by Steve Male, VP, Innovation & Strategic Partnerships

In March, I attended The Insight Show in London. This event has grown and grown in recent years, thanks to its partnership with Marketing Week, the U.K.-based website covering all aspects of the industry. From walking around the show and talking to partners, one expression seemed to keep coming up in conversations: ā€œartificial intelligence in marketing research.ā€

Itā€™s a little funny that AIā€”a term I used to associate with science fictionā€”is increasingly being used within the marketing research sector. This got me thinking about its impacts, not only now but also into the future.

So What Is ā€œArtificial Intelligence,ā€ Anyway?

To truly understand AIā€™s effect on marketing research, I think itā€™s essential to be clear exactly what weā€™re talking about when we say ā€œartificial intelligence.ā€ In all honesty, I think many organizations in many sectors boast about deploying AI to sound cool and forward-thinking, but the vast majority don’t use actually use AI at all.

Artificial intelligence is learning undertaken by machines to understand trends and key insights, which can then be adapted for a range of scenarios. To be clear, this is not the same thing as ā€œautomation.ā€ That term is widely used throughout the sector to describe the speeding up of many processesā€”from recruiting a sample to data collection to analysis. However, automation canā€™t learn, and it needs human intervention to change.

Learning is what separates AI from automation.

So How Has Marketing Research Adopted AI?

When it comes to marketing research, AI can come in many forms, but two of the ones in which Iā€™m most interested are social media listening and community engagement.

Social Media Listening

The ability to listen to peopleā€™s thoughts on social media is now widely accessible through organizations such as BrandWatch and Crimson Hexagon. Sometimes referred to as Big Qual, this technique involves using artificial intelligence and applying analytical techniques to big data, which can then be distilled into actionable results and insights to impact the bottom line.

Using AI, social listening devices can now offer insight on sentiments being shared across multiple languages. According to Hootsuite, 326 million people actively use Twitter every month. Think about the incredible combined number of comments these individuals are posting! Applying AI on this sort of large scale reduces the time it takes to analyze responses from days to seconds. (This is particularly the case when thinking about open-ended responses.)

Community Engagement

Using behavioural predictions, AI can understand and analyze the number of times someone has logged in to their community panel, the specific pages they visited, and the time they have spent on the website. This helps build a picture of an engaged individual in comparison to a disengaged person. With more data and predictive capabilities, researchers equipped with AI will be able to offer support or support to someone before that person becomes disengaged. The result is reduced dropout rates.

Behavioural predictions help identify those individuals within a community who are at risk of disengagement. Traditionally, this would take days for a researcher to understand, but the AI greatly speeds up the process. This allows moderators to more efficiently use their resources to decide how to manage a situation, rather than spending time trying to find those at-risk individuals in the first place.

What Does the Future Hold for Artificial Intelligence?

Artificial intelligence isnā€™t perfect yet, but progress is continually being made. Although it is very hard to predict where AI could be taken in the marketing research sector, certain areas have been developing at a rapid pace. One example can be found with chatbots, which are computer programs designed to simulate conversation with human users.

At the moment, most chatbots are driven by pre-programmed questions, presented via a user interface on an organizationā€™s website. Increasingly, though, advanced chatbots are becoming able to interpret answers from respondents in a way that allows followup questions to be tailored to that userā€™s individual needs. This is where a chatbot and the online conversation format evolves to the next stageā€”virtual moderators for websites.

How Will the Marketing Research Sector Be Affected?

Iā€™ve heard concerns that automation and AI could have a substantial impact on jobs in the marketing research sector, but I honestly don’t see this to be the case. As I mentioned, artificial intelligence is helping researchers by taking away mundane tasks that otherwise take days. As a result, the human researchers can better analyze the data to make real outcomes with their work.

With the continued evolution of AI, I see the role of marketing researchers changing. There will be more emphasis on their ability to do a deep dive into the data in order to truly understand what an individual or respondent is saying and how the results can have an impact on a business. For marketing research and insight professionals to make the most of a technology this powerful (and still not fully understood), all avenues and applications must be fully explored.

FAQs

How do companies address ethical considerations and privacy concerns when using AI in marketing research?

Companies using AI in marketing research, especially for tasks like social media listening and community engagement, are increasingly mindful of the ethical considerations and privacy concerns this entails. To navigate these challenges, they adhere to strict data protection laws, such as GDPR, which mandates clear guidelines on data privacy and user consent. Organizations strive for transparency in their data collection practices, ensuring that individuals are aware of and consent to how their data is used. Anonymizing data to protect identities and implementing safeguards against unauthorized access are also key practices. These measures collectively aim to respect and protect individual privacy while harnessing AI’s capabilities for insightful research.

How are accuracy and bias in AI algorithms managed in marketing research?

The potential for inaccuracies and biases within AI algorithms is a significant concern in marketing research. Companies are actively working to mitigate these issues by diversifying the data sets AI algorithms are trained on, thus reflecting a broader population spectrum. Specialized algorithms designed to identify and correct biases are being employed, and human oversight plays a crucial role in reviewing and validating AI-generated insights. These efforts ensure that the insights provided by AI are both accurate and free of systemic biases, thereby making them reliable tools for marketing research.

In what ways is AI affecting traditional research methods in marketing research?

AI is not replacing traditional research methods like surveys, focus groups, and interviews but is enhancing them. Traditional methods continue to be valuable for gaining deep qualitative insights, while AI augments these approaches by analyzing large data sets quickly and identifying patterns or themes that may not be immediately apparent. For example, AI can process open-ended survey responses on a large scale or swiftly identify key points in focus group discussions, thus complementing the depth of traditional methods with its breadth and speed. This synergy between AI and traditional research methods enables marketers to achieve a more nuanced understanding of their target audiences, combining the best of both worlds for richer, more actionable insights.

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