Qualitative data is by its very nature more difficult to analyse than quantitative data. Quant data usually consists of numbers and stats… lovely, logical numbers and stats; qual however brings in statements, feelings and even the intensely valuable opinion. Any researcher worth their salt though knows that qual data is often the exact gold that you mine every day and is like taping in to a user’s mind to get an insider’s view on their attitude to you as a company. Qual research has had it’s issues throughout time though and was rightly seen as the more difficult research type to run and analyse.
Old School Qual
Old school qual research took time, not hours, or even days, but weeks and even months of interviewing a sample either on the phone or in person, and then weeks more of collating and analysing this data which would still only really count as ‘small data’ when measured against any kind of quantitative ’big data’ scale.
|Qual data by its nature is more difficult to analyse the quant, but not for much longer. There is now technology available to help streamline qual insight generation, distribution, and activation processes.|
The early days of the internet weren’t really making any difference to this either and until pretty recently the internet had a real demographic issue, under 45's were using it and would give great feedback and basically helped to create the age of big data. Over 45's though, and especially those over 60 hadn’t exactly taken to the internet in their droves and as such couldn’t be used for research purposes. As well as the demographic issue, there also weren’t really the tools that were needed to gain qual research on the web yet.
Online Qual Tools
Not long after the internet became a feature in everyday life, qual tools began to appear that allowed for at least some qual research to be done online and take up far less time. Verbatim responses and video responses within a survey for instance give you the ability to get qual data at a scale that hasn’t previously been possible.
It used to be that if you wanted a video response from 1000 people it took weeks, now with a big enough sample and a tool such as a Video Booth question type lets you get this in a day. This coupled with the massive increase in people getting online (necessitated due to the pandemic) means that big data doesn’t just consists of numbers anymore. Whilst the myth still persists that qual data can’t be scaled, we at FlexMR call false on this and have addressed the potential issues and methods in our previous article ‘Qual at scale'.
We have developed one such tool to help facilitate the collection and analysis of video data sources, VideoMR. This tool automatically stores the video research data collected through the Video Booth feature on the InsightHub platform, so members of the insight team and stakeholders can access the raw footage as soon as it has been recorded. We can create montages from different clips, sort and group videos according to customised criteria such as theme or project. It’s tools like this that are now emerging in the insights industry that are crucial to the scaling of creative qual research for rich insight generation and distribution.
So, we know we have the tools at the ready, how can they be used to sustainably scale qualitative insight generation?
Sustainable Scaling Qual
There is no one way of scaling qual research, and there are a few considerations to take into account, but everything will all depend on your capabilities as a research team and the projects you require. To know which tools you want to take advantage of, you need to first decide: do you want to scale qual data collection, qual data analysis, qual insight activation, or all three? Most of the time, all three is desirable. But for those starting out, maybe master one at a time until you get good at working with all three stages.
Insight teams can enhance their capabilities instantly by using online qualitative tools that easily allow rich qualitative data capture and analysis; but sustainably scaling qual is also knowing where each new enhanced limit is set, and gradually increasing this by adding the right tools to your research technology stack, the right skills into your insights team to actively streamline qual research processes and expedite the generation, distribution and activation of qualitative insights. Only then we will sustainably understand how to sustainably generate qualitative insights and allow them to inform, clarify, and enrich the insights already in your database.
Doing It Yourself
While huge brands have great examples of using qual research to understand what their customers want, that doesn’t mean that it’s off limits to all but the biggest brands, qual research at scale gets easier every day. So, it’s easy to obtain the data, but what about changing those opinions and thoughts in to actionable insights? Well, there is no point trying to read and digest all that qual data and you’ll want to begin to translate your data into a format that is scalable.
One of the easier forms of data to analyse is through keyword tracking. With videos, it can be hard to go through all of that footage every time you want to clarify an insight, but auto-transcription software can be fantastic to help convert that data into an easily searchable format so we can begin to understand key sentiments that crop up.
|Scaling qual is one challenge, sustainably scaling qual research is another. How can we make sure that we are doing it smartly without compromising on quality?|
An auto-transcriber is going to be able to convert those thousands of video clips and recordings into a collection of words that you can then begin to analyse. Some words are of course more useful than others and a little common sense is required, otherwise you will come to the conclusion that all future products and services should be called ‘The’ or ‘And’. Keep in mind the question that consumers are responding to however and you will quickly gain a list of words that crop up repeatedly, from which you can begin to build your insights.
Follow Up for Co-Creation and Clarification
Whilst you can easily begin to get an idea of trends within this qual data you can easily become overwhelmed with the commonalities between peoples feedback and filtering is needed. The first process that will allow you to cut down the threads is grouping them together, roughly similar feedback can be grouped together into themes for more clarity.
Once you have created these themes then a follow up exercise can be an excellent way to burrow down to and distil the exact consumer insights to focus on. Whether this be in the form of a short poll to for instance define the most popular of the insights or a more in-depth exercise such as a survey, following up will allow you to take narrow down your insights.
So there you have it, a few simple (ish) steps and you’ve taken yourself from a mass of qual insights and scaled these down several steps to give you rich understanding of your consumer requirements.