Why AI (Artificial Intelligence) is a revolution for the insights industry

By Elisa Adams, CEO of Sprout Strategy

AI (Artificial Intelligence) is currently reshaping the customer experience, digital and marketing landscape. It is being used in a range of contexts to provide intelligent, convenient and empowered services to customers and consumers. AI’s biggest opportunity, however, may be in terms of market research and customer insights.

AI can make you a lot smarter about what’s happening with your business and customers. Rather than relying purely on the process of conducting and tabulating the results from surveys to perform market research, AI allows up-to-the-minute data from multiple sources like sales, texts, social media, behavioural information, and passive data to be added.

Samuel Irvine Casey Co-Founder and Managing Director for Australian AI company otso.ai, comments, “AI and Machine learning is fundamentally a shift away from deterministic or rules-based programming, to probabilistic programming. What this means is that rather than using rules to find patterns within data, we are using mathematical and statistical algorithms to find patterns”.

AI enabled analytics is able to make assumptions, test them and learn automatically. It can provide predictions at a scale and depth of detail not possible with traditional data analysis. Irvine Casey explains, “You can think about a machine learning model as a mathematical algorithm trained on a particular data set, to perform a specific task. The algorithm’s complexity level is what is referred to when you hear terms like deep learning or shallow learning”.

“Once you’ve got a model that is trained on a particular task, that is what we would call an AI capability. And it’s where you start getting something that resembles artificial intelligence – the training of a machine learning model to replicate a human’s cognitive function”.

Delivering actionable insights at speed and scale

AI has the potential to greatly reduce the time and costs it takes to perform market research projects, while expanding the scale and scope of market research to inform key business and marketing decisions.

As an example, global telecommunication companies are investing billions into building out 5G capabilities. Previously, they would rely on market research to decide where to upgrade networks. These days, with AI-driven market research, telecommunication companies can leverage the vast amount of data they have in-house and other real-time information sources to identify where network upgrades will deliver the best return on their investment.

Because of the speed and level of detail it can deliver, AI market research allows quantitative analysis to be performed in real-time, enhancing the accuracy of forward looking predictions and trends analysis.

Market researchers spend considerable amounts of time writing and generating reports. Research findings can be considered as data point, allowing an algorithm to make certain assumptions about that data to generate reports. As well as saving a substantial amount of time, it allows researchers to focus their efforts on higher-value tasks such as validating and communicating findings to clients and stakeholders.

Analysing unstructured data

Unstructured data includes data that isn’t stored in a structured database format, including emails, open ended survey responses, social media comments, contact centre voice recordings, transcriptions and so on. Unstructured data can reveal your customers’ true opinions and feelings toward your brand, which is challenging to extract from quantitative data or structured data.

Traditional analytic tools and methods such as regression analysis and pivot tables are not capable of processing unstructured data. Very lengthy, very manual techniques with a fair amount of guess work and cross referencing is required to provide any meaningful analysis or insights. Human interaction and intervention is required to query the data, validate patterns, create and then test assumptions. This of course is time consuming and costly and can lead to bias in the results.

Artificial Intelligence (AI) is very good at helping organisations process unstructured data. AI allows market researchers and insights specialists to dive into those millions of words and draw out meaningful insights into what customers are thinking and feeling.

Natural language processing and sentiment analysis

Sentiment analysis is an AI technique used as part of natural language processing, where computational power is used to understand text-based language. The textual data can be gathered from the feedback consumers provide, social media statuses and comments, news articles, emails, SMSs, chat rooms, information on web pages, video channels and so on.

By analysing this data, sentiment analysis can provide insights on the attitude and opinions of the different audiences organisations may be targeting or it can be used to judge the mood of the broader public on a particular topic or issue.  Current AI technology can parse massive amounts of data and provide quality analysis of that data in real-time.

The depth of predictive analysis and insights provided by AI and machine learning offers numerous opportunities, including:

  • The ability to assess customer sentiment across the entire customer base
  • The ability to be proactive in recovering customers you were about to lose
  • More accurate predictions concerning the success of potential marketing and sales campaigns
  • Better and more targeted profiling of customers
  • Greater customer engagement and retention through the delivery of highly personalised services

At Sprout we are committed to building the highest quality research and insights projects for our clients. Innovation is the key to this success. AI and machine learning is at the forefront of innovations impacting the market research and insights industry.