Artificial Intelligence (AI) is at the forefront of discussions, especially concerning its integration into research methodologies for deeper insights, agile innovation, and streamlined product development.
We asked our Advanced Analytics/AI team to explore the current and future tangible impacts of AI.
How you can use AI in market research
Market researchers today can use AI for many different use cases to help them do their job faster and more efficiently. Some uses include:
- Generating and brainstorming ideas
- Assisting with survey-writing
- Collecting and analyzing data
- Automating repetitive tasks
- Making qualitative research quantifiable
But what do these use cases look like in reality? We turned to our Advanced Analytics team (a.k.a. AI experts) to share some examples of how they leverage AI in research projects. Read on to find a few AI tools they’ve built for market research !
Generative AI: Redefining Idea Generation
At the core of AI’s capabilities lies Generative AI, where Machine Learning Models create novel information based on user prompts. This technology, familiar through instances like requesting articles from ChatGPT or meticulously editing photos via Midjourney, has found its place in research tools. Our very own platform, Upsiide, leverages Generative AI in our built-in AI Idea Generation tool to brainstorm ideas and concepts alongside your own. Surprisingly, these AI-generated ideas have even outshined human-generated ones in some instances, proving immensely valuable to our clients.
Read more: How to boost your concept testing survey with AI-generated ideas
Visual Idea Generation
There’s immense value in being able to generate visual design models and mock-ups quickly and effectively. This would be a game-changer for early-stage concept testing, aligning with the industry’s push for research agility.
Synthetic Data Creation
AI’s capabilities extend beyond idea generation into crafting synthetic datasets mirroring real-life characteristics without infringing upon personal data. While it’s still in its early stages, there is clear excitement among analytics teams for leveraging synthetic data to refine models and methodologies. At Dig, we’ve utilized this technology to showcase example results in proposals. The future foresees synthetic datasets as reliable predictors of product success, accelerating market insights and predictions.
Our Advanced Analytics members and AI pros, Joel Armstrong and Joel Anderson, share how you can use synthetic data and AI in market research in a lively Q&A session:
Open Ended Responses and Text Analysis
AI’s role in supporting data analysis is particularly interesting. Our Advanced Analytics team at Dig is continuously exploring AI applications for analyzing both structured and unstructured data. Dig’s AI tool for market research, Open Text Analytics Platform (OTAP), has revolutionized sentiment analysis, theme identification, and decoding of consumer behavior through unstructured data analysis.
We wanted to test our OTAP tool, so we asked 300 American and Canadian sports fans to share their favourite sporting memories. AI then analyzed those verbatim responses and was able to identify three key themes, which aligned pretty closely with the actual themes sports companies are using right now in advertising.
Meta-Analyses and Advanced Analytics
The use of AI for conducting meta-analyses in the insights industry is one of the most exciting applications yet, uncovering commonalities among results, spotting trends, and enriching insights. There is unlimited potential with this use of AI, and it opens doors to novel understandings of non-linear influences, barriers to purchase, and the potential for accurate predictions of consumer behavior.
It’s an oft-repeated sentiment: AI won’t replace human researchers anytime soon. Instead, it complements their efforts, streamlining numerous laborious research tasks and accelerating the acquisition of actionable insights.
Conversational AI’s Impact
AI’s contribution to conversational AI is pivotal, particularly in the realm of qualitative survey tools that engage respondents based on their answers—essentially, chatbots facilitating qualitative research through technology platforms. This advancement holds a lot of promise, especially for mid-market and growing companies without substantial research budgets, democratizing access to critical insights.
AI-Generated Surveys
The evolving landscape sees companies pushing the boundaries with AI in research, notably in outsourcing research-related tasks. For instance, our in-house engineering team at Dig has pioneered a proof-of-concept where AI drafts surveys within the Upsiide platform. While this technology is a work in progress, its integration with tools like pre-built audience templates promises to alleviate the time invested in survey creation, a boon for researchers aiming to focus on extracting actionable insights from data rather than administrative tasks. The future holds promise for a more streamlined approach to research tasks, bringing actionable insights within closer reach.
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