Using Predictive Analytics to Inform Add‑On Targeting for Platform Investments
From The Buxton Co
The buy-and-build strategy has become a go-to play in every general partnerís (GPís) playbook. Roughly half of all buyouts in 2017 and a third of all buyouts in Q1 of 2018 have been add‑ons to existing platforms. This method of value creation is being explored not only by mega funds, but also lower middle market funds.
Why Pursue an Add‑On Strategy?
Add‑ons to platform investments are appealing because of the ability to create value in an era of high platform valuations. Additionally, lower middle market firms can strategically position themselves to build out platforms that will eventually be attractive to strategic buyers or larger funds by way of secondary buyouts. It can be impractical and nearly impossible for a lower middle market fund to acquire already developed and expensive platforms especially when competition during the bid is as stiff as ever.
Additionally, with the commitment to develop a company through the buy-and-build strategy also comes the responsibility of effective growth stewardship, which often leads to longer holding periods. Capital can be more effectively deployed through the add‑on strategy because each add‑on can be carefully evaluated as opposed to making one large investment in a platform with potential unknowns. The add‑on strategy enables the platform builder to exercise more control over the parts making up the whole.
A New Approach to Add‑on Targeting Due Diligence
While add‑on deals have gained momentum, higher multiples, record fundraising levels, and the persistently high internal rate of return expectations of limited partners (LPs) have created a challenging and competitive environment for buyout deals. Todayís environment demands not only increased scrutiny during the diligence process but also creative thinking when it comes to value creation. The add‑on strategy is not going away any time soon and outperforming GPs will need to leverage technology and predictive analytics to make smarter decisions. Predictive analytics solutions relating to add‑on targeting often include core patient definitions, add‑on suitability studies, and whitespace or infill opportunities.
Using predictive analytics during the due diligence process isnít an entirely new concept. Top firms active in buy‑and‑build strategies are leveraging technology and predictive analytics to make faster and smarter add‑on targeting decisions.
However, using predictive analytics to evaluate add‑on investments is an area that few firms have explored. Results of these studies have enabled firms to develop pre-determined add‑on criteria and automate go/no-go looks instantly. With the click of a button, firms considering a small multi-unit add‑on for a platform investment will know if it is worth continuing the diligence process or if they should move on to other options. In most cases, these insights are produced with absolutely no interaction with the target.
Efficiency gains that result from the use of such technology enable firms to be more competitive. Resources are better allocated towards targets that are most suitable for the platform and less time is wasted sifting through potential add‑ons. This inherently minimizes the downside risk associated with add‑on acquisitions and empowers the firm to bid with confidence.
How it Works: Analytics in the Add‑On Due Diligence Process
Analytics-based commercial due diligence specifically designed for add‑on targeting can inform a wide range of objectives but is typically tailored to the specific platform. It usually addresses core patient/customer definitions, drivers of performance, and favorable trade area identification.
The core patient/customer definitions reveal the behavioral and attitudinal characteristics of the core patient/customer.
RFM (Recency, Frequency, and Monetary) insights help to inform the core patient/customer definition. This often leads into identification of trade area characteristics that are consistent with the platformís top performing units.
An understanding of performance drivers and the core patient/customer definition then produces the ability to locate trade areas with all the right characteristics to harness a top performing unit.
This entire process is then automated, and GPs can generate reports that will indicate whether a target is in trade areas that have shown to support outperformance within the existing platform. Moreover, this information can be leveraged to produce a market study that indicates where the best trade areas within the region of interest or across the U.S. are located. GPs can then filter down the geographical landscape when hunting for targets and minimize the risk of focusing efforts on potential add‑ons with a low likelihood for success.
The Bottom Line
It is more critical than ever that PE firms maintain a competitive advantage given the landscape of high multiples and record levels of dry powder. Traditional approaches to commercial due diligence will leave GPs exposed to downside risk that can be mitigated using predictive analytics.
Add‑ons to platform investments will continue to be a viable strategy, and lower middle market funds must apply additional scrutiny to the allocation of resources to stay competitive. Knowing when to dig in or pull back during the bidding process is key and those firms that leverage new technology and predictive analytics will reduce the risk of offering too much for an add‑on that is not consistent with the platform strategy.
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