Why Conversational AI Leads to Better Insight
After my first week working with inca from Nexxt Intelligence, I’m even more excited about Conversational AI and its power to reimagine online research for better insights.
So for the next few weeks I’ve decided to share a series of weekly mini blogs on this topic to explain why anyone working in market research, marketing, UX, CX, innovation or creative development should be excited about Conversational AI.
Firstly to give a brief explanation of Conversational AI…..
Conversational AI leverages the latest advances in machine learning and natural language processing to create compelling conversational experiences. It does this through a chatbot interface to ask questions and converse with participants.
Participants can be asked a range of closed, quantitative, and open, qualitative, questions. NLP is used to respond to participants open ended responses in a way that feels natural. The experience for participants is more akin to having a WhatsApp conversation with someone than completing a traditional online research survey. The AI is also used to analyse the open ended data, theming it for quick analysis.
Fundamentally there are two reasons why I think Conversational AI leads to better insight:
1. Better data quality
2. Qualitative insight at Quantitative scale
This week I’m focusing on better data quality. For the last few years the market research industry has often been guilty of behaving like ostriches when it comes to data quality. Most market researchers are aware that some of the data they’re getting back from online surveys isn’t great but many of them bury their heads in the sand and try to ignore it.
To be fair, the panel companies have been making great efforts to address the data quality issue through various quality checks, for example to weed out bots or respondents who speed through surveys or give a flat line response to questions. However, experienced researchers who have taken the time to go through data line by line have told me that they are still deleting up to 20% of respondents even after the panel companies checks.
And, of course, more often than not researchers don’t have either the time or experience to make these respondent level checks, so there is the danger that less than optimal data ends up being used with all the risks that entails for poor insight and bad decisions.
What’s not happening often enough is addressing the problem at source. That is, creating a better online survey experience for participants so that they give more considered, better answers to questions. This is where good Conversational AI works much better than a standard online survey.
Conversational AI uses a chatbot to engage participants in a conversation. Unlike online surveys which tend to be one directional, question and answer sessions; chatbots are conversational and conversations are two directional, a dialogue where participants are more reflective and considered in their response and which can be more emotional, more fun and lead to unexpected insights.
A conversational format also mimics the way in which people spend most of their time interacting with others online, especially on mobile, for example through WhatsApp conversations.
Good Conversational AI, such as inca, is built on qualitative principles - in qualitative research making participants feel comfortable, engaging them in a dialogue and ensuring they feel listened to are key to generating good insight. These same principles are applied to a good Conversational AI survey.
This means it’s important when considering Conversational AI that you don’t just use a chatbot designed for customer experience interactions that has been re-purposed for research. Chatbots used in CX are designed to give answers, whereas for a chatbot to be effective for market research it needs to be designed to ask questions.
Next week I’ll write about the advantages of qualitative data at quantitative scale