Detailed Revenue and Cost Builds

When a business doesn't provide granular revenue or cost details (e.g., by unit, user, or product), analysts often rely on creative approaches and alternative data to build a detailed PxQ (Price x Quantity) model or segment-level forecasts. Here's how you can approach it:

1. Segment-Level Forecasting as a Starting Point

• If the company only provides segment-level data, you can start by breaking down revenue into its key drivers (e.g., price, volume, market share, etc.).
• Use management guidance, historical trends, and industry benchmarks to estimate these drivers.

2. Alternative Data and Proxy Metrics

• Analysts often turn to alternative data sources to fill in the gaps. For example:
  • Job postings: If a company is hiring aggressively in sales or operations, it might indicate an upcoming push for growth.
  • Credit card or email receipt data: These can provide insights into sales trends or customer behavior.
  • Market growth data: Use third-party reports to estimate the total addressable market (TAM) and the company's share within it.
• These data points can help you decompose revenue into price, volume, and market share components.

3. Quantifying a Variable View

• When you lack precise data, you can create a range of scenarios to quantify uncertainty:
  • Base Case: Use historical averages or management guidance.
  • Bull Case: Assume higher growth rates, better pricing power, or increased market share.
  • Bear Case: Assume slower growth, pricing pressure, or market share loss.
• This approach allows you to present a range of outcomes and highlight the key variables driving your assumptions.

4. Granularity Through Assumptions

• Even without unit-level data, you can make educated assumptions:
  • Revenue: Break it down into price per unit and units sold. If unit data isn't available, use proxies like customer growth, website traffic, or sales team expansion.
  • Costs: Estimate costs based on industry norms, gross margin trends, or cost structures of comparable companies.

5. Iterative Refinement

• As you gather more data (e.g., earnings calls, management commentary, or alternative data), refine your assumptions and models.
• Focus on key drivers that management emphasizes, such as pricing power, customer acquisition, or operational efficiency.

6. Communicating Your View

• Be transparent about the limitations of your data and the assumptions you've made.
• Highlight the sensitivity of your model to key variables and explain how changes in these variables impact your forecasts.

In summary, while segment-level forecasting might sometimes be the best you can do, leveraging alternative data, creating scenario analyses, and making educated assumptions can help you build a more detailed and defensible PxQ model.

Sources: Q&A: Equity Analyst at a Sovereign Wealth/Pension Fund, https://www.wallstreetoasis.com/forum/asset-management/will-asset-management-industry-just-wither-away?customgpt=1, Why a lack of advanced modeling techniques? Anyone who uses probabilistic modeling justification for this?, My PE Recruiting Process, Private Equity - Working with Imperfect Information