1. Challenge: Research turnaround was too slow
- The Problem:
NASCAR needed to deliver insights quickly to internal stakeholders — often within a week or less. Traditional research methods took 4+ weeks, with analysis and reporting being the most time-consuming processes part of these projects. - Knit’s Solution:
Knit provided report-ready insights within 24 hours of survey fielding. NASCAR was able to move from research objectives to presentation-ready slides in days — not weeks or months.
“Being able to get the data at your fingertips with a platform like Knit is so quick… we've found a lot of efficiencies.”
— Maxwell Miranda, Senior Manager of Insights at NASCAR
2. Challenge: Too much time spent on analysis and reporting
- The Problem:
NASCAR’s insights team was spending too much time turning raw data into stories, delaying their ability to act or share findings broadly. - Knit’s Solution:
Knit’s AI automatically generated slides, insights, and narratives based on business objectives. This freed up the team to focus more on strategy and stakeholder storytelling — not data wrangling.
“It really allows you to… create that narrative you want to from the data.”
3. Challenge: Difficulty creating stakeholder-ready deliverables
- The Problem:
Transforming raw survey data into polished presentations was time-intensive. NASCAR often needed deliverables they could confidently show to executives or partners quickly. - Knit’s Solution:
Knit provided PowerPoint-ready slides with key takeaways, charts, and AI-summarized findings that were usable immediately. NASCAR even presented insights directly from Knit’s platform.
“We've even presented within the Knit platform and used the slides to showcase data.”
4. Challenge: limited resources to analyze open-ended feedback
- The Problem:
NASCAR’s team was small and couldn’t keep up with manually coding thousands of open-ended responses, especially from fan experience surveys. - Knit’s Solution:
Knit’s AI-powered text analysis turned qualitative feedback into quantitative insight automatically — tagging themes, surfacing trends, and backing them up with verbatim quotes.
“The AI text analysis platform is probably our favorite part… turning qualitative into quantitative is super helpful.”
5. Challenge: Inability to Leverage Data from Other Sources
- The Problem:
NASCAR had valuable data sitting in other systems (e.g., guest experience feedback) that wasn’t being analyzed deeply because of time and tooling limitations. - Knit’s Solution:
Knit allowed NASCAR to upload CSVs from other sources into the platform. They could instantly run AI analysis on open-ends and extract story-driven insights from previously untapped data.
“We uploaded the CSV… Knit helped us pull out that concerts were the second most important thing after racing [at events].”
6. Challenge: Difficulty Segmenting and Customizing Insights On Demand
- The Problem:
When stakeholders asked for new data cuts or follow-up questions, NASCAR couldn’t easily segment data by audience (e.g., Gen Z, Hispanic fans, casual vs. Avid). - Knit’s Solution:
With AskKnit, NASCAR could ask the AI any question — even during a presentation — and get slides tailored to specific segments in minutes. It enabled live Q&A with data, not delays.
“We’ve used this feature live as questions come up… it populates insights we need on the spot.”
7. Challenge: Making Qualitative Feedback More Impactful
- The Problem:
Quotes and open-ends were often buried in static reports and didn’t convey the full emotion or impact of the fan voice. - Knit’s Solution:
NASCAR used Knit’s video capabilities to capture fan reactions and assemble sizzle reels for internal and sponsor presentations — making the insights more visceral and memorable.
“Just hearing and seeing their reactions has been amazing… it makes qualitative feedback more impactful.”
8. Challenge: Reaching Beyond Existing Fan Base
- The Problem:
NASCAR wanted to reach non-fans, younger audiences, and diverse segments, which its internal panel didn’t always cover. - Knit’s Solution:
Knit provided access to high-quality external respondents (via its panel), enabling NASCAR to explore new audiences with the same level of data fidelity.
“Whether it’s non-fans, Casual fans, or Gen Z… we always get the segmentations and cut that we need from Knit.”
