One of the ways that people are using ChatGPT is to create more standardisation in the questionnaires and discussion guides they produce and, at the same time, speed up the production of a first draft. In this post I show how to use ChatGPT to create and use these libraries.
Two or three times a year I receive a message from an agency saying their client wants to do a study using the Kano Model. They ask whether I can explain it to them so they can respond. In the future, they will get a faster and cheaper answer by asking ChatGPT (or any other leading AI platform). I also get queries about various techniques, such as MaxDiff, ServQual, and Driver Analysis. ChatGPT will respond faster and cheaper than I will to all of these.
Here is another tip from my courses on how to use Copilot and ChatGPT for market research and insights. Generative AI can be used to ask for suggestions on how to do a specific task. For example, we might ask the AI platform to design a research project. However, I have found that it is a good idea to ask for multiple suggestions.
Should you use the web-based ChatGPT or create routines that call the ChatGPT API? In this post I set out my advice.
Do you have a set of open-ended comments that you’d like to analyse, for example from a survey? With ResearchWiseAI you can do that. You can read how to do it in this post.
One of the neat features of ChatGPT is the ability to create Custom GPTs. A Custom GPT allows you to build a bespoke tool on top of ChatGPT, creating a unique product. In this post I am going to tell you how I created a simple ‘Virtual Ray’ via a Custom GPT.
Here is my take on the differences between Microsoft’s Copilot and OpenAI’s ChatGPT and some thoughts on which is best for you.
In this note I set out what I believe to be Synthetic Data, why we need to define synthetics data, and some guidelines that I think vendors and buyers should adopt. I have been involved in a wide range of discussion with a wide range of organisations, but these views are my views, they do not represent the views of anybody else.
Last week saw over 1000 insight and market researchers from 78 countries gather in Athens for ESOMAR’s Annual Congress. With three stages and a host of side events, the range of topics discussed was immense, but one theme stood out above all the rest and that was AI. One aspect of AI in particular was widely discussed, reviewed and commented on – namely Synthetic Data.
One of the things that has caught my eye about the recent discussion about synthetic data have been the different reactions to the word synthetic. Whilst most of the debate has focused on the underlying methods, there have been numerous sideswipes at the word “synthetic”. At first this struck me as odd, so I did some introspection and reading to find out why my reaction differed from that of others. I quickly realised that, in many cases, it reflected people’s arts/science background along with various life experiences, cultural phenomena, and interests.
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