15 minutes

[INTERVIEW] The Rapidly Evolving Field of Insights and Its Challenges

Many industries are evolving at an unmanageable pace. This is certainly the case for the world of insights. I recently caught up with BBC Worldwide’s EVP of Insights and Affinio friend, David Boyle to discuss how deriving insights has changed in just the past few years.

Just five years ago, insights teams were using single source (and maybe dual source) data to answer most business problems. David shares that today, that is not the case. Insights teams have started to embrace the almost infinite sources of data and emerging technologies that are used to understand this abundance of information.

This evolution from a single-source to multiple sources has led to many new challenges for insights teams. They are now tasked with staying on top of and understanding the vast amount of emerging technologies. With so many new data sources comes that many more insights to analyze and understand.

The challenge today is knowing what technologies and sources to rely on first and then being able to draw meaningful conclusions from the data in a reasonable amount of time.

The following interview has been lightly edited for ease of reading.

The Evolution of Insights

India: In the last five years, how have you seen the research industry evolve?

David: In the old days, just five years ago, it used to be that single source or maybe dual source data could answer most business problems. Quant research, a little bit of measurement and you were pretty much good to go. You could influence most business decisions.

The world has become so much more complicated since then and so much more fragmented. That complexity is increasing so rapidly that it’s much tougher now. No one single data source has that big of an impact anymore. You need a whole suite of different tools in your arsenal to be able to help with most business decisions. It’s a lot more complicated these days, a lot more difficult. And, of course, you require lots of very different skills to make use of each of those very different data sources.

India: To that point, there are so many new data sources available and regular advancements to the ways that we analyze and understand the data available to us. Would you say that marketers and researchers are evolving as quickly, or even half as quickly, as the technology and information that’s available to us today?

David: That’s definitely a problem. It’s clear to me that researchers have tended to be skilled in one or two things, and have been unable to keep up with the pace of changes in data and technology. It used to be that researchers would get their hands dirty in the detail of the data and the analysis. Nowadays that’s completely impossible across all those different data sets. While it’s definitely possible for one person to write a questionnaire, analyze the data and be in the boardroom to share the conclusions, that same person can’t possibly be scraping social media, and clustering behaviors together, and doing similarly complex work on lots of other data sets. Researchers have definitely been unable to keep up with all different data that needs to come together.

I think they also haven’t been quick to embrace that data. They don’t understand it in the same way they used to understand the data they work with. They haven’t learned to relax and let go and step away from it and trust people like Affinio and then to integrate the results into their arsenal of tools and capabilities.

What Insights Team Look Like Today

India: It sounds like there have been dramatic changes in the way insights teams operate. What do data teams and insight teams look like today versus even five years ago?

David: Well, five years ago it was a bit like a pipeline. There was data, there was analysis, and there was reporting. Then there were the people using that data in the business to make decisions. Everybody was somewhere in that pipeline. It was quite a small, close-knit team. I think these days, just five years later, I find myself in a relatively similar business to the one I was in five years ago, and the pipeline is completely different. That model wouldn’t work at all today. No way.

That exact same pipeline still works for the exact same data sources that we worked on in those days, but alongside it, we have one person whose job it is to become an expert and advocate for Affinio. One person is an expert in Parrot Analytics. One person is an expert on piracy. And another couple people on different parts of social media. And another couple people on other things like measurement. There are way more people interfacing to way more agencies and partners to bring data into our team.

The person at the other end of the pipeline whose job it is to use all that raw material, use all that insight to help the business with it has way more interfaces, way more people framing the data. They use each person’s perspective on their data as they can’t possibly understand everyone’s data. They’re getting five or ten different reports on, for example, how a TV show has performed. A social media report, an Affinio report, a piracy report, a measurement report and a quant research report. The list goes on.

The person at the end of the pipeline helping the business has to pull all of those together. They’re sometimes contradictory; the data can’t be joined, so it’s the conclusions that have to be joined. That’s really tough. That person is way more removed from the specifics of those data sets because they can’t possibly be experts in all of those different sources. It’s a lot messier. There’ are many more relationships that have to be built to get insight where it needs to get to.

In the old days, there would be a presentation when the quant research was complete. We can’t allow that these days. It has to be presented alongside all the other data sources to form a rounded view of the issues and opportunities, or else the business person will go crazy from all the different presentations they have to sit in!

Prioritizing Data Sources

India: I’ve heard you say before that we are absolutely swimming in data. We have more data available to us today than ever before. With all the different channels and different reports and different ways that you can pull insights, how are the methodologies, the tools, and insights prioritized as far as decision-making goes?

David: The business users, of course, feel the same way as the people at the end of the insight pipe. They have so many different contradictory perspectives being given to them. Even if we do our best about joining them all together before they get to that person, they still see quite a lot of the original reports and they still want to. They’re being bombarded. They don’t know what data to believe, and they don’t know what to trust, and they don’t know what data is important. Even more, than before, it’s down to the insight team to advocate for the tools that are priorities for a particular business decision, which then makes it even more difficult.

In the old days the business would say, “Hey, we need a brand tracker,” and the insight team would deliver it. In the new world, nobody is saying, “we need social media segmentation.” They’re saying, “tell me who my audience is.” The insight team has to then decide what tool is the right one to answer the question. That’s tough. Do we advocate for a new tool or not? Do we push and make it mainstream?

Some of those same business people are still saying, “Just show me the research. Just show me the research,”; and it’s our job to challenge that and say, “No, no, there’s other priorities these days.” It’s to the point where Affinio is now our default first source of data on who our audience is, not the research! That’s radically different from where we were just a couple of years ago. It’s not something that a business would ask for. It’s something that we decided was the right answer and had to advocate for.

I think if you’re an insight team that wants to confidently lead, challenge and shape the agenda, then it’s still super hard work, but it is possible. If you’re not that type of person, which most research people aren’t, then, new tools and techniques just won’t find traction beyond demos and pilots. You’re just going to carry on with the same old, same old.

India: With the emergence of new data and technologies, there are still the more traditional white papers, surveys, questionnaires, focus groups, etc. With the speed at which insights teams need to move today, do you see any of those more traditional methods falling by the wayside?

David: They’re becoming significantly less important, yes. The simplest example is quant research. Three years ago, when I left the music business, our team still delivered 90% of our insights from quant research. That was sufficient insight to literally change the culture of the organization from gut instinct driven to evidence-based. The impact was remarkable, but it was 90% down to quant research.

At BBC Worldwide, we face similar opportunities and challenges for our brands. We still have to do that quant research because it adds an important piece to the story. But Affinio is now our lead tool for a big part of that same requirement. Now we’ve got at least two data sources doing what was once one. Yes, quant research’s role has diminished, but we still have to do it. And there are many more tools alongside it also. That leads to being spread very, very thin, which is a real challenge. There’s almost nothing that we’ve stopped doing because each tool still adds an important piece to the story, which is really annoying. It’s really frustrating.

The Complicated World We Live In

India: It sounds like technology and data science is actually making your life more complicated.

David: Yes, it’s definitely making it more complicated, but at the same time, the world has become more complicated. It just makes sense that measuring that world would have to get more complicated, and that’s okay. Data science and advanced computing are showing us things we couldn’t see before. They’ve unlocked opportunities that weren’t available before. That’s the plus side of it.

I think Affinio is a classic example of that, which is data that just three years ago I was arguing was misleading and dangerous. I was speaking at market research conferences about how people using Twitter data to make business decisions are misleading their businesses. They are basing decisions off a tiny and unrepresentative subset of users that tweet about a given topic. Affinio is taking data that wasn’t just junk, it was actually dangerous and making it incredibly valuable.

I don’t think Twitter has changed very much in that time. What’s changed is how we look at that data. We used to only be able to look at people who tweeted about, in the old days, music, which is an incredibly biased subset of people, leading to bad decision-making. Now, we can look at people who are interested in music (even if they never tweet about it) and we understand who those people are in much richer, more useful way and it is incredibly powerful. All of this from data that was worse than junk — it was dangerous just three years ago. That’s an amazing opportunity. That’s an amazing turnaround.

What TV Audiences Look Like Today

India: One of the major roles of the insights team at BBC Worldwide is to understand TV audiences. With the evolution of how people consume TV, how have you been able to understand these audiences and their nuances?

David: In the new world of TV, an audience is somebody not just who watches your TV linear broadcast at the time of broadcast, those captured in the Nielsen ratings. It’s also somebody who follows the Twitter stream, or the Facebook stream, or watches the show on Netflix or video on demand service at some later point — and none of those are measured in Nielsen.

The audience is something much bigger than just linear broadcast which means that the traditional tools in TV don’t work anymore. They measure the linear broadcast, and that’s an important piece of the story, and it’s the biggest single piece of the story, but there’s so much more to the story of the audience than that. It’s nowhere near good enough. Plus, it’s just not rich. It’s very boring. It is simple crosstabs of data.

Affinio has a much richer, much more actionable, much more rounded view of who the audience is for our brands. It allows us to think about the business models we should use to engage those consumers segment by segment. For some consumers, it might be the official linear broadcast, but there will be other segments of consumers that Affinio helps us to identify completely different ways in which we need to engage them, whether that’s discovery or engagement or consumption of the final TV show.

It’s Really Just About Understanding Audiences

India: We’ve talked a lot about collecting data and deriving insights. What decisions are being made off of these insights?

David: I think the biggest decision that Affinio has helped with is how we grow our brands and develop them into the future. I deliberately say the word ‘brand’ there instead of ‘TV Show’ because it’s not just about the TV shows these days. There are lots of things we need to do like short form content, stories told outside of linear broadcasts, in between season content and experiences, nonlinear TV experiences and consumption, brand partnerships to keep growing stories in other ways across other platforms.

What we used to think of as just a TV show, we now think of as brands that live outside of the linear broadcast. The first step in that journey is to deeply understand all of the different audience segments so that you can plan and prioritize all the activities and products and stories to maximize reach and engagement and grow the brand over time. We’re going from thinking of our TV shows as TV shows to thinking of them as brands. Affinio is fundamental in that journey.

It’s really just about understanding audiences. Let’s say we’re going to meet a partner like a TV network who’s buying some of our TV shows. We’ll run an Affinio report on that partner so we can better understand that partner and their audiences when we go into the conversation. Let’s say; we’re just selling a TV show to the partner. We’ll run it on that TV show or the topic the TV show covers or a competitive TV show. That enables us to have a conversation about audiences we wouldn’t have been able to have before. I think in that sense, its enabling conversations that just wouldn’t have existed before which is really, really exciting. Suddenly, we’ve got something that’s incredibly rich and powerful to talk about besides the sale itself. That’s amazing.

The Future of Insights

India: Thank you, David, for your time today. I do have one last question for you. You’re on the forefront of insights and innovation with BBC Worldwide and BBC Worldwide Labs. We’re in the homestretch of 2016. Where do you see the research industry going in the next few years and what methodology or insights exist today that will be displaced tomorrow?

David: That’s a great question. I think one lesson for me in the last few years is that you can’t really predict where the innovation is going to happen. In fact, trying to predict is dangerous. For example, me forming a strong view on how dangerous Twitter data was right a few years ago, but it turned out to be completely wrong a few years later. Technology had enabled people way smarter than me to turn junk data into gold.

I love the BBC Worldwide Labs program because people approach the program and tell us what they think we need to do instead of us looking for things that we think we need to do. That injection of fresh ideas, things that you didn’t know that you needed, things that you didn’t know were going to work; it’s been absolutely amazing to experience and help that.

My prediction? That insight professionals who embrace new technologies and new partners that challenge the status quo and that form deep partnerships with them will end up succeeding. Those that don’t will have a really, really, really, really increasingly hard time as the world becomes rapidly more complex around them.

Did you find this expert interview interesting? You may also enjoy my interview with Native Advertising expert, Melanie Deziel. You can check it out here.