Analysts and data scientists who dismiss research are missing the point – there is huge potential to add richness and depth through the amalgamation of disciplines.
Realise has been part of Unlimited for more than a year now and time has certainly flown. It has been an amazing journey, but one of the best things that has happened is the ability to scratch a long-standing itch by working with the wonderful folks at Walnut Unlimited. That ‘itch’ is in part based on my early career where I started out in market research before moving to the dark arts of analytics and data science. But it is probably better encapsulated by a conversation that I had some years back. Here is a (heavily paraphrased, partly redacted and certainly warped by time) flow of the conversation:
Researcher: “We have done some research and discovered that users of our online platform averaged almost 12 visits over the last three months. Can you please validate that?”
Me [Analyst]: “Of course.” and a short time later “We’ve looked at our data and our users have only used the platform 4.8 times on average over the last three months.”
Researcher: “How can that be – we’ve asked our users? We know that there is always some error but not that much as it averages out.”
Analyst: “We’re looking at this the wrong way. Both datasets are correct but they are looking at different truths – one is behaviour and one is perception. But this is exciting – we know that usage drives revenue for us. Maybe they believe they are visiting our site more frequently because they feel guilty about overuse. If we put a counter on the site, they could see they are using it less than they think, which could have a significant positive revenue impact for us. And we can look at the visits that they are remembering as being recent but were a long time ago – are there certain interactions that happened on the site that resonated with users that we should try to amplify or repeat?”
Researcher: “I think your data is wrong.”
There has long been a disconnect between research and analytics – when research doesn’t tie back to hard metrics, there is a perception that it is somehow wrong. But to me, this information is a critical counterpoint to behavioural data. Data science has long been able to tell us the what, when and how things happen – and that information is getting wider, more granular and a lot more interesting. But we’ve never known the why. We can often infer, but we don’t know until we ask – which is the power that market research brings.
Thankfully, that trend has been changing over recent years as data scientists and analysts embrace more diverse data sources (research included) and I think there is a genuine opportunity to bring these disciplines even closer together to better understand customer emotion, motivation and action in a holistic solution.
Which brings me in a full circle. Working with Walnut is scratching my itch by bringing together the what and the why. And it is leading to some exciting propositions and products – hybrid segmentations to name just one. Identifying the reasons and opportunities between behavioural data and research data has led us to our perceived-reality solution and there is plenty more to come. It’s been a great journey – long may it continue.