The Rise of AI in Market Research

Who wouldn’t like speed and efficiency in market research? Who in business doesn’t want smooth and faster workflows?

Advances in artificial intelligence and machine learning are unveiling a new age of automation in market research that is bridging the gaps and solving business challenges swiftly.

Businesses are able to drive more customers, improve customer experience, employee experience, and product performance, and make profitable decisions.

Integration with CRM helps to simplify the job

Measuring brand performance has become easier as brands are able to take instant action while an AI-enabled survey platform works in sync with CRM. This unique workflow helps organisations to take full advantage of machine learning in market research as closing the loop and follow-ups are becoming real-time and time efficient.

Imagine that, right after capturing the feedback of a customer, the algorithms automatically start to process the data and generate insights which are sent to the customer experience team for timely actions. Undoubtedly, it will become easier for a business to act on negative feedback and make data-driven decisions. Nevertheless, the CRM integration capability plays the role of game changer in customer journey mapping.

Feedback analysis has come of age with real-time reporting

Gone are the days when organisations used to spend a lot of time, effort, and money on conducting traditional market research to get feedback about their products or services. With automation, there are zero delays in getting feedback, and access to various insights helps businesses take instant action.

AI-enabled survey platforms help businesses to reduce time and generate real-time insights and actions as there is zero wait time, unlike the traditional method of doing market research, which usually takes one month to generate insights.

SurveySensum Dashboard

Introducing the next level of reporting for organisations to shape their customers’ journeys in order to drive profits and make informed decisions.

Enabling data-driven decisions through text and sentiment analysis

Data is becoming an insightful resource with its analysis of text and sentiment about consumer preference. Now, with an AI-enabled platform, businesses can capture real-time feedback and convert data into meaningful insights by using algorithms that can further help to boost the brand’s performance in a specific industry.

How does this work to get answers that matter?

Whenever a customer takes the survey and answers an open-ended question, the natural language processing (NLP) algorithms automatically analyse the text and sentiment of the answer provided. Such deep learning gives businesses an insightful view of customer preferences. Furthermore, getting an advanced-level view of market research by using visuals and algorithms will be a boon for businesses.

 

Storytelling to interpret data into goals

The biggest turning point in market research is automation that helps organisations to gain more customers and reduce customer churn rate. This is possible due to artificial intelligence (AI) and machine learning (ML), as businesses are able to put across collected data in lively storytelling. Through AI-enabled survey platforms, graphs or charts are instantly read and AI algorithms generate and deliver meaningful insights in real time.

Natural language generation (NLG) techniques in storytelling – a breakthrough!

Currently, most businesses depend upon the skills of business consultants to create insightful and action-oriented storytelling from collected data. However, with an AI-enabled platform, this becomes less time-consuming and more efficient. Action items are defined by algorithms to create stories in the form of corrective measures or alerts.

The survey platform uses Reiter and Dale’s methodology of NLG, which largely includes content determination, document structuring, lexicalisation, and linguistic realisation.

  • Content determination: Defines which information to mention in the text. The platform combines a schema template with explicit use case-oriented reasoning to identify meaningful insight.
  • Document structuring: Defines the correct sequence and grouping of sentences in a document.
  • Lexicalisation: Selects appropriate nouns, verbs, adjectives, and adverbs for the generated text.
  • Linguistic realisation: Creates the final presentation of collected data.

 

Conclusion

The rise of AI in customer feedback collection and analysis makes the market research task painless and seamless for enterprises. Investing in machine learning will be one of the best decisions of 2019 for businesses that demand consistency in customer experience and want to interpret data without the help of additional consultants.

AI has a lot in store, including speed and accuracy, saving on cost and effort. It simply brings the futuristic outlook to the business by distributing the right questions, collecting responses, and interpreting data through storytelling.