AI Beyond Data Collection
AI and its machine learning algorithms can be seen as a versatile new tool for market research—it’s a game-changer in terms of processing vast amounts of data, for example. It also has robust capabilities such as ways it can study and project consumer trends and behaviors. Of course, these newer AI methods should still be used in conjunction with traditional research techniques and with the support of human interaction and supervision.
But the role of AI in boosting market research’s capabilities is significant. LeewayHertz, in its blog, “AI in market research: Use cases, applications, architecture, benefits, implementations and solutions,” notes that “AI in market research involves integrating Machine Learning (ML) algorithms into traditional methods, such as interviews, discussions, and surveys, to enhance the research process. These algorithms enable real-time data collection and analysis, predicting trends and extracting valuable patterns. This process results in high-quality, up-to-date insights that transparently capture even minor market changes.”
So just how is AI aligned with market research functions? LeewayHertz points out several ways to use AI in market research:
- Open-ended text analysis: AI scrutinizes open-ended survey responses from various communication channels, from traditional emails to contemporary social media comments. It deepens into this textual data to extract the precise thoughts and sentiments concealed within. Natural Language Processing (NLP) and deep-learning-driven sentiment analysis modules are needed to avoid cognitive pitfalls in the research.
- Conversational insight collection: AI in market research empowers businesses to collect precise, relevant information with efficiency and accuracy. Custom AI models monitor consumer interactions, identifying recurring patterns. AI chatbots designed for conversational interactions can be trained to offer users more advanced and enhanced experiences. These AI-powered chatbots can learn and adjust based on ongoing conversations, resulting in more comprehensive consumer market insights.
- Auto report generation: AI-driven report generation empowers businesses to craft concise reports tailored to their needs, featuring customizable metrics. Each department within an enterprise can create reports to suit their requirements. This agility ensures that decision-makers receive the most relevant information, streamlining the decision-making process.
- Customer success automation: Through predictive modeling, AI handles a spectrum of tasks seamlessly, from scheduling follow-ups to crafting ‘stay-in-touch’ messages. It goes beyond automation by analyzing message content and frequencies, providing valuable insights and suggesting better-performing metrics. This precision and accuracy significantly elevate customer retention, fostering a loyal customer base.
- Advanced secondary research: Secondary research represents a deeper investigation conducted on the information gathered during the initial round of research. This is where AI in market research proves valuable. It efficiently fulfills these secondary research objectives, helping businesses make informed decisions based on well-structured insights.
- Preparing questionnaires: AI solves this challenge by presenting a predefined questionnaire, meticulously analyzing the responses, and adjusting subsequent questions accordingly. This dynamic process ensures that the results closely align with the core research objectives. AI in market research allows businesses to analyze how closely past responses align with standard answers to the questions posed. Consequently, the following questions are fine-tuned based on this analysis, enhancing response quality in real-time.
These various AI-enhanced research techniques, LeewayHertz suggests, can be employed in diverse tasks such as sentiment analysis, predictive analytics, social media listening, chatbots and virtual assistants, customer journey analysis, demand forecasting, consumer segmentation, image and video analysis, voice and speech analysis, concept testing and product innovation, survey ideation, competitive analysis, automated market basket analysis, AI-powered conjoint analysis, market trend and behavioral analysis, qualitative analysis and Ideal Customer Profile filtration.
Investing in AI
The start of market research with AI begins with data collection. In “Data Science Invests in AI as Development Pace Accelerates,” All Things Insights spoke with data science and analytics practitioners to assess their thoughts on AI. With the shifting landscape of the analytics and data science world, much of it impacted by developments in AI, it has become increasingly important to gauge the perspectives of the community. These 10 data science thought leaders weigh in on the changes taking place associated with AI, and they share their perspectives on a range of topics from what’s on their desk this year, to what needs improvement in the field.
Looking forward to TMRE 2024? The conference, which will be held October 8 to 10, will feature the session, “Unboxing GenAI: Crafting Market Research Best Practices in Year One,” presented by Max Wartel, Director of Consumer Insights and UX Research at Fandom. Register for TMRE 2024 here.
Take a Strategic Approach to Implementing AI in Market Research
Implementing AI in market research requires a strategic approach. While it may hold many advantages over traditional, manual market research, one must still weigh many options. From choosing the right tools and technologies, to data quality concerns, to integrating AI into workflows, there are many steps to consider, not to mention such challenges as hallucinations, privacy and security. We asked Gemini to outline some key approaches:
1. Identify Clear Objectives
- Define specific goals: Clearly outline what you want to achieve with AI. Are you looking to automate tasks, improve data analysis, or gain predictive insights?
- Prioritize use cases: Identify the areas of market research where AI can add the most value, such as data cleaning, sentiment analysis, or predictive modeling.
2. Choose the Right Tools and Technologies
- Evaluate AI platforms: Research and select AI tools and platforms that align with your research needs and budget. Consider factors like scalability, ease of use, and integration capabilities.
- Build or Buy: Decide whether to develop custom AI models in-house or leverage existing AI solutions. Consider the expertise and resources available within your organization.
3. Data Quality and Preparation
- Clean and prepare data: Ensure your data is accurate, consistent, and relevant for AI analysis.
- Data enrichment: Enhance data with additional information from external sources to improve insights.
4. Develop AI Models and Algorithms
- Collaborate with data scientists: Work closely with data scientists to develop AI models tailored to your research questions.
- Iterative process: Continuously refine and improve models based on performance and feedback.
5. Integrate AI into Workflows
- Automate routine tasks: Use AI to automate repetitive tasks like data cleaning, coding, and report generation.
- Enhance human capabilities: Leverage AI to augment human expertise, allowing researchers to focus on higher-level analysis and interpretation.
6. Build a Strong AI Team
- Hire AI talent: Bring in data scientists, machine learning engineers, and AI experts to support your initiatives.
- Upskill existing staff: Provide training and development opportunities for your market research team to understand and utilize AI effectively.
7. Ethical Considerations
- Data privacy: Ensure compliance with data privacy regulations and protect sensitive information.
- Bias mitigation: Address potential biases in AI algorithms to ensure fair and unbiased results.
- Transparency: Communicate AI usage and limitations to stakeholders.
8. Continuous Evaluation and Improvement
- Monitor performance: Track the performance of AI models and identify areas for improvement.
- Stay updated: Keep up with the latest advancements in AI and adjust your approach accordingly.
Navigating the New Market Research Path
By following these steps and maintaining a focus on human-AI collaboration, you can effectively implement AI in your market research and gain a competitive advantage. The future of AI in market research seems bright, and its capabilities seem limitless, but we have yet to measure the overall impact it might have on the insights community.
From virtual market research, synthetic data and forecasting, to chatbots and virtual moderators, to ways to conduct secondary research—these are just some of the aspects that might develop in the future, all with the caveat that ultimately human interaction, supervision and creativity will help balance the automated scales a bit.
As LeewayHertz observes, “In this rapidly changing landscape, AI in market research is not just an advantage; it is indispensable for those who seek to thrive and remain competitive. As we move forward, the fusion of AI and market research will continue to shape the business world, providing a clearer path through the intricate maze of the market and enabling companies to navigate it with confidence and precision.”
Video courtesy of Market Research Institute International
