Although some companies are dipping their proverbial toes in the AI waters, the majority have yet to incorporate it into market research. But we expect this to change, and quickly. Look at what we already have the technology to accomplish:

  • near-instant data collection, processing and analysis;
  • smart blending of multiple data sources, internal and external;
  • self-adapting surveys that change, add or drop questions based on user responses; and
  • automatic delivery of data-backed insights to business users and decision-makers.

What does the advent of AI mean for them? Will it make their jobs easier, or will it make their jobs disappear?

To understand the answer, let’s consider how AI is currently impacting marketing research. We’ll also look at some tech trends that client-side market researchers should monitor, and finally we’ll learn what MR professionals need to do to successfully utilize AI at work.

AI is already changing market research 

AI might be a relative newcomer to market research, but it’s already proven its potential. In the two real-world case studies below, notice how AI helped companies process, organize, analyze and present their data.

From data disarray to instant availability

One Middle Eastern dairy company had invested heavily in market research and had amassed a trove of information that was spread throughout the organization. It would sometimes take employees weeks to find a desired piece of information. To lighten the load on the research team and make insights readily available throughout the company, leadership decided to turn to an AI-based centralized data solution.

This solution used AI to process, analyze and sort data. A natural language processing chatbot was used as the interface to the system; this allowed business users to simply ask a question in their normal spoken language, get an answer and be given access to the relevant passage within a report, study or other research document. This system worked with both structured and unstructured data.

The result has been a much easier and faster dissemination of market research within the company. And market researchers can spend less time on unskilled tasks (locating and summarizing reports) and more time on their core work.

10 sources, 500,000+ locations, one solution

A Fortune 500 beverage leader had a host of data to manage: 35 billion data points coming in from 10 sources, representing information from over 500,000 locations. They needed to combine this internal data with external competitor and customer data (including monthly reports on occasions, consumer habits, trade area characteristics and local competition) and then use this to get a full picture of potential sales gaps down to the outlet level.

AI was used to process this data and to power a recommendation engine that allowed business users to see optimal assortments and SKU opportunities at various levels. Among other things, it also allowed the company to find the right mix between their well-known products and smaller brands.

This tool has become so successful that the company uses it for daily work (assisting the sales team) and for yearly planning meetings. So far, there has been an estimated 3% growth in incremental revenue and 4% consumption value growth across categories.

In each of these cases, AI was used to automate repetitive tasks like data processing and analysis. Research results were made directly available to the appropriate personnel. This sounds a lot like market research.

How will AI change client-side market research? 

It’s hard to generalize the role of a client-side marketing researcher; depending on the size of their company or team, a market researcher may own a project from start to finish, specialize in just one area or serve as the go-to person for many research needs. As we’ve just seen, though, AI has the capability to handle some of research’s core tasks. How will client-side marketing researchers be impacted by the industry’s adoption of AI?

Currently, algorithms are being developed that can design surveys and questionnaires, often in just a short time. With some relatively simple alterations, one algorithm can be used on several different businesses. Researchers working alone or in small teams may need to outsource some items to outside market research organizations; with AI, some of these items can be handled in-house, via algorithm, for much less.

AI also represents a significant time savings, streamlining research and freeing up researchers to focus on finding additional insights and adding value to the facts that algorithms uncover. AI’s “artificial brain” will always need human guidance and understanding; experienced client-side market researchers can fill the gaps with their industry knowledge and business acumen.

So, AI has already begun transforming market research, and it will certainly affect client-side research as well. It’s also reasonable to assume that, in one form or another, everyone will be working with AI. How can researchers start preparing now for a tech-fueled future?

Preparing now for AI

There are two major ways that client-side market researchers can prepare for AI: keeping abreast of current trends and planning ahead on how they’ll achieve a productive human-AI partnership.

1. Stay aware of the trends. AI may not have been integrated into your workflow yet, but that doesn’t mean you can afford to ignore it. Keep an eye out for these trends and you’ll be better equipped to work with whatever technological changes come your way.

  • The democratization of AI: Organizations that develop AI tools for business users – in other words, the popular “democratization of AI” – will gain traction in the near future. Such tools, which usually have a sizable machine learning component, allow companies to run tests and conduct research without a data science team. This will be a major way for AI to impact client-side market research.
  • Chatbots: As previously shown, chatbots are becoming popular interface tools; they’re fast, easy to use and integrate well in other apps. In time, we could see AI-powered chatbots working with researchers to find and deliver information. This might not sound as impressive as some other use cases, but programming a chatbot to work well in an unpredictable setting is actually quite a feat.
  • In-the-moment market research: We’re already seeing a lot of buzz around micro-surveys, which pop up while a customer is in-task and asks a few short questions about their experience with that service, system or platform. This type of immersive, in-the-moment research will become more popular; it’s an excellent way to capture consumer attitudes and behavior while simultaneously understanding various parts of their customer journey. Similar techniques will be used to contextualize this data and correlate it with various stimuli. This approach requires less time and effort for customers and provides researchers with authentic insights.
  • The integration of social media listening: Specifically, social media will be integrated with surveys and other data sources to provide more vibrant and rounded information. A listen-probe-listen-probe approach with social listening platforms and online communities will become the norm.
  • IOT-enriched insights: The rising number of connected devices mean a growing stream of Internet of Things (IOT) data. These shed light on customers’ usage patterns and behaviors in much greater detail than previously available. Researchers can help turn this data into effective customer experience strategies and smarter product and service suggestions that precisely meet customers’ unique needs.

2. Plan how to successfully partner with AI. It’s not enough to know the data; marketing researchers have to be able to demonstrate how various factors will impact actual business. For the client-side researcher who is getting ready to embrace AI, that means bringing the human element into the equation. It will be incumbent upon them to deliver actionable real-world outcomes, not just summarized facts. Here are three ways to get started now.

  1. Practice customer empathy. This is one of those areas that remains the purview of the human mind. Don’t just work to understand customers; dig into their needs and make their voice an integral part of your research. Whatever your business, customer satisfaction will always be key. Make customer empathy a key priority.
  2. Make the meaning clear. Data visualization might seem like a strictly tech thing, but at its heart it’s really a form of storytelling – an essentially human activity. It’s up to your human brain to make the point of your research clear, whether that be in a written case study or through a visual medium.
  3. Be a smart AI shopper. If you’re going to be outsourcing your AI to a third party, remember this: you get what you pay for. Instead of focusing on fast, cheap results, learn to distinguish quality. Be clear on your business objectives and know what research methodologies will best suit your needs. Client-side researchers should work closely with stakeholders to ensure that whatever tool, provider or partner is chosen will match their requirements.

An AI-enhanced future 

Will AI eat all the market research jobs? No, but it will continue to optimize the marketing research process. This is an industry that’s experiencing its own evolution, and research teams willing to adapt and adjust to AI will be in greater demand.