Early Signals Optimize Commercial Due Diligence Efforts

In both the commit and reject path, the value of information can depend on when in the process insights are developed. Early in the process, sponsors have much greater ability to avoid future costs and preserve resources and team bandwidth in the reject case and are much more likely to build an edge in a competitive process in the commit case. Later in the process, the value of information is diminished as both advantages subside.

Fortunately, the tools and approaches to developing rapid insights have improved, especially in data analytics. The ability to ingest, clean, and process greater volumes of more detailed data has accelerated the type and quality of early insights available. The ongoing evolution of machine learning and AI tools continues to accelerate the ability to process and drive useful findings from large datasets. While early market indicators are key and can be gathered from external data sources, market and expert interviews, early customer and company indicators can only be adequately understood via acquisition and analysis of detailed, granular internal company information.

A recent Stax case study of a private equity sponsor considering an industrials target is illustrative. Early market and demand indicators were positive, or at least as expected. Early high-level company metrics were strong: the target demonstrated significant revenue growth over an extended period, with especially strong growth in recent years. The initial read from the deal team was one of strong commitment to pursuing the deal.

However, granular customer analytics surfaced weakness below the high-level numbers. Stax was able to rapidly acquire and process detailed customer and transaction/order level data, and analysis of this data showed a more nuanced picture. While recent revenue growth was strong, when disaggregated through detailed analysis, a more complicated picture emerged. Most obvious was a series of recent acquisitions that in some ways masked core business decline. In addition, those acquisitions had introduced data quality issues into the mix with resulting difficulty to see accurate customer-level trends.

Once data issues were understood and quantified, better insight could be gained. The next insights involved identifying substantial recent price gains in the core business, with some corresponding volume declines. Of further concern was customer churn, with continuing core customer counts declining across multiple categories.

Because these potential issues surfaced within one week of acquiring data, difficult questions were brought forward in the process for the deal team and management. Ultimately, the deal team chose not to pursue the deal, which allowed them to proactively avoid incurring significant additional diligence-related costs and time. Bringing such insights forward in the process created significant value. At Stax, we see this play out often, whether the insights lead to more rapid shut-down of the process, or to greater early commitment to pursuing the deal. Our client is quoted as saying:

“Bringing analytics insights early into the process allowed us to move more quickly and efficiently through our decision-making process, saving significant time and resources and getting to the right decision faster.”

– Partner, Middle-Market Industrials Sponsor

In the cases of greater commitment to the deal, it is common for early analytical work to generate hypotheses for how to grow the business (such as geographies that are underserved, or products that are performing well). These are often tested further in the diligence process or post-close.

Across the hundreds of deals Stax works on per year, we see common success factors for developing early signals, and increasing value of commercial diligence work and analytics:

• Prioritize access to granular, detailed customer and transaction level data.
• Reduce the load on management teams by asking for comprehensive data access or transfer to reduce processing and analysis time for management.
• Take steps toward standardization, at least within industry verticals:  a more common structure of early customer analysis will increase speed and efficiency across deals and create data for internal benchmarking.
• Think probabilistically: early indicators have value, even when the final, absolute answer is not yet available or requires more questioning and work to discover.
• Build a process with frequent communication and iteration with internal teams and external consultants.
• Seek partners with the right combination of advanced data analytics skills and industry expertise and insights.

In this way, successful sponsors use commercial diligence, and in particular the analytics component, to build a useful signal to the ongoing process rather than a check-the-box market report. Both external and internal data views are critical to making this happen. Sponsors that build internal team capabilities and seek diligence partners with the ability to build data insights quickly, build an enduring edge over time. Doing so efficiently requires a combination of advanced data processing and analytics capabilities, paired with industry and business expertise and judgement to guide, and prioritize efforts.