Better, faster, cheaper research may be possible after all – Brian Carruthers takes the industry’s pulse at Qual360 Europe.
Quality, speed and price. You can only ever have two of these three in research, so common wisdom has it; at least one has to be sacrificed. But the story at Qual360 Europe suggests that doesn’t have to be the case as the sector moves to adopt new technology and adapt to working with other research disciplines.
Speed is an issue, admitted Dominic King, senior principal, research at Accenture, during a panel session at the event organised by Merlien Institute in Berlin; qual is often seen as slow and expensive – and the results vague. There’s also a worry that the increasing deployment of technology like machine learning in this space is devaluing the role of qual researchers.
It’s a concern for many people, said King, but technology brings benefits to the sector, not least in enabling combinations of qual and quant in new ways that can “alleviate the fear trigger” that clients sometimes have when confronted with change.
Expand the sample and settle nerves with richer data. Use artificial intelligence and natural language processing together, for example, and you can run a 1,000-strong focus group. That supplies the sort of research numbers that settle boardroom nerves while also allowing the voice of the consumer to be heard. King again: “Qual and quant combined is so much more powerful.”
It’s one thing to say 75% of people would buy your product, he pointed out, quite another to see how someone really uses it (or doesn’t) in their own home. “Big brands like Unilever and P&G say they're tired of reported data,” he added. “They want actual data – what people are doing as opposed to what they say they’re doing.”
Angad Chowdhry co-founder of Quilt.ai (the main sponsor of the event) concurred. Offline research of the sort that qual typically involves brings three things, he believes: authenticity, robustness and truthfulness. Quilt’s approach involves layering online data sources on top of that and using AI to execute human insights and anthropology at global scale. “Our data sets are so large, it’s already qual and quant,” he said.
Three brand stories:
- Henkel: a case study of research at speed. The qual/quant crossover and desire for speed was evident in several presentations. As the innovation process has had to accelerate, research has had to keep pace, and Henkel’s Maren Jekel claimed to have cracked the better, faster, cheaper conundrum. “Mission Impossible became Mission Possible,” she said.
The household products business successfully telescoped the innovation process into three months – the time it took a ‘game changer’ team to go from insight generation to validated concept across a total of 24 projects, while meeting internal benchmarks around speed, flexibility and deliverables. Online networks of consumers in Germany and the US provided the “structural research capabilities” that saved time and money.
- Kindoh: insights to adapt product for a new market launch. Korean diaper brand Kindoh used a branded “pop-up community” ahead of its launch in Germany, recruiting 150 parents who over a nine-day period tested the product and delivered both qual research in the form of forums, personal videos, online diaries and video focus groups and quant via quick polls and surveys to rate the diapers – input which led to several small but important product changes.
- BBC: the depth of slow research. Not everyone is obsessed by a quick turnaround, however; the BBC is quite prepared to sacrifice speed for quality. “When an agency told us they could turn round an analysis of the qual in three weeks, we told them do it in six,” reported Jim Davies, senior audience researcher at BBC World Service.
Whatever people may believe about the BBC and its budgets, he doesn’t have a lot to spend when looking at 70 different markets. So when he does qual he tends to do it in-depth – interviews of up to three hours in challenging markets, in order to be able to negotiate sensitive topics, whether culturally or politically (interviewers have been arrested in the past for asking questions about politically loaded topics) and gather as much information as possible.
“A focus on quality adds to the longevity of the research,” he argued. “We think really good qualitative research and the insights that you get from it can be revisited, a year or two years later.
“You can go back to good quality qual research and introduce new findings from quantitative or digital analytics or any other kind of project that's being run.”
And for those worried about tech snake-oil salesmen, Chowdhry had this advice: “Give it [the tech] the most obvious problem to solve. If it gives us an insight that is surprising then we do more R&D on it.”