Key Takeaways

  • David Toms, partner at Hg, avoids top-down sponsor mandates by using executive ego and peer collaboration to drive operational improvements across dozens of B2B software assets.
  • Portfolio executives resist directives from private equity sponsors sitting outside daily operations, preferring advice directly from peer operators who share their operational realities.
  • Hg shares full distribution rankings with CEOs and CFOs while anonymizing other assets, showing leadership teams exactly where their business ranks against the entire portfolio.
  • Lagging performers consistently ask two questions upon seeing low quartile ranks: how to improve, and which top-performing peer they should speak with to fix the deficit.
  • This operational playbook is codified in Hg's Friendly Competition Benchmarking Method.

The Hg's Friendly Competition Benchmarking Method

Step 1: Collect and Benchmark Comparable Portfolio Data

Gather operating data across similar portfolio businesses on a specific priority metric (e.g., Days Sales Outstanding / DSOs).

Step 2: Produce Anonymized Relative Rankings

Generate a full ranking distribution across the entire portfolio without revealing every individual company name.

Step 3: Private Distribution to Leadership

Send the overall distribution to each CEO and CFO privately, revealing only where their specific business sits relative to the whole group.

Step 4: Facilitate Peer-to-Peer Knowledge Transfer

Connect lagging companies with top-decile performers willing to share their playbooks, allowing portfolio executives in the arena to resolve operating gaps organically rather than enforcing top-down sponsor mandates.

When This Works (and When It Doesn't)

This method functions cleanly when portfolio companies share identical revenue models, unit economics, and operational mechanics. In Hg's core domain of application software, metrics like Days Sales Outstanding (DSO), net retention rates, and customer support ticket resolutions behave similarly across companies. When the data is clean and directly comparable, the ranking carries immediate credibility with analytical executives. As Toms explains, “We find the single most effective thing is to rely on people's natural desire to help each other, coupled with their natural desire to compete.”

It fails when private equity firms attempt to apply it across heterogeneous, multi-sector portfolios. Comparing working capital efficiency or sales velocity between an industrial manufacturer, a healthcare clinic network, and an enterprise SaaS company creates friction instead of healthy competition. CEOs immediately reject the data on the grounds of structural differences in their end markets. If an executive believes their lower ranking stems from business model differences rather than operational execution, defensiveness replaces constructive action. Toms notes that the sponsor's position outside daily operations weakens top-down demands: “You don't have to go in there with your big stick beating them and saying, 'You need to improve your DSOs. You need to do X, Y, Z,' because you're the guy from the private equity company. You're not the guy who's in the arena fighting the battle. They wanna talk to other people in the arena fighting the battle.”

Why It Matters

As multiple expansion cools and value creation relies almost entirely on organic growth and margin expansion, sponsors face rising friction when trying to enforce standard playbooks. Operating partner teams often expand headcount rapidly, only to run into institutional resistance from portfolio executives who view sponsor operating groups as detached overhead. Hg's mechanism shifts the burden of operational turnaround from expensive internal consulting teams directly onto peer networks.

By converting operational variance into a private status signal, sponsors trigger executive action without creating governance gridlock. The mechanism demonstrates how sector specialization creates structural advantages: funds with tight sector focus can generate proprietary internal data sets that generalist funds cannot match. This creates operational scale advantages that compound across successive fund vintages.