Finding Deals by Tracking Deals: How A Data-Driven Approach Can Improve Deal Management

There was a time when the keys to effective deal management were the experience and instincts of the players involved in the deal. Operating by “gut feel” worked well—or, at least, it seemed to since there was no other method to compare it to. 

The tools for ingesting and analyzing data simply weren’t available to firms.

The Deal Management “Game” Has Changed

First, “game” is in quotes because it’s used facetiously here. Deal management is no game, especially since many millions of dollars (and sometimes billions of dollars) are at stake.

Today, successful firms have replaced gut feel with a data-driven approach to deal management. 

They’ve had to for a couple of reasons. First, savvy stakeholders are no longer willing to bet on someone else’s instincts—even if that someone is a highly regarded industry veteran. They want the firms they collaborate with to provide advice that’s based first and foremost on facts.

This isn’t to say that other, qualitative factors aren’t important. A portfolio company founder’s experience and personality, the company’s team chemistry, and other attributes can and should be part of the decision-making calculus. But stakeholders are, understandably, more focused on data today than ever before.

The second reason that Firm A has adopted a data-driven deal management strategy is that Firm B did so months ago. Fail to optimize your operations and you’re effectively handing deals to Firm B (or Firm C, or…). And they’ll gladly take them!

Deal Management and the New Post-Pandemic Reality

The COVID-19 pandemic was another driver behind the move to data-driven deal management. Being unable to meet in-person forced stakeholders to look for other criteria on which to base their decisions, and collecting and analysis of data weren’t slowed at all by the crisis.

In fact, private equity pros who were spending less time on planes and in boardrooms had significantly more time to amass and leverage information, so the flow of data actually accelerated during the pandemic. With firms and stakeholders the importance of easy access to high-quality data, the flow rate will only continue to increase.

Beyond “Big Data”: The Rise of AI, Machine Learning, and Other Tools

The recognition of the vital role that data and deal tracking can play in deal management has been followed by a movement to get more out of that data. Artificial intelligence (AI), machine learning, scoring, and other predictive analytics are enabling firms to anticipate trends and make even better investment decisions.

These additions to the data-driven approach to deal management are having a positive impact in many areas of firm operations, including:

  • Sourcing investments
  • Performing due diligence
  • Choosing targets
  • Monitoring investments
  • Assisting portfolio companies post investmentCreating value

And the sources of the data that supports these activities can be surprising. For example, with seed and early-stage companies that don’t have a track record that can be analyzed, some firms are building data models that look at things like social media signals to get a sense of the organization’s potential.

Those signals might previously have been dismissed as irrelevant. But with improvements in how data is collected and analyzed, they may prove to be very useful. Social media signals can also be predictive at the individual level—pointing to product managers, developers, and others who might be interested in starting their own company or who already are planning to do so.

Connecting with them while they’re actively and eagerly looking for strategic partners can open the door to a long and lucrative relationship in some cases. Years ago, these thought leaders and trendsetters would not even be on anyone’s radar until they already had established key relationships.

4 Steps for Developing Data-Driven Deal Capabilities

Data-driven deal management isn’t something that happens overnight. There are four critical steps firms have to take to get there:

  1. Prioritize the acquisition of high-quality data. You can’t succeed with just any information. You’ve got to obtain or develop clean, accurate data. Like they say, “Junk in, junk out.” So, in that sense, having little to no data is probably less damaging than bad data.
  2. Get the right systems and people in place. Outdated data management methods (think shared spreadsheets) have to go. You need the right, purpose-built technology to get value from your data. Similarly, you need team members who either have data management experience or are willing to develop it. It sounds harsh, but the reality is that people who insist that “the old approach to deal management is the best approach” are only going to inhibit your progress.
  3. Continue to leverage industry experience. Adopting a data-driven deal management approach doesn’t mean you should ignore the skills and insights of your team members. Data may be “driving” your approach, but you still need the experience of your people to help with navigation.
  4. Don’t settle for “good enough.” Technology evolves. Companies evolve. Markets evolve. Firms that have optimized their data-driven deal management processes today and then assume they can turn their attention to other initiatives inevitably find that those processes become outdated much faster than they expected. Then they’re playing catch-up. On the other hand, firms that keep a watchful eye on technology, their data sources, and their processes continue to be leaders in their area of expertise. 

Data-Driven Deal Management: A Permanent Shift in the Requirements for Success

The idea of “finding deals by tracking deals” isn’t a fad. It’s a solid strategy that’s here to stay. And the sooner you immerse yourself in data-driven deal management, the sooner you’ll differentiate your firm and move to the front of the pack.

A traditional crm was built for general ‘customer’ scenarios

Software platforms have made the world a better place by making work a better place. Indeed the world is better off when people enjoy their jobs even marginally more, and workplace applications on big CRM platforms like have done that and much more.

But the potential that platforms like these offer presents diminishing returns: once the platform provider has engineered too many industry specific components into its platform, its usefulness for other industries begins to be threatened, and with that so do the usefulness of the component tools built into the platform.

So it is with the CRM category that has defined: it is generic enough to work for many industries, and yet still offers the potential for others to round off the edges and nail more vertically-oriented and extremely tailored software solutions.

Private capital markets are actually a great demonstration of this dynamic. Where generic CRM platforms simplify — appropriately so — to assume there’s a business, a customer, a sale, and service of that customer, there are a few industry-specific pieces that are missing.

Take for example, that investors become customers by investing through legal entities the GP raises. It’s a subtle but important nuance that just doesn’t make sense at a platform-as-a-service level (because it’s overly complicated for a simple one-time sale that many industries require), but which can easily be added without 10 years or software engineering. Once provided, the rest of the platform’s components become tremendously powerful again and you’re set to take over the world.

As a traditional CRM in our pillars methodology, these nuances must be present to properly account for investors in these legal entities, potential target companies and which are owned by these entities, the context of all interactions with these parties (as well as the appropriate overlap, ie co-investments), and how you’re arriving at finding these opportunities on both sides of the equation, such that you’re able to piece together what’s effective and what’s not. Not just because we say so, but because these are the very relationships and data that are key to the motivation behind a CRM in any industry.

It’s critical, too, that the valuable publicly-available information that helps to enrich CRM systems and save users painful steps of entering it themselves is fully-integrated at the platform level.

Again, look no further than the 3,000+ pre-built integrations that — the creator of the CRM platform concept — has at a platform level to do so, and which only exists by way of holding just short of overly-specifying certain industry workflows that would present challenges to properly integrate.

Stakeholder reporting and communication (investor relations) draws on a range of datasets

The traditional “customer service” model of CRM systems once again makes overly-simplified assumptions about the customer relationship when applied to private capital markets.

In fifteen years I personally have yet to hear the terms “warranty” or “service call” in this market because it’s just not the same. But make no mistake, as uncomfortable as it may be to say aloud, customer service is more important now than ever and it’s constantly happening; the industry is, after all, considered to be a financial “service”.

As it turns out, that service is primarily information-based — it’s driven by data and takes the form of reports and analysis that drive decisions, and then end up again in investor-facing reports and analysis.

The foundational elements of a private capital markets CRM must be built such that they accommodate this data (like we discussed above), but so too that it can accommodate additional supporting data that investors (customers!) need in the context of service.

Oftentimes this supporting data — financial metrics and time-based values, for example — is believed not to meet the traditional definition of CRM and the natural thought is “well, better do this in Excel!”.

While I happen to believe Excel is still the greatest software application ever built, its introduction to this value chain we’ve discussed herein actually creates the problem many firms suffer from: key data needed to provide customer service (again: effectively the entirety of a firm’s reports and analysis) is now in disparate systems and detached.

Both of those dynamics are important and distinct: not only is this supplemental data disparate, but when brought together there is no logical association that can be made between the two data sets.

Allow me, then, to make the point very simply: not only can this financial and time-based value data (you may be thinking about is as “portfolio monitoring” or “accounting”) be a part of a CRM, it is arguably the most important part of a CRM because it’s at the core of what providing service to the customer entails — information that comes out of data!

Firms need a digital method to engage stakeholders (ie investor portals)

Investor portals are not new; in fact, for many of us — including myself — they conjure up horrifying nightmares in which we’re aimlessly guessing at folders to find the newest document we need.

So in lies the opportunity: not only have the portals we’ve come to hate not simplified the process of acquiring information, they’ve failed to create an entirely new experience that is “customer service” driven.

To be fair, this is not a B2C market where you’d be long out of business for not having focused on customer service and thus the customer’s technology-driven experience. But don’t expect to be around too much longer if you aren’t thinking about this shift.

Today’s institutional investors increasingly expect this same consumer-like experience, and a massive opportunity is being missed by not providing it. It’s not about providing them the experience they desire; it’s more about the ability to measure engagement that is had in return.

Put simply: what’s keeping the market from providing this experience is the availability of the information that’s required to create the service that provides the experience.

If you’ve hung in this long, you know that by focusing on your CRM, you have the data that’s required to manage the customer relationship and the technology-driven experience through which that information is shared to create a differentiated and opportunistic customer experience.

deal management