With scores of data generated every second, marketers across industries are hard-pressed for ways to turn information into revenue-driving insights. By 2020, studies suggest that an individual will create nearly 1.7 MB of data every second. For researchers, however, collating, structuring, and interpreting this data to distill timely actionable insights will only get more time-consuming and capital draining.
Thankfully, artificial intelligence is emerging as a game-changer in this space. By combing through vast data sets at jet speed, AI can not only deliver quality insights but, through constant learning, it can make sound decisions as well. Text analytics, machine learning and other avatars of AI are the enablers making this happen. Eager to know how they are augmenting the market research process? Here’s a deep-dive into its application areas across industries:
1. Improving the efficiency of existent workflows
Traditionally, a market research activity across social media platforms involved a sentiment and buzz analysis. These offer cursory insights into reigning trends and behavioral patterns among your target audience. However, machine learning, a form of AI, digs deeper to offer researchers elaborate data categorizations and reveals correlations between complex responses.
Unilever, the consumer goods magnate, employs AI to get the most out of its research data. It replaced its traditional system – one that required considerable human effort to garner the required insights with an AI-powered tool. Armed with this tool the firm can now deliver customized insight reports to each of its decision-makers, basis their functions. By learning from the kinds of queries they feed in, the tool supplies customized intelligence and grows more proactive with use.
2. Real-time insight powered by IoT
By 2025, over 75 billion devices are estimated to be connected by IoT, generating vast amounts of customer data. This will give market researchers the opportunity to conduct in-depth longitudinal studies on customer behavior, instead of having to work with a perfunctory analysis.
Moreover, the combined effect of AI and IoT will make forecasting models more accurate. This will serve to deliver better market research forecasts in the form of predictive analytics. We can already watch this at play across a slew of industries functions.
Insurtechs are constantly finding newer ways to minimize unpredictability with smart devices. For instance, Progressive, an American insurance company, is using telematics to determine the premium of their customers based on their car’s features, driving pattern, traffic, etc. Similarly, Erie Insurance uses drones to determine the premium and indemnification for a property damage claim.
3. Affective computing
Another application of AI-based market research has been found in affective computing. Affective computing is nothing but a form of AI that recognizes the emotional state of a user to provide an appropriate response. TAWNY.ai is an example of affective computing where biometric data are collected with the help of wristbands or cameras in order to classify human emotions and affective states. This allows machines to become empathetic, leading to better user experience. The future will witness an amalgamation of facial, voice and textual analysis with AI, delivering a complete emotional profile of the user to marketers.
Future smart homes will use affective computing to know which settings make their residents more comfortable. Similarly, Netflix will also be able to recommend shows and movies depending on the mood of the user.
4. Recognizing patterns
Understanding customer demands closely can help businesses maintain a competitive edge. Very often, marketers will find valuable insights hidden in customer queries. AI can help researchers recognize patterns based on past customer data to identify emerging customer issues and help firms attune their marketing strategies to the latest demands.
That’s why the future will see smart speakers delivering personalized answers, stemming from a rich amalgam of data, based on past user experiences and its core database network. The data gathered from such interactions will be vital in identifying future patterns in customer expectations.
5. Chatbot surveys
Feedback is vital for the continual improvement in customer services but filling out a lengthy survey can be frustrating for many. On the other hand, conversational chatbots can keep customers engaged while gathering valuable feedback. A chatbot survey used by an AI company CONVRG had 70-80% users answering questions, including open-ended ones, which was three times more than what was achieved by email surveys. Furthermore, chatbot surveys allow you to see the users who left the conversations in the middle, helping researchers identify the points of friction and improve the conversation flow in the future.
AI has emerged as a catalyst for innovation across a number of industries besides market research. In combination with connected technologies, it’s helping organizations be more agile and fuel the next level of growth.