Maheu spent six years at the independent Razorfish, retiring in 2003 after guiding the sale of the company to SBI (before its time with aQuantive, Microsoft, and finally Publicis Groupe). Following Razorfish, he was the chief digital officer of Ogilvy & Mather, and most recently, global CEO of Publicis Modem.
In this chat with AdExchanger, he discusses his leap from the agency side and the value of social analytics to traditional media forms like TV.
JP MAHEU: There have been a few changes among major agencies. All the big groups now have trading desks and real-time bidding platforms. We always complain that agencies are not moving fast enough, but you roll the clock back five years and those tools didn’t exist at the agencies. There is this lack of self-confidence in the agency ecosystem sometimes. We like to criticize ourselves, sometimes a bit too much. And there’s an echo chamber that picks that up and runs with it.
The major agencies have done it and at the creative agency you find a lot of analytics, too. At Ogilvy, we had a team of analytics people that were the best in the industry. I believe that creative and media agencies understand the need to embrace data. I’m not concerned about that. The reality is that agencies, clients and publishers are big organizations and changing anything in large entities takes time. It takes time for good reason.
Don’t get me wrong, I’m not saying there needs to be more change and that it needs to happen quickly. But I’m not of the kind that they are dinosaurs and they have no idea what they’re doing. Absolutely not. I try to think that agencies will continue to be the primary go-to partners for CMOs.
AdExchanger: So, after working on the agency side for many years, you have decided to join social analytics startup Bluefin? Why?
Number one, I think TV is still the largest medium out there and I don’t think that is going to change for quite some time. Marketers still believe, and to some extent rightfully so, that when you want to launch a big product, once you want to reposition a brand, once you want to make a big splash, TV is still the biggest and the most effective medium at doing that. However, as you know, the rating points are down, so the market has also realized the need to amplify the story somewhere else and a lot of where to do that is in social media now.
On one side, you have the biggest medium and then on the other side, you have the fastest growing medium in the history of marketing. Bluefin is at the intersection of those two mediums, connecting them through analysis and data. That is one thing I realized about the trend. The second thing I like is the team. Deb Roy and Michael Fleischman, the two cofounders of Bluefin, are smart, special individuals. What they have done from a technology and data standpoint is no less than remarkable… a very very high-IQ team, extremely strong engineering teem. That is the second main reason.
But making the move from the agency world to a startup still seems like a big leap, no?
If you look at my career, I’ve gone back and forth between big companies and small companies. I joined Razorfish when Razorfish was twenty people. Prior to that, I was a strategy consultant in a consulting firm that had offices around thirty countries in the world. I think that I was ready to go back to a kind of an earlier-stage company that has a huge growth potential with the risk/reward associated with that.
I get that Bluefin’s stats on how much Twitter or Facebook activity is happening around a given show is interesting, but are they valuable? Do you see it as a direct challenge or a mere complement to standard TV ratings, such as Nielsen’s?
We analyze social commentaries related to TV programs across 120-plus networks. We also know what ads are being shown during those programs in quasi-real-time. As such, we can actually connect social commentaries and Twitter activity and some Facebook comments not just with the shows, but also the actual ad.
How does that translate to value for an advertiser and the TV network?
The near-real-time data we gather adds up over 24 hours, 7 days a week and over months and so forth. The networks, they use the data to analyze the shows that generate the most social engagement. Then they use the data to continue to rationalize the pricing of their ad space is what it is.
A network will use the data, in addition to gross rating points, to showcase to advertisers how engaged those shows are with viewers. We also have ad-tracking capabilities where we can tell a network what advertisers and products have a very strong affinity with their shows.
What’s the benefit on the marketer’s side?
As I noted, the ad-tracking capability helps them as well as the networks in terms of telling them fuller value of a buying time on a particular program. Nielson and Kantar and a number of other companies do that, too, of course, but we do that in real time.
Is that type of real-time advertising important for TV? After all, TV buys aren’t done with dynamic ad insertion like online, except in what are much smaller cases through some cable and satellite systems.
Marketing is becoming more and more real-time so this idea of spending so much money on TV ads and then waiting a month to get a report, it’s not that helpful. For example, it’s significant layer of intelligence to track TV ads in real-time to better understand the value of launching an ad at a precise moment in time. Questions such as “How much weight are they putting behind this creative versus that creative?” can be understood almost immediately.
True, you can’t optimize TV at the same time you can optimize the display ads; however, that near real-time information can really help you plan more effectively in your kind of week-in, week-out major plan.
The second value proposition we have for brands is around this notion of creative optimization. Imagine a brand that is launching a campaign with three TV ads. Very quickly, we can track how much buzz each creative generates and based on the target audience that the brand of the marketer is focused on, we can segment our response data to those creatives following certain target audiences
The third value is around earned media. As you know, marketers have been talking about that topic for a long time. [Federated Media founder] John Battelle has been talking about conversation marketing for five years so. Marketers are trying to create a conversation around the brand of the product that obviously favors them. We can track that. Beyond tracking, we can help marketers identify the TV shows that are going to generate the most earned media for their messages so there is a kind of an earned media amplification value prop that our data can provide to marketers.
As you say, Bluefin can do a lot with social data. As you settle into the role of CEO, is your plan to expand the use of those existing tools or do you see a need for additional ones?
Bluefin has done a fantastic job of defining the captive area of social TV. It is kind of a new category and we need to continue to focus more on putting greater investment in the data and the analytics we provide. I see my role very much in scaling the organization, growing the number of clients that we have. We have 42 TV networks buying Bluefin data and we serve over 20 brands. There is obviously a lot more clients we need to approach and convince them that they should buy the data.
What else can we do with the data you collect? Can you think of any other permutations?
Let me just give you one example. We know what people like about TV shows and the TV shows that are the most engaged with by the audience. As an example, take Hulu, a fairly big, online video provider. We know that marketers are spending an increasing amount of the dollars in online video. We could provide data to marketers to identify the right programs to put their ads in on Hulu. It is kind of a targeting data if you have the program data that we have. We are the only one that can connect social commentaries with programs and brands and actual ads, we have those affinity data so we can match a specific audience with a specific program and a specific brand.