Enhancing Traditional B2B Market Research (Surveys) with AI and Web Data.
Traditional market research, particularly surveys, faces challenges like survey fatigue — where repeated exposure leads to lower-quality, less insightful responses. Surveys can also be costly, time-consuming, and often yield high-level answers that lack depth. In other scenarios, they are used merely to confirm existing assumptions, and asking people directly may not always be the most effective approach.
How The Web Data Can Help
The web is the world’s largest research resource, but extracting meaningful insights for specific market research scenarios requires advanced capabilities. At Glass.AI, we’ve developed cutting-edge AI that understands language at scale, interprets diverse content sources, and integrates data from public domains. By analyzing web pages, news articles, social media, and official documents, our AI identifies valuable indicators to address complex market research questions. This enables researchers to derive insights directly from publicly available content, reducing reliance on traditional surveys. As a result, businesses can save time while gaining a more nuanced understanding of the market — uncovering trends and patterns that surveys alone might miss.
Here are some examples where our AI technology uncovered the insights:
COVID-19 Business Impact: During the early stages of the pandemic, the UK’s Office for National Statistics (ONS) sought to understand the effects on businesses. While phone responses dwindled, businesses updated their websites to reflect changes. By tracking over 500,000 websites, it was possible to assess closure rates and operational shifts.
Targeting Business Support Needs in Dorset: A regional economic partnership aimed to understand businesses’ support needs and traditional surveys had become less effective. Using AI to analyse digital content such as websites, news, and social media, they identified the types of support businesses mentioned and targeted resources accordingly which allowed the partnership to provide targeted, data-driven support to the businesses that needed it most, ensuring effective resource allocation.
Tracking Green Tech Adoption for the EU: The EU Commission wanted to assess the adoption of green technologies across different sectors. Language barriers and data inconsistency made it difficult to gather reliable information. By monitoring publicly available web sources, our AI was able to capture adoption patterns across industries and regions.
Analysing Productivity Drivers in West Yorkshire: A study on productivity drivers used web data to avoid potential biases found in survey responses. By analysing online content, a more objective understanding of factors influencing productivity across businesses was gained.
Mapping Supply Chains for DSIT: For supply chain research, information traditionally collected via surveys was instead gathered from business websites, news articles, and social media. This web-based approach identified relationships between companies and their suppliers, customers, and partners.
These examples illustrate how an AI-driven web research approach can provide deep insights without asking a single survey question. Each of these cases would traditionally have been solved with a survey.
Asking the Right Questions
While online content can often replace traditional surveys, there are still many cases where surveys are needed — for example, when seeking forward-looking perspectives or specific business opinions. In such cases, the rich web data can help refine and improve the survey process by identifying businesses to prioritise for contact and informing which questions should be asked. For example, a pre-screening of web data can highlight areas that don’t require further questioning or suggest more specific response options. This ensures the survey process is as efficient and targeted as possible, and can focus on where the respondents can offer the most value.
Both the EU and DSIT projects mentioned earlier used this hybrid approach, leveraging web data first, followed by traditional surveys to gather further insights. Combining AI insights with strategic survey questions can lead to even greater efficiency and effectiveness.
Maximising Efficiency with a Hybrid Approach
AI-based research presents an opportunity to optimize B2B surveys. Before launching a survey, consider which questions could be answered using online data or whether existing content could help refine the questions being asked. This approach streamlines the survey process, enhances question relevance, and improves efficiency for both researchers and respondents.