Older researchers reminisce about the way they used to do things ‘properly’ – by which they mean the hard, manual way. It’s like saying that it was better when we washed dishes and did the laundry by hand. Yes, it did the job, but machines do it better. However, it is true to say that one advantage of the old ‘hands-on’ research methods was that we were much ‘closer’ to the data and to respondents – it is much harder now to keep that connection – the human touch.
In response to all the changes I have spent the last few years underlining ‘Everyone needs Market Research skills!’ or ‘The Future of research is qualitative!’ – depending on my audience. At first glance these two mantras can seem rather contradictory – but in fact they are the two sides of the market research ‘bitcoin’ of the future. The main places where we need to invest.
Because when all the big data has been fed into all the algorithms in the world – there will always remain the questions: ‘Why?’ And: ‘What does it mean?’
Because when all the big data has been fed into all the algorithms in the world – there will always remain the questions: Why? And: What does it mean?
And to answer those questions we need some very human skills. First we need to understand how good were the sources of the data, to find out what parts of the market we might be talking about. And, secondly, we need to ask why do consumers behave in that way – before we can even start to decide what it means for the business/strategy/policy or whatever it is that we’re trying to take forward.
So we need basic market research skills to understand the validity of the finding and qualitative skills to understand ‘Why?’ – and then we need business skills to turn the finding into a business opportunity or action, and then the communication skills to tell the story.
All these skills do not generally reside together in the same human being. And so, instead of the hierarchical homogeneous teams of yesteryear: of traditional researchers with different levels of expertise, the concept of ‘connected teams’ has developed. Teams which include specialists in data analysis, story-tellers, business analysts, qualitative consumer behavioural experts, and managers with the skill to get the best out of the team.
We do still have teams of people – fewer people than before because the machines do so much of the work – but will we need teams of humans in the future? Will the algorithms be able to do everything, eventually?
What do humans have that machines/algorithms don’t?
When differentiating us from algorithms using words such as ethics, morality, perspective, intuition, empathy, common sense, caring, relationship, it seems to me that these concepts segment into two groups. The first is about broad experience over time which can be brought to bear on a current issue and the second is about attitudes, values and empathy which enable understanding of what consumers want and need.
In the first group the key words are ‘perspective’ and ‘intuition’, which come from deep experience outside of the given situation. It comes back to asking the question ‘Why?’ all the time. If the algorithm tells you that people who wear red socks are more likely to buy your product – you don’t, as a human, (though you might as a machine!) immediately look to collaborate with a red sock manufacturer to market your products – as a human, you ask what it is about red sock wearers that might make them more favorable to your products. Experience, perspective and intuition make you more likely to question and analyse automated results and look behind the perhaps superficial finding.
In the second group the key word is ‘empathy’ – it’s empathy that builds relationships that enables researchers to become the trusted partners and advisors of clients and stakeholders. And empathy that helps you understand why consumers might be behaving in a certain way.
In the past it was relatively easy to find researchers with MR experience of different levels and types, but where should we be looking for our insights professionals when the type of staff that agencies and clients need to be employing has changed.
Experience, perspective and intuition make you more likely to question and analyse automated results and look behind the perhaps superficial finding.
Where to find the right people
Pre-automation we could simply employ good graduates, start them off at the bottom, let them pass through different departments and train them in basic MR skills. And this can still work on the agency side – though I have found it frustrating – particularly in developing markets – that agencies seem to expect a fully formed product from the university – not only the basic MR skills but also the insight and interpretation and business skills now required. They don’t see the role that they should be playing in training their employees. This is both short sighted and a huge mistake. Everyone needs to be learning all the time. Particularly methodologically, things move on very quickly – so what was accepted good practice five years ago may be out of date now and we could end up with bad data. The polls are a particular example, I believe
Obviously we can source new graduates and teach them the details of pragmatic MR – or we can bring in graduates with experience and skills from other jobs – and teach them research skills – but either way we all have a responsibility to them and to our clients to make sure that they understand the research business.
Then we need a plan for what exactly is a high performing research team when it comprises a mix of skills.
What’s the recipe for a good team?
As a minimum the team needs to include at its core at least one person with mathematical skills, an expert story-teller, someone with good qualitative skills, and be as diverse as possible in culture, background and experience.
The mathematical skills are needed to interpret numbers correctly – and also understand the data sources, the algorithms and the overall quantitative side of issues.
Storytellers are required for their writing and communication skills – because unless you can convince the business the team has no role to play. Qualitative skills are needed to bring understanding of human behavior, empathy and the connection to the customer.
The importance of diversity cannot be overstated – we all have a tendency to work with people like ourselves but people like you have similar ideas, so they reinforce your incorrect assumptions, they don’t question them. And don’t be afraid to employ people cleverer than yourself. You can all take credit in the team’s success. And employ people with different cultural backgrounds. The times I see companies trying to introduce products into a new country without any idea about that country’s culture or background – they make very expensive mistakes!
Once you have this minimum diverse mix of skills make sure they are trained in creativity – everyone has creativity in them, they just need to work on it. Also make sure they are all immersed in the real world of the market. Immersion goes in and out of fashion as it tends to be implemented badly in organisations. I once had a client where everyone was supposed to spend time with their customers – and they interpreted this as coming to listen in to telephone interviews occasionally. It was better than nothing but definitely wasn’t true immersion! Also make sure that they are mentored well by others who will give them wider perspective. This can enhance their primary skills.
With this mix of skills and training/development you will have the requisite curiosity and inspiration to tackle most business and research questions.
And it will be a long time before these skills are automated. We humans still have a lot to bring to the party!
