Why Conversational AI Leads to Better Insight - Part 2
In this second mini blog about Conversational AI I’m focusing on the advantages of qualitative data at quantitative scale and provide some guidelines for how to create an effective conversation that leads to better insight.
Quantitative data is great at providing the “what” insight. For example:
What xyz need statements people have when buying a category
What xyz brand perception statements people associate with a brand
What % of people say they will buy a new product concept
What % of people prefer ad A to ad B
etc….
However, quant is less good at providing the why, which is where qualitative research comes into its own to understand peoples’ beliefs, motivations, attitudes and perceptions to a much greater level of depth.
To date, quantitative and qualitative have been separate disciplines and separate projects; extending project timelines and costs when a client needs to know both the what and the why. Getting to the what and the why in the same project has been tantalizingly out of reach, the holy grail of market research!
Conversational AI gets us closer to that holy grail. Whilst I’m not claiming that Conversational AI can get to the same level of deep insight as extensive, well conducted qual research, what it can do is provide much more ‘why’ insight than standard online quant research.
Conversational AI does this through use of open ended questions and follow up probing to generate rich verbatim data. Doing this effectively leads to much deeper insight, as illustrated by this client quote following a recent inca project:
“I’ve read it (the report) twice and can’t believe the wealth of insight you’ve uncovered!…this is really great stuff!”
However, it’s not as simple as asking a lot of open ended questions. The questions need to be well structured to engage people and encourage good response and, importantly, smart probing is needed – which is where the AI comes in (more on this in a future blog post). There is also the issue of how to analyze a large volume of unstructured, open ended data at speed – another feature of the AI which I’ll return to in a future blog.
One of the unique things about inca is the Conversational AI not only asks lots of open ended questions with smart probing but there is also the option to use qualitative projective techniques at scale. These can get to even deeper levels of insight.
For example the guided fantasy projective takes participants on a journey to a brand planet (e.g. planet Apple) and they are asked what their feelings are on the journey, how they feel when they get to the planet and then, perhaps, how they feel as they leave planet Apple to journey to planet Samsung. Hopefully you can see how this type of projective can lead to more insightful answers than just asking a participant “how do you feel about Apple”.
Another example of a projective technique we commonly use is ‘Treeman’. This projective is particularly useful to get people to express frustrations, fears, hopes, aspirations etc. and, as such, is really insightful for early stage innovation research. However, it also has a number of other useful applications, for example to get a deeper understanding of how people feel about their job, as can be seen in the screenshot taken from inca below.
Next week I’ll write about the AI in conversational AI and how it is used to probe the open ended answers.