Insight is changing fast.
Understanding users and consumers now involves a crowd of related teams, skillsets and methods: research, data science, UX feedback, customer experience, digital marketing, e-commerce, business intelligence …
Insight leaders are juggling all this complexity as they transform their teams. They are contending with new data sources, software tools, team members, reporting lines and stakeholder expectations.
So how are they doing it?
That question was the inspiration for a panel discussion at the 2019 IIEX Europe Conference. Four insight leaders from Nestlé, Turner, Just Eat and Mail Metro Media shared their experiences as they navigate this fluid environment and build combined research and data analytics teams.
In this series of short interviews with these leaders, we touch on 4 key aspects of each brand’s insight transformation.
In this article, Pedro Cosa, VP Data & Consumer Insight at Turner EMEA, shares his story.
Turner is part of WarnerMedia, a division of AT&T. It operates children’s entertainment brands including Cartoon Network, Boomerang and Cartoonito; and general entertainment properties including Adult Swim, TNT and Turner Classic Movies. In EMEA, it is present in more than 20 markets.
MS: How does research and data analytics come together in your business?
In our business, we have huge amounts of first and third party consumer data: TV audience measurement panels, website analytics, YouTube viewing, usage data on our apps and so on.
We have an increasing remit for analytics, and have digital reporting and data science specialists who complementing those who have more experience in primary research and panel data
The aim is to be data source agnostic – to build a holistic understanding of our consumers by combining different data sources in the best way we can.
Not all countries are evolving in the same way or at the same pace. For example, the move away from linear TV to digital platforms is happening much faster in the UK and Scandinavia than it is in Southern Europe. So our approach to insight needs to reflect this variation.
But in general, we are moving towards a much more integrated research and data analytics framework.
MS: What different skills and roles do you need for this new world? Do you hire them or train them?
We need to get ahead of the changes in consumer behaviour so it’s essential that we add skills in digital insight.
It will always be a mixture between acquiring fresh talent – hiring people with strong backgrounds in data science and digital analytics – and evolving the skills of the existing team. It’s essential that we have people who understand the business, the marketplace and our competitors. Domain knowledge is incredibly valuable, and we need to retain that because it takes a long time to build.
Everyone is looking for data science ‘unicorns’ – people at the middle of 3 overlapping circles (statistics, computer science and business expertise). They are incredibly rare. And it’s the commercial pragmatism that is often the hardest to find. Data that gives us 80 percent confidence is better than a coin toss; but very often, data scientists can fall more on the academic side and want to get to a 95% answer.
This is where I feel that our experienced researchers give us a good dose of pragmatism – they know what the business needs and how it works; and they know when we have enough data or confidence to make a decision.
So it’s about getting these different skills to work well together in the team. Some companies operate a model where researchers act as ‘analytics translators’, filtering between the data scientists and the business.
This can work, but I tend to believe the best model is to have these different profiles within the team: both those who specialise with expertise in data science, and those who can bring the business needs and analytics together.
In that sense I think you need to have certain positions filled with pure specialists and other positions filled with people who can bring the knowledge of the business and the ability to understand how new methodologies can help to solve the question. The latter group are the “translators” who bring a lot of value to the equation.
MS: What new data sources or software tools are you working with now? What benefits and / or challenges do they bring?
We’ve invested in lots of new tools. We have business intelligence dashboards, TV audience panel data from our EMEA markets, website and app analytics tools, specialist platforms for video analytics, competitor website and app performance and so on.
Our biggest audience across EMEA is children – so this adds another layer when working with data or choosing a new platform, as what’s essential to our approach is to be extra mindful of parental consent and permission.
We use carefully chosen social tools to help gather parents’ opinions about our children’s brands and content as well as younger adult audience views on young adult brands.
MS: Can you talk about any tangible benefits you’ve seen from joining up these different data sources, skillsets and tools?
First and foremost it’s about understanding the behaviours of our fans; in better doing so we can use that to inform the kind of content we offer them.
We have run some attribution analytics projects to demonstrate the commercial value of cross-platform campaigns with some of our key franchise brands.
And we have a long term programme in place to create a harmonised KPI framework for our business. This means combining different sources of performance data across platforms (TV, web, YouTube etc); and building deduplicated audience models that use both survey and behavioural data, all combined to give us that fan understanding that will in turn help us deliver more of what they want, where they want it, and that in turn drives the success of the business forward as a whole.