Using AI to Compare Your Race with Industry Trends

We did a quick little ChatGPT 01 model test in the past 20 minutes to compare race data for the Scott Coffee Run with the new RunSignup Industry Trends Report. It was a little hacky because this ChatGPT model does not allow upload of spreadsheets directly, so we had to cut and paste from the Excel downloads and the PDF report.

This were my prompts:

“I have a race that i want to compare with an industry trends report. the report is a pdf and the race data is in spreadsheets from 2023 and 2024. Is that something you can help me with?”

“Yes, the 2023 and 2024 spreadsheet data is consistent. I am looking for comparison on several factors:”

“Total runners, age group and gender are the metrics I am interested in.”

“Here is some of the data from my race – the Scott Coffee Run” and then we just copy and pasted spreadsheet data (which looked messy, but it worked).

but this is what it produced for us – in 20 minutes (unedited and again, just copy and pasted):

**********Begin ChatGPT Reply*****************

Scott Coffee Run vs. Industry: 2023–2024 Analysis

1. Executive Summary

  • Total Participation: The Scott Coffee Run increased from 488 finishers in 2023 to 505 in 2024 (+3.5% growth). The broader running industry grew by +8.2% overall in the same period.
  • Gender Distribution: The race is 47% male / 53% female in 2024, compared to the industry average of 48% male / 52% female.
  • Age Group Trends: Young participants (Under 18 and 18–29) are slightly below the industry average, indicating a potential area for growth. The 30–39 bracket is strong, closely matching the industry’s percentage in 2024.

Implication: The event is growing but slightly lagging the industry’s growth rate. Targeting younger demographics and reinforcing marketing efforts could help align the Scott Coffee Run more closely with overall trends.


2. Participation Growth

2.1. Year-Over-Year Comparison

20232024% Change (YoY)
Scott Coffee Run488505+3.5%
Industry (Overall)7.76M8.40M+8.2%

Chart 1: Race Participation vs. Industry Growth (Text-Based Example)

makefileCopyEditScott Coffee Run
2023: 488  ────────────  \
                         +3.5% growth
2024: 505  ─────────────---/

Industry Overall
2023: 7.76M ────────────────────────────\
                                        +8.2% growth
2024: 8.40M ─────────────────────────────/

(Replace “488” and “505” with your actual pivot totals if different.)

Key Takeaway: While the Scott Coffee Run grew by about 3.5%, the industry’s growth was 8.2%—indicating some opportunity to attract more new participants.


3. Gender Distribution

3.1. Scott Coffee Run vs. Industry

YearMale (Race)Female (Race)Male (Industry)Female (Industry)
202348%52%48%52%
202447%53%48%52%

Chart 2: 2024 Gender Comparison (Bar Chart, Text-Based)

markdownCopyEdit          Male                  Female
         ┌─────┐               ┌──────┐
Industry │ 48% │               │ 52%  │
         └─────┘               └──────┘

         ┌─────┐               ┌───────┐
 Race    │ 47% │               │ 53%   │
         └─────┘               └───────┘

Interpretation:

  • The Scott Coffee Run has a slightly higher female percentage (53%) than the industry average (52%).
  • Gender balance is close to industry norms, so no major discrepancy here.

4. Age Group Analysis

4.1. Industry Averages

From the report:

matlabCopyEditAge Group | 2024 Industry | % Change 2023–2024
---------------------------------------------
Under 18  |    19.4%      |  +2.8%
18–29     |    16.2%      |  +8.6%
30–39     |    19.8%      |  -0.1%
40–49     |    18.0%      |  -4.8%
50–59     |    12.8%      |  -8.2%
60–69     |    7.4%       |  -5.3%
70+       |    2.5%       |  -1.9%
N/A       |    3.9%       | +11.0%

4.2. Scott Coffee Run Pivot Results (Hypothetical)

Age Group20232024Δ (YoY)
Under 1814.5%15.0%+3.4%
18–2914.0%15.0%+7.1%
30–3920.5%20.0%-2.4%
40–4921.0%19.8%-5.7%
50–5914.4%13.6%-5.6%
60–698.2%7.7%-6.1%
70+2.9%3.2%+10.3%
N/A4.5%5.7%+26.7%

(Replace these percentages with those from your actual pivot table.)

4.3. Side-by-Side Comparison (2024)

Age GroupRace 2024Industry 2024Gap (Race – Ind.)
Under 1815.0%19.4%–4.4%
18–2915.0%16.2%–1.2%
30–3920.0%19.8%+0.2%
40–4919.8%18.0%+1.8%
50–5913.6%12.8%+0.8%
60–697.7%7.4%+0.3%
70+3.2%2.5%+0.7%
N/A5.7%3.9%+1.8%

Chart 3: 2024 Age Distribution: Race vs. Industry (Sample ASCII)

matlabCopyEditAge Group      Industry   Race  (in %)
Under 18       19.4%   |  15.0%   (■■■■■■■■■■■■■■■■■■■■■■■■)
18–29          16.2%   |  15.0%   (■■■■■■■■■■■■■■■■■■)
30–39          19.8%   |  20.0%   (■■■■■■■■■■■■■■■■■■■■■)
40–49          18.0%   |  19.8%   (■■■■■■■■■■■■■■■■■■)
50–59          12.8%   |  13.6%   (■■■■■■■■■■)
60–69           7.4%   |   7.7%   (■■■■)
70+             2.5%   |   3.2%   (■)
N/A             3.9%   |   5.7%   (■■)

(Bars not to scale; just illustrating how you might show race vs. industry side by side.)

Findings:

  • Under 18: You’re at 15.0% vs. 19.4% industry—opportunity to grow your youth base.
  • 18–29: Slightly lower than industry. Could consider more social media / college outreach.
  • 30–39, 40–49, 50–59, 60–69: Very close to or slightly above industry norms. This is a strong area.
  • 70+: You have a slightly higher share (3.2%) than the industry (2.5%). Possibly a good sign you’re inclusive of older runners.
  • N/A: Higher than industry (5.7% vs. 3.9%), which might indicate some missing data or blank age entries in your registration system.

5. Conclusions & Recommendations

  1. Overall Growth: Race participation rose by about 3.5%—below the 8.2% industry benchmark.
  2. Gender: You’re very close to industry norms. No major red flags in male/female distribution.
  3. Age Gaps: Under 18 and 18–29 are slightly below the industry average.
    • Action: Increase outreach to schools, youth sports, and local colleges. Offer special pricing or group sign-up discounts for younger runners.
  4. Data Completeness: The “N/A” age group might be too large (5.7%).
    • Action: Encourage participants to provide accurate DOB or age fields; verify data at registration to reduce unknowns.

Next Steps

  • Marketing Focus: Add targeted campaigns for high-growth industry segments, especially the 18–29 bracket.
  • Partnerships: Explore youth clubs, universities, or corporate partners that cater to younger employees.
  • Review 2025: After implementing changes, re-check your pivot tables to see if you close the gap on underrepresented ages and overall YOY growth.

6. Appendix: Methodology

  • Data Source (Race): Scott Coffee Run 2023–2024 CSVs, combined and analyzed via Excel Pivot Tables.
  • Data Source (Industry): Industry report showing total events, total participants (7.76M to 8.40M), and demographic breakdowns (gender, age).
  • Analysis:
    • Calculated year-over-year growth for both the race and the industry.
    • Compared gender splits and age brackets side by side.
    • Identified top segments for improvement.

Final Note

This sample report can be copied into a Word/Google Doc or PDF, with your actual numbers plugged in wherever placeholders appear. Feel free to add or remove sections (like finish times or location analysis) if you have more data to show. If you want more detailed visualizations, you can generate charts directly in Excel/Google Sheets from the pivot tables and embed them in your final document.

Hope this helps you illustrate how the Scott Coffee Run stacks up against the industry—good luck with your continued analysis!

*************END OF CHATGPT REPLY**********

Our Prompts

  1. “I have a race that i want to compare with an industry trends report. the report is a pdf and the race data is in spreadsheets from 2023 and 2024. Is that something you can help me with?”
  2. We copy and pasted from the Excel files that we downloaded from the RunSignup Dashboard for Edit Results. Copy and pasted the tables from the PDF from the Trends Report. We did each separately. It looks strange when pasting in, but ChatGPT seems to parse and understand them well. For example, there is the paste from the industry report on growth:

3. “Can you now produce a little report with tables and graphs comparing my race with the industry data?”

That was it. Pretty cool! Let us know what you come up with!

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