How we helped a Private Equity client in performing due diligence analysis for the target company operating in HCR Industry

The Context

The client, a Private Equity firm, wanted the TresVista team to perform a Due Diligence analysis on a target company operating in the USPS Highway Contract Rout (HCR) Industry. The client wanted to draw inferences about the operations of the firm by analyzing various files of the Data Room, consolidating similar types of information in organized excel files, and performing a competitor analysis.

The client also requested to assist with a CIM along with creating a 3-page teaser for the investors.

The Objective

To convert raw data files into useful information for analysis and assumptions for the Model.

The Approach

The TresVista team followed the below:

• Sanitized and Consolidated the Data: Formatted various raw data files into standardized formats and created summary tabs
• Identified Discrepancies: Analyzed various summary tabs to understand the firm’s operations and identify any discrepancies/gaps in data
• Created the CIM Template: Created the structure of the CIM and sourced the required information via online search/data room files
• Created the Teaser: Created a 3-page summarized teaser using information from the detailed CIM
• Performed Competitor Analysis: Built a template and leveraged Factset to pull data and analyzed the performance of the top 20 peers in the industry

The Challenges We Overcame

The major hurdle faced by the TresVista team was to ensure that no data point was missed as it would hamper the overall summary analysis. The frequent updates from the target company led to changing the assumptions to incorporate the new data points. The competitor data and metrics based on several underlying assumptions had to be standardized. This required managing and working simultaneously on various workstreams.

The TresVista team overcame the hurdles by communicating well with the client, as it was necessary to explain each step in detail. The team performed technical analysis and provided a total output by effectively handling a large amount of data.

Final Product (Sanitized)