In today’s data-driven landscape, insight and business professionals are often looking for more efficient ways to analyse information and generate actionable insights. Artificial intelligence is here, heralded as a pivotal tool in this process, reshaping the traditional approaches to market research. By automating data collection and analysis, AI enables organisations to make informed decisions more efficiently than ever before.
AI introduces a range of new capabilities to market research, including predictive analytics that forecast potential consumer behaviour, data interpretation uncovering new trends and identifying those that might become staples in the industry, and sentiment analysis that gauges popular public opinions.
 |
Tweet This |
|---|---|
| AI is often heralded as a crucial tool to increase efficiency in market research - but are they right? In what ways has AI infiltrated the market research process? |
But it also helps us scale research capabilities way beyond current manned capacities, which was the inciting desire as stakeholders require more insight at the speed of business. As AI continues to evolve, it continues to have profound implications for understanding markets and consumers and paves the way for more daily strategic decision-making.
Artificial Intelligence in Market Research
The Promise and Pitfalls of Artificial Intelligence
The amount and variety of artificial intelligence tools in market research have undoubtedly skyrocketed in the past few years, with new generative artificial intelligence programmes promising new realms of insight generation at the touch of a button.
FlexMR has always embraced AI-based innovation, creating new tools including:
- TextMR - using generative AI to analyse qualitative data to create rich, detailed analyses of qualitative data complete with themes, a bespoke code frame and sentiment breakdowns in just a few minutes.
- PromptAI - a new question type that allows researchers to follow up with intelligent probing of verbatim responses. Prompts are dynamically generated based on participant answers, but guided by the constraints that researchers specify.
- Conversational Surveys - a survey user interface designed to feel like a natural conversation that listens and reacts to responses in real-time, equipped with a variety of classic survey answer options and uses PromptAI to adapt and probe in a more personalised manner.
While AI integration into market research processes offers significant benefits, it also presents challenges that organisations must be wary of - as artificial intelligence has increased our capacity to generate insights at incredible scale, there are elements we need to supervise to maintain the accuracy and efficiency of these tools as they're used in anger.
For example, with the increase in popularity of synthetic data, using artificial intelligence programmes to fill in data where gaps are based on other responses can be incredibly useful too - but we need to be mindful that it doesn't present us with fabricated false data that is then used specifically for key decisions. Another example would be artificial intelligence programmes designed to moderate focus groups (whether synchronous or asynchronous), supervising the AI as it moderates a focus group, even if you're moderating another, will mean researchers generate twice the amount of data gathered in the same space, with only a little more effort to ensure the AI isn't veering off track and is generating useful data.
Supervising artificial intelligence programmes to ensure it is creating correct insights from the data provided will always be a human task, but this will still leave us with plenty of time to curate these insights in engaging ways and encourage insights activation in stakeholder organisations.
|
Tweet This |
|---|---|
| While AI has indeed increased our capacity to scale insight generation and activation, it isn't without its limitations. |
AI in the Pursuit of Customer Salience
With all the benefits of artificial intelligence, giving us back the time to dedicate to other pursuits is arguably its most attractive quality. One way I would advocate stakeholders and insight experts use this time is in the pursuit of Customer Salience.
Just getting insights in front of stakeholders has become harder than ever, but taking that extra step and getting stakeholders to act on those insights is a challenge we continue to battle against. The extra time afforded to us through artificial intelligence programmes can be used to better understand stakeholders, and even be used directly to better communicate insights to stakeholders.
For example, automating the roll out of the latest insights either through live dashboard updates or regular newsletter and reporting updates will be incredibly useful to researchers. Or using AI tools to design better insight communications based on stakeholder feedback for quicker implementation will also be useful to help stakeholders engage more with insights in both the short and long term. Whichever ways artificial intelligence is used within the market research process, it's undoubtedly here to stay, so understanding where in your process you would benefit from AI tools is crucial to optimising your processes and making the most out of the data and insights gathered.
