How qual and quant customer insights combine to build a rich and measured view of experience

As the first qualitative researcher in a travel experiences business, I have been raising awareness of how qual and quant differ, and can be combined to support product decisions. Particularly if you need to:

  • Explain paradoxes
  • Augment and enrich clues
  • Understand prevalence
  • Ask the right questions
  • Frame hypotheses

Sam Ladner’s Mixed Methods book and Erica Hall’s recent articles on badly designed surveys (links referenced below), have offered rich food for thought and guidance on this theme, so I credit and thank them both, for this.

Why you might need to start raising awareness about mixing methods?
Traditionally we rely on product owners to connect qual and quant insights and use them to make decisions. Personally I’ve found that people who understand the pros and cons of both qual and quant, and how to sequence them to leverage their value, can be a rarity. Also,  relying on one person to flit between the two ‘states’  can be challenging, particularly as qual and quant embody different but equally valuable mindsets. Sam Ladners describes this as two views of the truth, which I’ll attempt to paraphrase below.

If we think of the double diamond, The qualitative mindset is divergent and generative. Qual research involves building understanding, exploring the complexity and nuance of a person’s reality and appreciating that this is constructed. For example my perspective of ‘well being ‘ is based on my experiences, my mental models… the highs and lows of those micro moments and my interpretation of that. This as a constructivist perspective of reality or an understanding that truth is fluid and built.

I m primarily a qual researcher, and so, from my limited understanding, the quant research mindset is convergent, as it focusses on correlation and deduction. Ideas are proven or disproven. Logic is applied to arrive at a conclusion. Concepts fit into neater categories. The truth is objective and the facts are stable.

When might you raise awareness?

There are a couple of scenarios where I wish I’d known about mixing methods sooners and taken ownership of it. Here are some:
  • a team where qual research was respected and championed, but run in a silo from split test and analytics teams. Opportunities to split test or advise on tasks to measure sucess for, were ample, but hard to deliver as a result. KPIs like sales and KPIs, were high level harder to action.

  • a team where market research was well established, and focus groups were the main recognised source of qual.


So how might the two mindsets interface with each other?
This is something I am still working out. However, a few things have worked well when I’ve collaborated with analysts, data scientists and market researchers in the past:
Broadening  a theme
Credit: unsplash, imre-tomosvari
Qual and quant researchers migh Introduce a broad topic – let’s say it’s ‘how customers plan’. Then find a few hours to independently explore what data points and past research you have about this. Present ideas back to each other. Qual researchers and analysts can  use clues to discuss takeaways, interpretations and questions they have. These can then become starting points for future activities.
Framing assumptions and hypotheses
Unsplash – Dewang Gupta
Quant and qual researchers can walk through  data,  audit journeys, and look through past insights to brainstorm opportunities. These can be framed as hypotheses by discussing what changes if an opportunity is explored.
Scaling or working out prevalent issues are
Unsplash – Nithya Ramanujam
A walk through or formative user test, may offer ideas on potential pain points. Error or bounce rates can triangulate this and offer ideas on how widespread these issues are. Similarly interviews may offer clues on how people plan a trip. A survey can scale how prevalent those behaviours are, for who and perhaps where. Surveys are reliant on self – reported data, but can offer an indication.
Explaining paradoxes or anomalies
red lights in line on black surface
Photo by Pixabay on
Quant methods may reveal high drop outs or low csat in a region. Qual  methods can fill in the colour here and explain why.
Explaining correlation
Unsplash – Maria Teneva
Quant methods may reveal a pattern – for example, people who eat breakfast are less likely to be obese. However, qual would be needed to understand why breakfast eaters are healthier. Maybe their activity levels make them hungrier? Maybe they have more energy to burn off? We can only speculate until a qual method is used.
Augmenting and enriching knowledge
Unsplash – Tim Mossholder
Quant methods may indicate how well a feature is performing or converting to orders. However without the nuance offered by qual -particularly formative creative methods, the unmet needs and challenges facing people, beyond the solution you offer them, blindspots to support innovation are missed.
Asking the right questions
Unsplash – Pawel Czerwinski
Erica Hall describes surveys as a shotgun – they’re very easy to run, but can lead to damaging skewed findings, if they’re poorly designed and analysed. Qual ‘clues’ can shape options in a multiple choice question, to ensure options are exhaustive, specific and mutually exclusive.




If you’re a quant researcher, how have you found working with qualitative mindsets? What worked well or could have been better?

Qual researchers, how did you know when to switch from a divergent to a convergent state with your research? What were the signs or clues? Whatever stage you’re at, I’m keen to hear how others have combined methods.


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