Why the power of the crowd unlocks breadth and depth in consumer insights
Traditionally, breadth and depth in consumer insights have seemed like mutually exclusive options. All consumer research is done with the same end goal in mind: to bring the voice of the consumer into the process of unlocking progress along the innovation path and ultimately achieving in-market success.
Since time immemorial the industry has been defined by the division between qualitative and quantitative, both on the sides of buyers and practitioners of consumer research.
The Shortcomings of Focus Groups and IDIs
Focus Groups and In-Depth Interviews (IDIs) have have long been viewed as the cornerstones of Qualitative research– because they are considered to provide greater depth of insight. They are designed to provide a direct view into attitudes, emotions, and behaviours, which is essential for understanding the nuances of consumer decision-making.
However, both methods exhibit significant limitations which have been well documented.
Attending a Focus Group is an enjoyable day or evening out of the office, it’s something people really look forward to. You get fed, watered, and get to do some people-watching behind a mirrored screen. It is likely that the discussion will generate – or, most likely, validate – some ideas, reactions or hypotheses that you already had. However… you’re observing people fully out of context; taken in isolation this can be deeply misleading and potentially dangerous. You need to pair what you might learn from this environment with unfettered quantitative validation of this observation, with the consumer in a much more natural habitat.
By design, these methods (while allowing for greater depth) sacrifice breadth by only being able to speak to a very small number of people, largely for reason of “data acquisition” cost. High cost to assemble this small group of individuals (even higher for a single IDI), high cost to moderate, high cost per individual respondent to analyse and generate insights. There are always cost-to-design compromises being made in an attempt to achieve some semblance of “representivity”, but that circle cannot truly ever be squared.
From an insight generation perspective, the main drawbacks of Focus Groups are caused by the nature of group dynamics as explored in depth by Stasser and Titus (1985) amongst others.
Focus groups are at risk of:
- Groupthink
Participants may conform to group opinions rather than express their true feelings or thoughts, or be constrained by social acceptability. - Sensitivity censoring
Participants may be reluctant to share honest opinions on sensitive or controversial topics in a group setting due to fear of judgment. - Dominant Participants
Strong-willed or vocal participants may dominate the discussion, overshadowing quieter group members. - Conflict – or conflict avoidance
Clashing personalities or differing viewpoints can derail the conversation or make participants uncomfortable.
Focus Groups – and even more so IDIs – suffer from limited generalisability: small sample sizes and the subjective nature of focus group discussions mean the findings can never be statistically representative of the larger population.
Throw in the potential of moderator bias (or individual skill level) with the fact that it’s an artificial environment far removed from real-world behaviour, and the limitations begin to multiply.
And then there are the considerable cost elements: recruitment, incentives, travel, refreshments and venue hire (if face-to-face), viewing facility, highly-skilled moderators, simultaneous translation, transcription, etc. All of these costs are significant practical consideration factors which usually land us with a design which is even more of a limiter to the breadth and representivity of our Focus Groups or IDIs.
Finally; the considerable difficulty and cost of analysis
Qualitative – or unstructured – data takes a lot of time to sift through, find patterns in the responses, distil into pertinent themes and stories before arriving at actionable insight. This is best done by the hands and minds of highly skilled insight professionals, and with that skill comes cost.
Anyone who has wanted to add open-end questions to a predominantly quantitative study in order to understand the “why” behind the “what” will testify to the fact that they often shy away from doing this because of the sheer volume of unstructured data this generates, and the time it will take to properly analyse and codify.
IDIs, while allowing for more individualised insight, are constrained even further by their small sample size. A typical IDI involves a single respondent, whose views, although valuable, will almost certainly not be representative of the wider consumer base. This limitation is compounded by the absence of group interaction, which can fail to capture the social and cultural dynamics that often influence consumer behaviour.
Crowd-based research: Scaling depth of Insight
Here at Catalyx we have long been proponents and practitioners of Crowd-based research in overcoming many of the shortcomings of Focus Groups and IDIs.
This method lies behind everything we believe in, and everything we do.
Crowd-based research addresses many of the shortcomings of traditional methods by enabling the collection of insights from a larger, more diverse and more truly representative group of consumers. The methodology draws on the principles of collective intelligence and the “wisdom of crowds,” which posits that large groups of people can generate more accurate, unbiased, and nuanced answers than a select few (Surowiecki, 2004). Studies have shown that diverse crowds are particularly effective at problem-solving, outperforming expert panels and homogeneous groups in tasks that require creative and innovative thinking (Page, 2007; Becker et al., 2013).
Through digital platforms, Crowd-based research engages participants in a more dynamic and natural environment, allowing for richer, more authentic responses. These platforms can scale to include hundreds of participants, representing diverse demographics and psychographics, thus mitigating the biases inherent in smaller, more homogeneous groups.
Large, diverse groups are better equipped to identify hidden consumer pain points and motivations that might be overlooked in traditional qualitative methods (Hong & Page, 2004).
At Catalyx we have never shied away from dealing with large quantities of unstructured data, safe in the conviction that therein lies truly transformative insights.
Development of Artificial Intelligence (AI) continues to make this approach ever more attractive, marrying the benefits of breadth (high, representative sample sizes) with depth of unstructured data, analysable at much lower cost than before.
In other thought pieces we will talk in more detail about the exciting possibilities that AI opens up for research.
How AI as a tool could supercharge breadth and depth in consumer insights:
- There’s a strong belief that we will see a blurring of the historical boundaries between qualitative and quantitative, trading off depth and breadth – you truly can have both.
- Dynamic discussion guides as AI leads a participant through a conversational “interview”.
- Quantification of Qual: Theme identification, development of meaningful and specific language lexicons and automatic hard-coding of unstructured data, opening up the possibility of understanding statistical relationships between sets of data which originated from unstructured responses.
- Qualitisation of Quant: doing away with pre-defined “response options” (which can only be built from existing definitions, and therefore limiting) and letting these patterns come out from consumers’ own language.
Proof of Depth: The Crowd’s Advantage in unlocking breadth and depth in your consumer insights
The depth of insight gained through Crowds research has been demonstrated across numerous case studies. For instance, in a project for a major FMCG brand, Crowds research revealed multiple latent consumer needs that were not captured through traditional methods like Focus Groups or IDIs. These insights directly influenced the brand’s product development strategy, resulting in offerings that better addressed consumer pain points.
Additionally, the ability to capture diverse voices and perspectives leads to more reliable, cross-sectional insights. Research has shown that diverse, distributed inputs can often lead to more creative and innovative solutions compared to smaller, homogeneous groups (Page, 2007). This has been validated by numerous real-world applications of Crowd-based research, where brands have uncovered valuable insights that significantly impact their innovation strategies.
Conclusion
While Focus Groups and IDIs continue to be used as tools for gathering qualitative insights, they are increasingly unable to meet the demands of today’s world for both depth and scale. Crowd-based research offers a compelling alternative that blends individual depth with the collective power of diverse consumer perspectives.
By capturing a broader range of insights, Crowd-based research helps businesses move beyond the obvious, uncovering hidden opportunities and offering a more accurate understanding of consumer behaviour. For brands seeking innovation success, Crowd-based research is a methodology that provides the depth of response needed to drive better, more informed decision-making.
It is our strong belief that adoption and development of Artificial Intelligence not only enables this coming together of depth and breadth, but makes it the logical next step for the insights industry.
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Don’t just take our word for it; check out our new website’s Case Studies to discover how our Qual at Quant scale approach has supercharged the success of innovations across industries.
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