Over the past few years, the market research industry has experienced massive growth, but the market research industry has been decreasing over the same time. Global sales dropped to $45.8 billion in 2017 from a high of $46.1 billion in 2014. But AI is poised to revitalize the working of market research, and it will change all about how market research is being performed and used around the world.
To understand the potential for AI 's transformational impact on market research, the factors that led to the sector's decline in its conventional form need to be appreciated. One reason is that the area of client observations has been market research. However, the rise of big data as a business intelligence tool has dramatically eroded that position within an organization.
Labour-intensive, costly and time-consuming, conventional market research projects require advanced knowledge about what information is necessary to formulate a business strategy. That's why market research has been used almost exclusively to make high-stakes decisions on big-ticket products such as branding, product design or pricing — it's too expensive and time consuming to use for more granular choices.
Another downside to conventional market research is that the produced data and insights are tightly held by a small group and are not leveraged around the business. Usually, the findings are used once to answer a business question, and then consigned to the scrapheap. And while intuition is the commodity that drives conventional market research, consumers are left alone to determine next steps.
How AI is Reforging Market Research and Market Insights
AI can practically alter all the dynamics that characterize mainstream market research, and it is already doing so. Apart from the small implementation of automation (think SurveyMonkey), market research was largely resistant to the digital revolution over the past decade or two, prior to recent AI-driven developments. AI would radically change market analysis, addressing prices, scheduling, delivery and implementation.
The use of algorithms and machine learning decreases project cycles from weeks and months to hours and days by making market analysis quicker and cheaper. The move alone makes it possible to use market research outside big decisions; it is possible to apply AI-driven market research to everyday decisions with fast results.
Another fundamental shift is the opportunity to use real-time data from a range of sources in market research initiatives. Now, it's possible to incorporate up-to-the-minute data from sources such as sales, emails, social media, behavioural knowledge, and passive data, which can turn market research from a feature of backward-looking analytics into a discipline of the future.
AI and Market Research in Action!
In ways that illustrate how the methodology improves access to data and drives tactical decision-making, companies are now using AI-driven market analysis. Global telecom firms, for example, would spend billions in setting up 5G capabilities in the years ahead. They would have been depending in the past on market analysis to tell them where to update networks.
Telecom companies will exploit the huge amount of data they have in-house with AI-driven market analysis and add other real-time information to determine where network investments can provide the greatest return on their investment. That's a big leap forward, and Fortune 500 telecommunications companies are now using AI and Machine Learning to ensure that strategic gain.
Additional use case for AI-driven market research is supply chain optimization. AI has changed the supply chain field, but its use in market analysis allows for genuinely future-oriented outcomes such as predictive supply chains that anticipate needs and optimize the supply chain until they materialize to meet demand.
What lies in the future?
With these innovations in mind, it is a good time for companies that have cut back on their market research activities (and those priced out of the conventional market) to consider how market research powered by AI can deliver scalable effects. As AI gets smarter over time, businesses that integrate AI-driven research into their activities can first gain a sustainable advantage.
Market research powered by AI is accessible because it doesn't need an expert team to design a study and analyse findings. Users can go to a portal and simply ask a business question, and the capabilities of Natural Language Processing (NLP) allow the AI solution to access the correct data and return actionable language suggestions that can be interpreted by everyone, ensuring that more individuals can use market analysis around the enterprise.
Natural language processing (NLP) will boost processes such as landscaping technology, competition analysis, and weak signal detection.
Leaders in innovation technology are finding ways to efficiently use artificial intelligence (AI) to generate value and optimize data for optimum effects. Lux takes natural language processing (NLP) and subject modelling of the AI tools of choice into account. These methods have the ability to stimulate progress at the front end in many sectors but remain underused. According to the latest whitepaper from Lux Research, "Improving the Front End of Innovation with Artificial Intelligence and Machine Learning," NLP will enhance processes like landscaping technology, competitive analysis and poor signal detection. NLP allows for quick processing of massive text volumes, which is where much of the innovation-driving data resides.
"When effectively used, machine learning can easily mine data to generate actionable insights, dramatically decreasing the time it takes to conduct a detailed study. A study that would have taken weeks before can now be reduced to days, "said Kevin See, Ph.D., VP of Lux Research at Digital Goods.
The pace conferred by NLP is enabled by the comprehensiveness of the topic modelling, which extracts essential concepts from text while removing the associated human assumption and bias. An inquiry was historically hampered by either the primary investigator's limited expertise or bias, both of which are mitigated when using machine learning. Due to an error of human judgment, a beneficial technology or concept is less likely to be missed,” clarified See.
There are many important applications which use machine learning to leverage innovation speed and comprehensiveness. Landscaping is used to construct a taxonomy that describes patterns within a single theme for key areas of innovation. Similarity of concept may take one piece of content and find other related posts, patents or news to accelerate the process of innovation. Topic modelling may also be used for strategic portfolio analysis when applied to a company instead of a technology, or when applied to large data sets such as news or Twitter for poor signal detection.
By widening access and removing research skills requirements, AI would democratize market research. It will make market analysis more widely applicable, expanding use cases to inform tactical choices beyond strategic decisions. AI will change all about market research for all these reasons and more, and businesses that get in on the ground floor will profit the most.