The Market Research industry is finally catching up with Artificial Intelligence
During the past three weeks I travelled 10 time zones east and west of GMT, to present at three different ESOMAR events:
- 1.MENAP Forum 2017 in Dubai on March 22nd
- 2.UK member meet-up in London on March 30th
- 3.LATAM Forum 2017 in Mexico City on April 7th
As a souvenir from Mexico City I brought back a broken foot but …hey….no regrets, it was all worth it.
I have been an ESOMAR member for many years, initially as an agency side executive, and now as an entrepreneur, and overall I mainly have positive things to say about the premier organisation of our industry. I have to admit I was a bit worried at the beginning of this decade, mainly about the pace of adoption of innovation, but I think ESOMAR has now fully recovered and is on the ball again.
This is what I spoke about at the three events:
- The integration of social analytics with retail sales reports and brand survey tracking - together with Nielsen
- The importance of image processing for theme detection in social listening and analytics
- Social media listening case studies in LATAM
In all three of my presentations artificial intelligence and machine learning occupied centre stage. The one thing that makes me even more pleased than getting a speaker slot in those events is that DigitalMR was not the only agency that had something to say about the use of AI in discovering customer insights.
The hardest thing when innovation is introduced in an industry is to educate clients to use it effectively. The inertia that we had to endure during the past few years was relentless. Thankfully, the feeling I have after participating in these events is that there is change in the air. The fact that more people talk about AI now, means that we will finally get some traction in adopting these new methods in mainstream market research. After four years of hard work in doing R&D and running pilots with early adopters, we may be nearing the phase whereby the early majority will start kicking in.
Those of us in this field can use all the help we can get to establish machine learning as an acceptable way of analysing big data and integrating it with surveys and behavioural data. Having said that, we have to be really careful as an industry and set some boundaries that will not allow aspiring tech companies to destroy the image and reduce the value of what market research offers as an industry (to its clients).
The same way ESOMAR once created the 28 questions that a client has to ask a vendor before they engage in online research (using access panels) we now need to define the parameters of acceptable market research standards around social listening, the use of natural language processing (NLP), and by extension artificial intelligence. Here is a list of 20 questions that DigitalMR proposes ESOMAR should use as a starting point to create those standards, in a way that is simple and hopefully easy to understand:
- Are the sentiment classifying algorithms based on Natural Language Processing (NLP) linguistic or statistical methods or both?
- What is the average sentiment accuracy achievable with the method used?
- How is sentiment accuracy defined?
- How exactly is the algorithm trained (if one is used) and how long does it take to get to the maximum achievable accuracy?
- In what languages can the vendor analyse for sentiment and topics in an automated way?
- How long does it take to introduce a new language?
- How is noise (irrelevant posts) due to homonyms removed from the data set to be reported on?
- Are search terms used or is it an open ended inductive approach?
- Are posts weighted according to author influence? If yes, how?
- Is the profiling of people who post (by demographics and other variables) available?
- How are the harvesting sites selected?
- How are comments gauged and classified for sarcasm?
- Is the pricing based on the number of search terms researched?
- How is the reporting done; what are the deliverables?
- If Natural Language Processing is used, are adjectives classified as positive or negative in a library (rule based approach to define sentiment)?
- Is the vendor a technology company or a specialized market research agency?
- Can specific emotions be detected and analysed? If yes, which ones?
- Does the vendor use topic taxonomies to identify discussion drivers? What is their semantic accuracy?
- Is image processing for brand logo and more importantly theme detection available?
- Can the vendor integrate the harvested data flow with your brand tracking surveys, Nielsen retail reports, or other in-house data sources?