Data analytics & due diligence: Key ways to drive value creation
Data analytics can help private equity groups (PEGs) realize important benefits during due diligence — it can streamline the diligence process, reduce seller deal fatigue, and increase post-merger value creation. With deal flow again at an all-time high and staffing resources scarce, the return on investment in analytics technologies is clear and present. Through technology-enabled data gathering and analysis across all transactions, PEGs can better optimize human efforts, accelerate deal throughput, and reduce portfolio risk. To demonstrate, let’s take a look at the following four data analytics use cases:
1. Accelerate traditional due diligence efforts
Extracting timely, reliable data from your target’s accounting and ERP systems is often a challenge. Funds that use data analytics tools and services such as application programming interfaces (APIs) spend a lot less time going back to the target for missing data or reformatting reporting extracts. As a result, they’re more likely to identify data quality issues earlier in the process, if not avoid them altogether.
Standardized data models can be built to support like transactions, which allows analysts to eliminate costly data preparation and manipulation of general ledger, sales, customer, vendor, expense, and inventory data. Similarly, pre-built visualizations accelerate analyses of revenue, EBITDA, and cash projections, and support detailed margin analyses by product, customer, geography, and similar dimensions.
With our support, applying even a modest utilization of data management and business intelligence tools can save PEGs several hours in information requests and data prep. As we’ve partnered with our PEG clients over time, we’ve been able to further tailor specific solutions to their investment models, industries, platforms, and decision-making processes. This has cut days and even weeks from the tedious work of cleansing, transforming, and visualizing target data.
Even a modest utilization of data management and business intelligence tools can save PEGs several hours in information requests and data prep.
Understanding e-commerce growth trends in consumer goods businesses
We’ve seen increased investment activity in consumer packaged goods (CPG) during the COVID-19 pandemic. Investors believe many products will experience sustained demand but need to accurately understand the underlying trends driving demand, particularly in new digital and e-commerce channels.
Because e-commerce growth often hasn’t maintained the same net margin as in-store sales, it’s important to understand — quickly — how the changes in mix and volume may or may not support future revenue and profit projections. Combing through large customer sales datasets to identify the impact of pandemic-influenced growth in e-commerce versus cannibalized in-store sales is a critical CPG analysis — an effort made timely with analytics technologies, and without which would be daunting and prohibitive.
Enabling a deeper dive into operational KPIs in healthcare
Some of the highest value creation in the diligence process comes from the ability to create transparency and insight beyond general ledger information, drilling down through financial results into underlying operational source data. However, this data can also be harder to get to, more complex, and less standardized.
Typically, numerous niche applications support the operations in any given industry or sector, with varying levels of maturity in their respective data tables. Often these are not well integrated into their sister accounting and financial reporting systems. Physician practice management systems are one common example. By applying some of the data modeling and standardization strategies described above, we’ve been able to consistently serve up timely operational and financial KPI trend data and insights that aren’t possible using traditional tabular analyses performed in spreadsheets.
By focusing in on key performance indicators (KPIs) related to new and active patient data, production per visit, and collection rates, and then drilling down into the underlying medical practice system data, an investor can quickly identify location-specific opportunities and risks that often can influence deal structure, pricing, and terms.
Inventory cost analytics in asset-based transactions
The ability to accelerate the preparation, modeling, and visualization of inventory cost data can also directly impact the structuring of an asset-based deal. If not discovered upfront, inventory valuation issues can result in misstated returns and unforeseen liquidity issues soon after the transaction closes. It usually takes multiple, complex datasets to generate inventory cost insights — insights that are extremely difficult and time-consuming to uncover using spreadsheets.
It usually takes multiple, complex datasets to generate inventory cost insights — insights that are extremely difficult and time-consuming to uncover using spreadsheets.
2. Challenge assumptions
In order to manage the cost and duration of a diligence process relative to the value and risk of the transaction, investors often rely on information that’s been deemed credible or accurate by various parties internal and external to the target company. But PEGs can miss major threats when they assume data quality or don’t adequately scrutinize information.
Analytics technologies can be used in a forensic manner, allowing the PEG to “trust but verify” previously reviewed and even audited financial statements and projections. Using common data discovery, filtering, and validation techniques, we’ve helped our PEG clients retest financials and projections against established fund metrics, benchmarks, and assumptions to quickly identify abnormalities and potential risks. Such efforts can uncover significant adjustments, including revenue recognition and normalizing EBITDA adjustments, that the PEG’s diligence team — or even third-party auditors — might not otherwise identify.
3. Grow advanced analytics capabilities to tap into unrealized value and foresight
The examples above primarily illustrate descriptive analytics, which answer questions such as, “What happened?” or “What’s the accurate value or projected future value?” Diagnostic analytics goes further to uncover root causes — “Why did this happen?” — and predictive analytics focuses on the future: “What will happen?”
These kinds of insights require more complicated data architecture (e.g., complex joins, transformations, data matching, and calculations), and they commonly employ advanced analytics capabilities. These include statistical analysis, what-if scenario modeling, and artificial intelligence, including machine learning. This depth of analysis currently isn’t merited for a prototypical early-stage investment, but we expect that to change as analytics technologies and related skills in the labor market mature. As such, it’s important to have a vision and plan for how you’ll continue to build and mature your capabilities.
Taking the earlier CPG example, the next level of analysis moves beyond identifying what is happening with consumer demand to evaluating why it’s happening and what will happen if the same trends continue. Why are e-commerce margins lower in the first place, and what can be done to increase them?
By presenting sales volume and pricing trends by category alongside marketing investment, trade, and promotional spending as well as supply chain cost data, analysts can quickly identify and evaluate potential long-term profitability risk and opportunities in both in-store and ecommerce channel sales forecasts.
The level and complexity of analysis in this type of margin intelligence is extremely difficult if not impossible to do with spreadsheets and manual data integration and transformation efforts. However, sophisticated data models in high-impact, high-complexity analyses help our clients see the critical story being told by the data. Often, this is the insight needed to signal performance issues bound to arise shortly after closing; in other cases, the insights can point to opportunities for achieving alpha.
Sophisticated data models in high-impact, high-complexity analyses help our clients see the critical story being told by the data.
4. Leverage data assets from the diligence phase to improve integration outcomes
Any acquisition is disruptive to business, but the faster you can define and align portfolio company leadership with the desired target state and the execution of a plan to get there, the faster you can drive toward profitable growth.
In our experience, a data-driven approach to integration allows for more accurate, timely optimization of business activities, increased accountability and alignment around decision-making, higher change adoption rates, and lower turnover. Digitalization and automation also reduce the post-close risks associated with undocumented processes and loss of significant institutional knowledge.
Let’s revisit the physician practice use case above. Patient leakage data evaluated during the diligence phase can can serve as a foundation for a 100-day plan to improve practice economics by practitioner. By investing further in the data, resources, and capabilities necessary to transform this into a robust sensitivity analysis, the acquirer can quickly set post-acquisition retention rate targets, pricing, demand forecasts, and revenue planning. Maintaining continuity of data and analyses from diligence to integration sets the stage for real-time monitoring of the most critical KPIs and drivers of return on investment, and helps proactively identify the need for early corrective actions.
Don’t wait — Realize the benefits of data analytics during diligence
Whether you rely on an internal resource or a strategic partner — today’s transaction volume, the fierce war for talent, and the rising affordability of relatively low-tech tools make an increasingly irrefutable case for PEGs to deploy analytics. While it does take some investment upfront, successful analytics programs can be built iteratively and nimbly, enabling PEGs to innovate while keeping overhead costs low.
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