Running M&A Due Diligence on a Live HCM SaaS Deal

Tushar joined JPMorgan's M&A North America team as the junior analyst on a live deal covering an enterprise Human Capital Management SaaS platform. The mandate was to run the sell-side process: prepare the company for buyer scrutiny, manage the due diligence flow, and keep the transaction moving.

He and his associate were a two-person working team. They owned the due diligence process end to end, splitting workstreams across financial modeling, buyer question management, and client coordination, with their VP providing oversight and comments.

The client data was rarely clean. Tushar's first task on each workstream was to take raw data dumps, often ten years of unstructured bookings history, and reconcile them into usable cuts: bookings versus backlog, pipeline versus live, segmented by product, customer size, and sales cycle. Only once the data was clean could he begin answering buyer questions from it.

Managing the Buyer-Client Gap

Buyer questions arrived in batches, sometimes five sub-questions inside a single ask. Clients were not always willing to answer everything. Tushar learned to navigate the gap: surface what the client would share, satisfy the buyer's immediate hunger, and sequence the harder disclosures for later in the process. When a client was being evasive on sensitive data, he would offer a partial answer, sometimes removing employee-level detail or sharing data for a single customer segment the buyer already knew, to keep the process moving without forcing the client's hand.

The harder moment came when a reconciliation error surfaced during a buyer call. Rather than admitting the mistake on the call, Tushar identified the source of the error, fixed it, and sent the buyer a reconciliation map alongside an updated model. The buyer worked through the map themselves, and in doing so revealed how they were actually thinking about the numbers, specifically how they were using backlog data to underwrite forward revenue. That insight shaped how Tushar framed subsequent model outputs.

Over time, he developed the ability to anticipate buyer questions before they were asked, reading patterns from prior due diligence rounds and flagging inconsistencies in growth assumptions, such as a revenue stream jumping from four percent to eight percent growth without an acquisition or macro tailwind to explain it.