard.

Don’t assume that all the data you’re collecting is useful, but it’s likely that it’s not, unless the user is very close to making a transaction.

Wrap up
OK great, so we’ve covered the main topics. We talked about how publishers make money, and the process of turning remnant into premium. We also talked about brand versus direct response metrics in evaluating targeting campaigns. And finally, we talked about why social networks are so bad at monetization.

If anyone has questions, please feel free to e-mail me or check out my blog. Thanks!

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