Benchmarking is essential for assessing a company’s credit risk. A £1m profit is excellent for one company but terrible for another. Context is everything. However, benchmarking private UK companies is difficult because their data is not publicly aggregated.
The most efficient way to benchmark UK private companies is to use an automated system to aggregate financial data for a defined peer group. This involves using modern industry classifications to build the group, programmatically extracting financials from filings, and then normalising the data (e.g., common-size financials) to compare key ratios.
Why is benchmarking private companies so difficult?
The core challenges are data access and standardisation:
- Data access: Financial data is locked in individual PDF filings at Companies House. There is no central, queryable database.
- Peer group definition: Creating an apples-to-apples comparison is difficult. Traditionally, this is done with Standard Industrial Classification (SIC) codes, but these can be outdated or too broad.
- Data standardisation: Different companies may use slightly different accounting labels or formats, requiring manual intervention.
What is the most efficient benchmarking method?
An efficient, modern workflow relies on automation to overcome these data challenges.
- Step 1: Define a precise peer group. Move beyond broad SIC codes. Modern benchmarking may use more granular industry classification systems or a bottom-up approach by identifying a list of known competitors.
- Step 2: Automate data aggregation. This is the most critical step. Instead of manually downloading and spreading financials for 20 peer companies, an automated tool is used. This tool programmatically fetches the filings for the entire peer group and extracts the key financial data.
- Step 3: Normalise and calculate. The system should automatically create common-size financial statements (showing each line item as a % of revenue) and calculate key ratios (e.g., margins, leverage, liquidity) for the company and its peers.
- Step 4: Analyse distributions. The analyst can then efficiently compare the target company against the peer group’s 25th, 50th (median), and 75th percentiles for each key metric.
What data is required for this process?
To be effective, this automated process requires a clean, structured, and comprehensive database of company data. The system must be able to:
- Identify companies within a specific industry.
- Access their historical financial statements.
- Parse those statements for key line items (Revenue, EBITDA, Debt, etc.).
- Store this data in a queryable format for analysis.