Lower borrowing costs, more aggressive lending and excess cash continue to prolong the elevated valuations of middle- and lower-middle-market companies.  The sustained higher prices have created a fiercely competitive buying market and upset the balance between acceptable risk and return. As a result, many investors still find themselves sidelined—unable to justify returns necessary for the risk profile.

But for investors willing to look beyond multiples to a business’s underlying profitability drivers for value, there is a way to navigate the competitive landscape and compensate for higher valuations at an acceptable risk. The way forward is to leverage “little data,” which can reduce the uncertainty of future financial performance through better understanding of a company’s profit-mix dynamics.

What Is Little Data?

You’ve likely heard the enthusiastic promises of Big Data.  You may have tuned them out.  That’s because Big Data—which relies on the accumulation and processing of large, complex data sets from sources outside the target company—is impractical for many middle- and lower middle-market deals.

Yet unlike Big Data, little data is the lowest level of transaction data from within the target company. It is the detailed transaction information that lives deep in the databases of the target’s information system.  It can include financial, sales order, purchase order, production, logistics or other data that the target may not have the skill or understanding to mine itself.

Leveraging the power of little data can increase investors’ understanding of their investment beyond that of the seller—and in some cases can even reset an investor’s demand for acceptable risk and return.

 The Case for Little Data

Consider how each investment includes certain levels of both systematic and nonsystematic risk.  We’ll define systematic risk as the external risk associated with future cash flows, affected by the general market risk relevant to the target. In contrast, nonsystematic risk is the internal risk associated with future cash flows, affected by the company’s specific risks.

While it’s difficult to reduce systematic risk because of unforeseen market conditions outside an investor’s control, it’s possible to reduce a certain amount of nonsystematic risk—the risk posed by the target’s product mix and individual product contribution margin. Nonsystematic risk can be reduced only if an investor better understands what drives future Ebitda variability. 

The two largest unknown dynamics affecting Ebitda variability are the impact of changing volume and mix.  Since overall volume depends on numerous variables, including outside factors such as market conditions and competition, quantifying the impact of volume variation across a changing mix is difficult. However, if profit-mix dynamics were known, then profitability could more easily be quantified even with changes to volume.

But in middle- and lower-middle-market companies, a company’s profit mix dynamic is often unknown for several reasons, including incomplete data or systems, poor reporting or even an uninformed management team. The challenge is how to navigate these obstacles within the short time frame of exclusivity, often with limited access to the acquisition target.

A Four-Step Process

In order to overcome these obstacles and effectively leverage little data to make more of the company’s profit mix dynamic known, investors should consider a four-step process.

Step 1:  Extract Data – Target management may be limited in the financial information it can provide because of perceived information system constraints or lack of technical expertise. However, while relevant management reporting may not exist, the underlying transaction-level data does. Therefore, it’s possible to bypass reporting limitations by extracting the transaction-level data directly from the system. This can be done by developing system queries or utilizing Open Database Connectivity (ODBC) drivers or other data source drivers to extract the data directly. 

In cases where target management is limiting information access, it will help investors to have a clear knowledge of the exact transaction-level data needed so they can concisely frame their information request.  For example, requesting specific sales order detail, purchasing data, customer master data, product or service configuration and employee master data that can then be analyzed can be much more useful to your assessment of the target than imprecise, high-level management reporting that might not reflect the underlying economics of the business or expose natural biases of the seller. Furthermore, in situations where target management may be hesitant to share sensitive data, investors can propose blinding certain data or creating a master key that allows data to be unveiled as the process advances.

Step 2:  Accumulate Revenue & Cost – With data in hand, the next step is to accumulate revenue and cost data and assign it to the underlying activities of the target. At this point, it may be possible to construct a product- or service-level cost model.  For example, sales order detail can be used to group products or service and customer revenues. Corresponding direct costs can be found using purchasing detail, including bill of materials, service line detail, labor routings/reports and payroll information.  Other variable and fixed costs contained in the general ledger may be assignable to particular products, customer or resource/work groups or partially assigned based upon other directly correlated drivers.

Even when information systems have not been fully implemented, pockets of usable data often exist. It’s critical to not over allocate or spread cost in a manner that doesn’t represent the true variable economics of the business. The goal is to create product- or service-level economic performance and avoid cost-assignment assumptions that eliminate product specificity, thereby dampening the impact of how change in mix affects profitability.

Step 3:  Understand the Economics – After accumulating revenue and cost information, investors may discover a new economic view of the target.  Through “bottom-up” economic modeling, investors can gain cross-sectional insight into profitability across different areas of the business including product and service lines, customers, vendors and suppliers and end markets. With a deeper understanding of the profit-mix dynamic of the target, the investor now can better confine future Ebitda variability—and in some instances rebalance what previously was unacceptable risk versus return.

Step 4:  Identify Risk & Opportunity – Once the underlying economics are understood, the investor has a clear roadmap to identify various enhancement opportunities, such as profitability improvement, product and service line rationalization and strategic pricing opportunities.  Identifying these opportunities through the use of little data affects the investor’s view of the overall value of the transaction. In this way, little data actually can embolden some investors to compete at higher prices, because they have a view of profit enhancement opportunities not seen by other buyers.

While it’s not clear how long current market conditions will continue, reducing uncertainty will continue to be a key to smart investing.  As such, while little data may be small in nature, it has the potential to have big impact.

Patrick Gilbert is a director in transaction services for BKD LLP, a national CPA and advisory firm.

Financial services industry