Modeling Data Heavy Sectors w/o Alt Data

When modeling data-heavy sectors like auto dealers, salvage auction markets, or used parts providers with limited disclosures and without access to alternative data, here are some actionable steps based on the most helpful WSO content:

1. Focus on Key Drivers:

   • Identify the primary drivers of the business. For auto-related sectors, these could include:
     • Volume trends (e.g., units sold, auctioned, or processed).
     • Average Selling Price (ASP) trends.
     • Gross margins by segment (if available).
     • Macroeconomic indicators like GDP growth, consumer confidence, and interest rates.
   • Use management's directional commentary to estimate these drivers, even if hard KPIs are unavailable.

2. Leverage Publicly Available Data:

   • Use industry reports, government data, or publicly available statistics to fill in gaps. For example:
     • Seasonally Adjusted Annualized Rate (SAAR) for auto sales.
     • Scrap metal prices or used car price indices for salvage markets.
     • Regional economic data to estimate demand trends.
   • Tie revenue or volume assumptions to these external benchmarks for credibility.

3. Build a Flexible Model:

   • Create a model that allows for scenario analysis. For instance:
     • Use base, upside, and downside cases for key assumptions like volume growth or ASP changes.
     • Incorporate sensitivity analysis to test the impact of macro variables (e.g., interest rate changes on financing costs).

4. Historical Data Approximation:

   • If historical data by channel is unavailable, consider:
     • Using proxy data from competitors or industry averages.
     • Estimating trends based on management's past commentary or earnings call transcripts.
     • Back-solving for implied metrics using reported financials (e.g., revenue divided by estimated volume to approximate ASP).

5. Thesis Validation Without Alt Data:

   • While you may lack access to scrape data or providers like Yipit/Mscience, you can:
     • Conduct primary research, such as channel checks or supplier/customer calls, to gather anecdotal evidence.
     • Monitor public sentiment or trends using tools like Google Trends for search interest in related terms.
     • Use historical correlations between macro indicators and company performance to validate your thesis.

6. Highlight the Limitations:

   • Acknowledge the gaps in your model due to limited disclosures or lack of alternative data. This transparency can strengthen your credibility when presenting your thesis.

By focusing on these steps, you can construct a robust model and thesis even in the absence of comprehensive data. Remember, the goal is to make reasonable, defensible assumptions and to stress-test your conclusions.