“Why can’t you get me that research faster?” That is perhaps the most common challenge I hear is faced by research leaders .
I pointed out, in a post of ‘ research as fashion ‘, that a common theme in case studies, was the challenge to get research faster. So, as promised, this post will share a number of relevant posts and resources on that topic.
Let me first state that faster is not always better. Aside from the wider “ In Praise of Slow ” movement, our previous posts on “ Thinking Fast & Slo w ” & “ Deep Work ” make this point.
Faster work can mean lower quality thinking & be more susceptible to the biases of brains on auto-pilot. So, it can often be worth pushing back, when more robust data & careful interpretation are needed.
But, we like to be pragmatic here. In real businesses, whatever the pros & cons, customer insight leaders need to deliver quicker. What are some of the options to achieve this with research work?
This post shares some possible solutions, as well as advice to consider…
The diverse role of technology, in delivering research faster
Although it’s easy to assume that the rise of AI will be the silver bullet for market research, in reality IT is helping in a range of ways. From faster access to more diverse data sources, to self-service platforms & media for debriefs . It can be difficult to keep pace with all the options available.
So, I hope this post, from Sarah Schmidt for MarketResearch.com , is helpful. They interview 23 different research leaders, who each share how they see technology helping. Two things strike me. First, there a variety of ways that different technologies can improve different parts of the research cycle. Second, there is not yet one solution, and perhaps that is a good thing:
Related: Why Consumer Research Is Like Fashion: Choose Your Clothes
Will Big Tech transform the speed of survey execution?
We shared in the past, the potential for Microsoft’s free software to own the online research space. Since then, other Big Tech players have entered the market, particularly focussed on online surveys.
A good example (despite some sound quality issues) is this recorded webinar from GreenBook’s blog . At this event, Monica Plaza, Head of Sales at Google, explains Google’s Consumer Surveys product . Which includes their leveraging of Google Play credits as flexible incentive.
It is interesting that Google are seeking to address both quality issues & speed to market. They use their scale (data reach) to enable better sample quality & more relevant questions for participants. I’m reassured to see their focus on statistical rigour as well as a simpler survey experience.
The actual usage of a product is never as smooth or ideal as a product demo, but I recommend at least hearing what Google has to offer:
Can automation deliver on its promise of making research faster?
Finally, in this short post, let me turn to the promise of research automation. Those following our previous posts on research innovations & technology trends, will have heard of this. It feels like, every annual prediction of market research trends , predicts research automation as key theme.
However, most research leaders I meet, still describe a largely manual workflow with lots of human intervention. Why is this? With some many different technologies available, why is MRTech not as big as MarTech or AdTech?
These two posts from Quirks Magazine , help explore the challenges more fully. Although both begin to a look a little dated now, they raise important points. The first includes a survey of research leader demand. In that we see that a number of technologies are already being used. One I haven’t touched on so far is how automated data visualisation is improving results presentation.
Related: The Future of Market Research is Behavioral
But, as leaders make clear at the end of that article, there is still a need to be more flexible. Each research request may not need a bespoke solution, but a focus on the question not the method, requires more flexibility. This requires both support for a broader range of research methods & more integration between tools.
This second post, from Quirks, shares the results of a report into the pros & cons of research automation. As well as restating the potential promise (much as described by Google earlier), it helps by making clear ‘ The Ugly ‘. This downside risk, includes aspects we may never want automated. The humanising impact of face to face groups, and human reflections on interpretation of results.
How are you making your research faster, or do you just say no?
I’m really interested to hear how you are responding to this challenge. Which technologies, if any, have you adopted, to speed up your delivery? Have you found other methods or (at least partial) solutions?
Please feel free to share your experience, including of saying no to needing faster research. Best wishes for a fast, but also insightful, future.