My first assignment out of law school was monitoring M&A deals.
I used a web tool that aggregated deal announcements and made them easily searchable: mergers, acquisitions, and similar types of corporate transactions. It allowed searching and filtering by various criteria, including the deal size, type, advisors, etc.
I scoured for large deals as part of a seemingly straightforward business strategy: a big deal gets announced, big deals mean lots of contracts to review, lots of contracts mean someone needs help reviewing them, so we call them and win the work.
On paper, it sounds like a reasonable approach. However, it has two main limitations.
Limitation one: by the time it’s news, the opportunity is gone
The first limitation is that by the time a deal has been announced, the advisors have already been selected and the diligence scope has been set (if not completed).
Related, the contract review approach has either been decided weeks or months before the press release went out or is an add-on to the more complex advisory workstream. Typically some combination of an internal team, outside counsel, and preferred vendors will be used.
So the deal announcements that show up in your alert feed or reports are disproportionately the ones you can no longer help on this deal. The key decisions were already made, during a highly confidential planning period.
As a result, reactive monitoring is a lagging indicator. It isn’t an effective origination strategy or lead source. The real opportunities come from being known before the deal exists: proactive relationships, so the company comes to you when the deal is still a secret. However, you can complement these proactive relationships with news monitoring, using announcements to check in on those relationships or perhaps build new ones.
Limitation two: volume doesn’t mean need
The second failure is subtler: it turns out that the premise that larger deal size means more contract work isn’t reliable.
Deal structure determines whether contract review is important and different deals are structured in different ways. A few examples that look similar but are fundamentally different in terms of contract work:
The acquihire. A handful of critical people command a large deal price: you can get to several hundred million, even half a billion, on a few names in the current AI talent market. The contract review necessary for these types of deal is minimal, often limited to things like employment agreements or restrictive covenants associated with those key employees.
The asset purchase. One entity buys a discrete set of assets, for example a fleet of trucks. There is often a small amount of contract diligence that is helpful on the buy side, mostly to validate things like ownership. However, because the assets are owned outright there is often limited counterparty outreach necessary. However, the seller may need to do more expansive review to transition or exit any related agreements (for example maintenance, support or vendor agreements to keep operating the assets).
Business-as-usual flying under the radar. The company enters into fifty thousand new agreements a year in the ordinary course of business. That’s a completely separate pool of potential contract work from anything M&A-related, and it never shows up in a deal feed because the vast majority of contracts are too small to require announcing publicly.
These scenarios illustrate that high deal volume is weakly correlated with contract-review demand, and in some cases the flashy transactions that dominate the news are often the least relevant for contract review work.
What actually predicts demand
The initial screen can still be helpful as a starting point. Big, diversified companies are likely serial transactors; their historical deal cadence tells you something. Working in an industry with more physical goods is a valid consideration as well: a business with a wider supply-chain footprint tends to have more contracts than a lighter software business (though very large software companies are exceptions to the rule). Company size, deal history, deal size, industry provide a decent starting point that’s reasonably simple to build.
To make this more valuable requires going one click down, however.
To do so requires layering in the things the deal aggregation tool can’t see: your relationship history, your sales pipeline and track record by type of deal and lead source, and nuances learned from industry experience such as the buy-side/sell-side asymmetry mentioned above. Feed that into a model and you’re asking 2 questions:
Who’s likely to transact in a way that actually generates contract work?
Where are we well-positioned to win it?
These two questions are more thoughtful and reliable than simply looking for the biggest deals.
Conclusion
As mentioned above, to generate the best insights requires internal context: your relationships, data, business history, experience and know-how. A third party tool can help you generate a first pass but it can’t replicate the part that makes the prediction more reliable, which is the proprietary context.
Proceed accordingly and be wary of anyone selling you something to the contrary.
If you’re working on origination or capacity planning around contract work and any of this maps to (or contradicts) what you’re seeing, I’d love to discuss.