Market research is highly fragmented: it arrives from multiple departments and providers in presentations, spreadsheets and other formats. Sometimes it’s in aggregate, sometimes raw, and is consumed by disparate teams who may not even talk to each other.

Qualitative and quantitative research mixes language and numbers, with current AI and conventional capabilities coming up short on integrating them. This results in labor-intensive human analysis to understand what every question and response really means.

Despite big corporate investments in business intelligence, research often doesn’t make it to data warehouses due to high setup overhead. Without this laborious prep, even the best new AI tools can’t make full use of your data.

Customer profiles are collections of thousands of attributes, but traditional analytics often can’t see the bigger picture. They often only look at narrow slices of data and ignore important connections. New AI tools still rely on legacy system queries to pull insights from data warehouses, so they fall into the same trap.