“Technology enabled research is a trend in the marketplace,” noted Bruce Friend, President of Global Media and Entertainment, Maru/Matchbox. Harnessing the power of technology in the pursuit of research insights is something at which Friend excels thanks to his previous work at OTX and Vision Critical. Now, much of his effort focuses on “building capabilities that are platform-based [unlike] a traditional research company, which still tends to be very focused on one-off, ad hoc studies, employing different survey based methodologies,” he recently told me. Friend helps clients create single-source panels that leverage a company’s own consumer assets, essentially connecting behavioral data from their DMP and other data assets with survey based demographic and attitudinal data to track over time.
“We build custom panels where our clients own the panel members and all of the data collected on those members,” Friend added. “So, we are helping them create their own insights platform and program that has tangible asset value. This value then continues to grow as the panel size increases and we collect more data on the members.”
Friend had much more to say in our recent interview.
Charlene Weisler: How has the custom research business changed?
Bruce Friend: We are seeing that companies want to work with research partners in an ongoing relationship — always on, always delivering in terms of data and in terms of thinking. That is where we are heading. We are doing so not only by continuing to partner with Vision Critical, which spun off the research consulting part of their business to become Maru/Matchbox two-and-a-half years ago, but by acquiring new companies that complement the part of the business that we are concentrating on as well as enable us to do things on a standalone basis, on private panels and communities.
Weisler: And the media landscape overall is changing.
Friend: It’s been an interesting time in this “pending merger world” that the media industry is in now. We are seeing (content) companies building out full capabilities to support the process from script to screen. And beyond script to screen, really — being able to control the entire ecosystem from the standpoint of developing content, marketing it, distributing it, continuing to build franchises and monetize the businesses going forward. It is also about the platform, not just the content anymore. It’s about how we get the content to the consumers in different ways. We see that even from companies such as Amazon (Prime Video), Twitter (TV, Video), Facebook (Watch) and others that are adapting to video being a new and increasingly dominant content form. It always seems that it all comes back to video and we are certainly seeing that more and more in the online space.
Weisler: Video is important, but more people are also talking about voice.
Friend: Yes, obviously voice activation is going to be the norm. In the not too distant future, I see companies conducting surveys through Alexa and Google Home. There are certainly some privacy issues around that sort of thing and I am sure there will be ways of working through that, probably by creating panels of homes where people will opt in. In addition to voice, audio is also making a resurgence. We currently work with four or five companies that are very audio-focused. Just like with video, audio is finding many new areas where it can exist and thrive. The emergence of podcasting is only going to continue. It is an indication of where our business is heading, where people want to listen to what they want when they want to listen to it, just like video.
Weisler: In creating panels from a client’s own dataset, it sounds like you are able to fully leverage first-party data. Is this a trend? And what if a company doesn’t have a lot of first-party data?
Friend: We are leveraging both first- and third-party data. Certainly there are many resources for third-party data. Obviously the first-party data is better because it is essentially a 100% match rate as we recruit customers directly from the client’s database to become panel members. But in some cases some people don’t have first-party data. In those cases, we look for third-party data matching opportunities. We also run our own panel here in the U.S. and in Canada — both have around 250,000 members — so we can leverage them, as well as look to match more data sources into them. Our panels can also be used to look at communities outside of the company’s own panel. If you want to look at competitive viewers and competitive distribution services for example, you can then leverage our panel in addition to your own. Clients don’t always want to talk to just their own customers.
Weisler: Do you see any evolution in how online communities are being built and used?
Friend: When communities started, they were about better, faster and cheaper. Communities were the start of agile research. The client could control and use the platform as a DIY tool. Most of these panels were 5,000 to 15,000 members. The trend is now not to have these smaller siloed communities within one company and across different brands, but to build a mega-community or an enterprise-wide community. We can now put all of these communities together as an organization with 50,000 to 150,000 members across the organization. Going bigger is better and when you then connect your DMP or other specific first-party data, you then have a very powerful asset with enough scale to do some very interesting things with the data and with surveys on top of it to give you more strategic insights. Communities used to be “light tactical” research — most people were not using communities for very strategic work. What we are finding now with some of our larger clients, who have made the effort to build out bigger panels, some as large at 175,000 members, is that they can now do more strategic work on them. As a result, we are seeing budgets move from more traditional research into platform-based panel offerings such as ours, where clients can better leverage their own big data.
Weisler: So where do you see research going?
Friend: I see this model where companies tie into technology with an embedded community where you can talk to someone today, talk to them again a week from now, and on an ongoing basis. The company owns the panel asset and is building out that asset. That asset really has (data) currency to them while they can still conduct large survey studies within the panel. But it’s really about the creation of a resource that links behavioral data, attitudinal survey data, qualitative data, etc., into an ongoing relationship in an ongoing data stream. Automation will drive a lot of this, as will A.I. We are making big data smaller, more contextual and more understandable because we are looking at data that is in a panel and is more representative of the audience or subscriber base that the client has.
Weisler: Sort of bringing the data science and ethnography elements of research back together.
Friend: Yes. I feel that we are coming full circle, back to where we were years ago when I first entered the industry. At that time research and big data lived harmoniously within the same insights departments, and that must happen again in my opinion. Otherwise, companies today that haven’t already moved to effectively consolidate their research and data science teams into one and build business intelligence assets that support their entire organization run the real risk of rapidly falling behind their competitors that have.
This article was originally featured on the Media Village website on July 26, 2018.