Sell-side analytics

Getting ahead of the sell-side curve

Phil Wong

Phil Wong

Principal, Advisory, Technology Industry Strategy, KPMG US

+1 617-899-8999

Joel O'Hair

Joel O'Hair

Specialist MD, Analytics, Strat - Center of Excellence, KPMG US

+1 303-382-7534

The seller in any M&A transaction must provide adequate information to potential buyers. But in a hot sector like TMT with high valuations, that burden is amplified by more demanding buyers and tight deadlines. It’s commonplace now for the buyer and their advisors to rely heavily on data analytics to build their investment case and identify potential areas of value creation. For sellers to tell their side of the story with confidence-and avoid delaying or even derailing the deal—they need to get ahead of the curve and get their (data) house in order.

Buyers are wary of seller metrics that are either too general or cherry-picked.

PE firms are prominent players in TMT deals and often bring a high degree of scrutiny to the seller-provided data. They are wary, however, of seller metrics that are either too general (e.g., bookings growth) or cherrypicked and defined to showcase them in the best possible light. In addition, if the underlying data comes from disparate sources ladened with inconsistencies, it creates unnecessary distractions from the seller’s core value proposition.

Investing in sell-side data analytics, especially well ahead of the commencement of the sales process, can help companies add value across the deal lifecycle by increasing the availability of rigorous data and enabling faster due diligence. Also, getting firm control of the data early on can lead to new ways to articulate the underlying value of the business and its growth potential. For TMT companies, some basic but important performance metrics in addition to EBITDA include analyzing the recurring revenue roll-forward, retention by cohort, and land and expand dynamics:

  • Recurring revenue roll-forward: Understand increases and decreases in recurring revenue overall and at the product/business unit level
  • Retention by cohort: Evaluate customer revenue paths via gross, net, and customer retention over the customer tenure, grouping customers by start date
  • Land and expand dynamics: Build views of customer lifetime value (CLTV) by quantifying total value by cohort tenure, including typical product expansion patterns

Processing these large data sets at deal speed isn’t easy. Companies need robust analytics platforms that produce KPIs rapidly and granularly, and in a format that can be plugged directly into your model. Automation and flexibility also need to be built into the platform for faster insights. Best practices for sell-side analytics include:

  • Starting early (with a first-round analysis of the top line two to three quarters in advance of launching the sale process)
  • Connecting the analytics with the income statement and business plan assumptions
  • Aligning metrics and KPIs with the value story (e.g., target customer segments, conversion to new platforms)
  • Going to the next level of detail to unpack deeper underlying trends and strengthen the value story

KPMG recently helped solidify recurring revenue metrics of a carve-out unit of a public company being sold to a PE firm. We developed KPI calculations, including a market standard definition of gross and net retention, which was higher than management’s internal version. We also performed cloud migration impact calculations and discovered that the seller had declining legacy products whose customers could be migrated to its next-generation products. Lastly, we made sure the seller was able to meet the deadline for providing information and data to bidders.