How leading insight teams combine research and data analytics – part 2: Just Eat

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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, Rufus Weston, Head of Insight at Just Eat, shares his story.

Just Eat is a smartphone-based food delivery platform operating in 13 markets around the world and headquartered in the UK.

 

MS: How does research and data analytics come together at Just Eat?

RW:

We have 3 sources of consumer insight here: the UX research team, the data team and the insight team. I lead the insight team but work closely with the other two.

In the past, the consumer story wasn't always consistent between those three sources. We'd even see contradictory data. But we've tackled it through structure and - more importantly - culture.

Structurally, an organisation change helped to join things up for us. The data analytics and insight teams now report into the Chief Customer Officer.

But the organic connections between teams make at least as much difference as reporting lines. Our 'Insight Network’ of practitioners from all three areas meet every two weeks; and together we produce consolidated insights on the same cadence.

Every project starts by reviewing what we know about a given topic, so we have a coherent narrative joining up all sources of data. For example, on recent work about hygiene ratings for our restaurant partners, we built a 'single source of truth’ to kick off the project.

This now happens with all major initiatives, and the insight review becomes a living document. It identifies gaps to be filled by each team, and then feeds into product development on ideation projects.

As we progress in building our knowledge, the insights in that document are validated or adjusted further down the line.

 

MS: What different skills and roles do you need for this new world? Do you hire them or train them?

 

RW:

It helps to have data literate stakeholders. We’re lucky at Just Eat - we’re a data-native business. The hiring process at companies that run on data - Amazon, Netflix etc - tests for data literacy. Like them, we need people at all levels in the business who understand data and insight.

I previously worked in publishing, where this level of data confidence wasn't so much part of the business culture. For certain types of work, it made it harder job to land the insight with stakeholders.

For insight people though, telling stories through data has always been a core skill - so I don't see this being such a big leap when we're working with different types of data.

The best insight people lead with the commercial narrative and can translate their analysis into relevant actions for their colleagues. I actually think good researchers have the edge here: expert data people can sometimes be more technical, and less familiar with the business context.

My team's strength is in interpreting and explaining outputs from the data team and repackaging them with clarity. The insight team are all fairly senior, and we outsource junior work to plug-and-play software platforms or to agencies. The insight team is more of the interface between the data team and rest of the business.

 

MS: What new data sources or software tools are you working with now? What benefits and / or challenges do they bring?

 

RW:

Everyone uses Tableau for analysis and reporting. We have also rolled out new tools like Chattermill for analysing feedback from CX surveys and reviews; and Usabilla for in-app feedback.

This brings advantages, but it also leads to other hurdles we have to navigate.

It's great to have access to your own data, to have the immediacy that these tools bring.

But we're a lean team, so we also need these tools to democratise access to insight: if we can give colleagues direct access, it reduces the load on us.

But it can be hard to get people to use them, and that can be deflating. The onus is still on the insight team for interpretation - and in some cases for basic handle-turning.

For example, my team have been producing PowerPoint reports to summarise customer feedback from the Chattermill platform. These reports go to product managers, who love reading this stuff. Over time, we are transitioning them to working directly in Chattermill. The key is to get our internal users hooked on the content first, and then get them using the tool themselves.

But it's a journey.

 

MS: Can you talk about any tangible benefits you've seen from joining up these different data sources, skillsets and tools? What commercial impact has it had?

 

RW:

We've been rolling out a big change in our app, which has been the inclusion of food hygiene ratings for our restaurant partners.

Looking separately at each data source, we could have made quite different decisions.

In customer surveys, hygiene ratings showed up as an important feature that customers wanted.

The analytics - based on trials with sub-groups - saw some reductions in average order values for some restaurants.

And in the UX research, some users said they would prefer not to know.

Ultimately, our commercial direction was clear: we wanted to introduce hygiene ratings because it was the right thing for customers and the right thing for our long-term commercial model. It also meant introducing a programme to improve restaurant ratings and meet customers’ expectations, which is what Just Eat is here to do.

Bringing all our sources of insight together at the start of the project meant that we had a full picture, understood the nuance and trade-offs better, and could optimise the rollout better.

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