In Insights Industry, Artificial Intelligence has consistently remained the most talked about subject. Despite process streamlining by machine learning, the future of market research is reliant on the perseverance of hard skills.
Over the last decade the market research scene hasn't changed much – or perhaps in the last two or three decades. While we use technology to make things quicker and more effective, we still rely more on methods and resources that devour energy, money and hours for employees. Sure, there are more data and it's getting better than ever before. Yet there is still a difference between the production and compilation. Sure, new technology technologies are emerging (like the BI dashboards). But old school stalwarts such as slide decks and presentations still get the most time on the screen.
And our latest market research strategies have yet another problem: shifting skill sets. Things happen when experienced MR employees leave their jobs-they are followed by a large amount of professional expertise. It is hard to replace those knowledge and skills.
It is time we modified the way we perform market research. In the MR context, most people believe that AI will be the agent of this transition. Which poses the question Is AI going to end market research as we know it? And would that lead us to a better, newer way?
Can Market Research Be Scalable, Reusable, and Adaptable?
This is true that some businesses use AI in market research; with impressive results, AI subdisciplines such as machine learning and natural language processing were implemented. But in the context of market research we have not seen AI becoming fully adopted. Could be used, for example, to AI:
· Transfer work into other projects from one group.
· Build "smart" surveys that adapt the questions proactively to the responses of each respondents.
· Give insights within seconds of collecting the data.
· Combine research data with other sources (IoT, sales, behavioural, etc.) and use it to guide decision-making strategies.
· Enable business users to ask questions (in normal, spoken language) and obtain answers backed up by the data.
We are not far from these scenarios being a regular reality; several are already in use as we will address later in this article. We are gradually moving towards reusable, repeatable and ready to deploy market research methodologies around the enterprise.
And we should accept that AI has the demonstrated capacity for transforming market research. What's industry analysts looking for in the future? Will AI be eating all the work at MR? No, but it will cause a shift in the way jobs are done, particularly when we see algorithms taking over more routine manual tasks from MR.
Algorithms: The New MR Skill
Today's algorithms can do some of the things that members of the market research team do, including develop surveys and questionnaires and evaluate the incoming information. More importantly, we can now bring data from the market context into these algorithms, which provides more practical and accurate performance. Over time, we would expect to see algorithms currently in the process of creation to surpass what human researchers can do in those fields.
This will improve market research, making it less expensive, less time-consuming and more widespread. Companies will be able to build or even rent algorithms to perform specific tasks; as these algorithms are self-learning and flexible, they can be applied fairly easily to multiple different tasks.
So how would the human researcher put that? Instead of concentrating on repetitive tasks, researchers will be able to use their ingenuity and imagination, focusing on discovering and telling stories hidden in the data, adding meaning to the core business problems addressed by AI, and using what AI doesn't have — the human brain — to find insights that are greater than those generated by AI.
In short, we see the future in terms of a relationship between human and AI with each playing to their own strengths. This will allow market research teams to generate greater value across the enterprise. And smaller businesses that don't need a full-time MR team or can't afford it can have access to superior MR capabilities through leased algorithms. In reality we see this relationship in its early stages already.
Uncovering Insights in a Mountain of MR
Extracting information from a mountain of disorganized data was the main obstacle for one Middle Eastern dairy conglomerate. They wanted to create a way to make research results easily accessible in addition to managing their vast amount of information – essentially building their own internal 'Google' page for research studies. Ideally users should ask a question and get an immediate, data-backed response.
Years of academic knowledge and experience with deep learning algorithms went into the solution, which turned both structured and unstructured data into specific consumable nuggets. Second, years of market research data have been collected, catalogued, and organized with great care. Now an AI application organizes and analyses all the data from the business and makes it deliverable to non-technical users.
Years of technical knowledge and experience with deep learning algorithms went into the solution, which turned structured and unstructured data into specific consumable nuggets. Second, years of data from market research have been collected, catalogued, and organized with care. An AI platform now organizes and analyses all data about the organization and makes it available to non-technical users.
AI and MR: A Future Alliance That Is Now
The future of market research should concentrate on making it quicker, more effective, and more scalable. It will be a collaboration between AI processing and human cognition, with algorithms performing routine tasks, and researchers applying their expertise to selecting and integrating the right data and having detailed feedback from broad insights.
AI will be propelling market research into an age of transition and growth. Being able to obtain data-backed results faster and more effectively than ever before would extend the depth of in-organization of MR and hit its inter-organization. Now is the time to plan for and begin to adjust to this change; market research teams that do will have a major advantage over those teams who are merely trying to keep up to the speed of technology.