Why consumer research is like fashion, and the role of your staples
I didn’t realise consumer research is like fashion, but a webinar helped me see it & value “old staples“.
Given topical concerns, about gender balances in work, it was interesting to experience a female bias. Joining a useful webinar, hosted by Quirks and with speakers from 2020 Research, I began to feel out-of-place.
Our hosts were women (I’d say younger, but most people are beginning to feel younger to me) & audience was mainly female.
What helped me get a feel (for what it must be like for women in male dominated events), were the analogies. Focussed on the topic of why old research methods are still relevant, the analogy was the world of fashion. I lost track of the number of times older research methods were compared to the staple of “the little black dress“. The analogy worked well, but much of the language used reminded me how unaware I can be of gender bias in content. A useful reminder.
Why consumer research is like fashion – choose your clothes
To kick of the use of fashion, as an analogy to the world of research, our hosts shared this famous quote from a legendary designer:
“Fashion is made to become unfashionable” Coco Chanel
We’ve shared enough previous posts to evidence the growth in new research methods & tech:
- Behavioural Research methods
- Implicit Research methods
- Use of technology in research methods
- Innovations that help with Insight Generation
- Future proofing your research plans
The comparison made on this webinar, was that different research methods are like different types of clothes. Two examples are:
“The Tried & True” These are your staples. Use of methods like Quant surveys, In-Person Focus Groups, Online Qualitative. Today their use can feel basic., but they are also proven & so have stood the test of time. Even if they don’t feel as fashionable.
“New & Shiny” These are the new research methods/innovation creating a buzz in the media. Examples change, but currently might include: Automation; AI; Prediction Models; Big Data.
Debunking 3 myths about research methods & what you should wear
The right way doesn’t have to be trendy. Some clients (and some agencies) might bring pressure to bear to use new methods that seem exciting. But, the focus should be on the insights needed. The business question/need should guide method selection, not the other way around.
The right way doesn’t have to be boring. Once you are need based, the why before the how, you should consider all methods. Some innovations can really help with specific questions. This is often the case when combined with more proven methods. Female friends inform me, this is akin to accessorizing wisely (but I’m far from a fashionable dresser).
Data is not Research. With so much focus on Data Science, Big Data & AI in the worlds of analytics & marketing, it’s also impacting research. On the positive side, there has been a push to improve statistical rigour & efficiency. sadly, there has also been some confusion. Just conducting behavioural analytics is not research, there is still a need to dig deeper. Market researchers are in danger of giving up their key differentiation, if they the search for “why”.
Some case studies on choosing the right research method to wear
Helpfully, our hosts then went on to bring these principles to life, through sharing 4 brief case studies.
- “Trend Spotting“: An example of a client with a vague brief, of global trend spotting, that risked esoteric answers. Rather than using Big Data or automated social listening, the answer proved to be improving the question. Once asked “what are you going to do as a result?”, there was a clearer need. The challenge proved to be how to connect with a segment of early adopter consumers. Rather than just getting some words, from social analytics, layered qual & quant questioning worked. Online, but also using traditional methods from surveys & focus groups.
- “Parking Lot Confusion“: Here the challenge was to find the ‘why’ behind quantitative answers provided to ‘in store’ survey. Rather than IoT tracking being the answer, an innovative version of qual follow-up worked. Consumers were prompted to record a video via app to answer open question about reasons for their survey scores. It proved a real hit with customers & provided great vox pops to generate insights.
- “Community Overkill“: An example of a trendy method that is being over used. Too many clients are relying on an online community as default research tool. Risks of selection bias & communities becoming stale or institutionalised. Identifying recent cohorts, at different stages of CX, helped this client get faster qual & quant feedback. These people, outside community, helped provide insights not coming from the usual suspects. For some clients, ethnographic research also brings this to life in context.
- “Social Listening“: Another overused method. Despite all the hype about Big Data, volume does not equal value. Too often this output provides, even with text analytics, just a bunch of disconnected words or themes. That can’t prompt actions as lacks context or opportunity to test solutions. However, it can provide a useful prompt to deeper work with targeted qual & quant validation. Back to that accessorizing idea (I think).
One common theme that appeared in all four case studies was the challenge of time. Too often traditional research methods are rejected as they, “take too long“. Finding ways to deliver robust qual and quant findings quicker, may be the key to continued usage.
The key will be how to ensure still have robust findings, context & can generate useful insights. Just providing speed, at the expense of quality, will risk the same pitfall as mindlessly using new methods.
Sounds like a topic for another blog post…
Meanwhile, I am happy to share this recording from the full webinar. Many thanks for Quirks Magazine & 20|20 Research for sharing such a useful viewpoint:
Would you say your research is like fashion? How trendy are you?
I hope you find those reflections useful. Did they ring true with your experience of choosing research methods?
If you have more case studies to share (either using new or traditional methods), we’d love to hear from you. Please comment using the link below, or reply to this post when shared on Twitter or LinkedIn.
Let’s keep sharing which research methods fit us best. But don’t “tell me my bum looks big” in this method!