This interview is with Nav Deol MBA, Advisor, Massachusetts Institute Of Technology / Westgate.
For Featured readers, could you introduce yourself and the lens you bring as an Investor in the Alternative Assets space, especially how fund's use analytics to shape Corporate Strategy across Private Equity and early stage companies?
My name is Nav Deol. I have attained my MBA from the Massachusetts Institute of Technology. My career has spanned extensively across finance and technology. My background includes helping deal teams and collaborating with founders, which affects my perspective on value creation beyond underwriting.
The lens I bring shows how financial institutions actually make decisions on a day-to-day basis. Analytics has become central; it is embedded in how opportunities are sourced, diligenced, and managed post-investment. In the private equity vertical, data is used to pressure-test assumptions around growth, margins, and exit scenarios. This usually goes significantly deeper than public market comparables.
Looking at the realm of early-stage investing, the application differs, but it is still very important. Less historical data exists for these companies, so the attention must shift to leading indicators like user behavior, retention cohorts, and unit economics. These signals are then used to guide company strategy and investment decisions. The leading investors don’t just evaluate these metrics; they actively help founders improve them.
This all ties into corporate strategy in that this data informs decision-making after capital deployment. Funds are increasingly hands-on, using analytics to guide pricing, customer acquisition, and expansion strategies within portfolio companies. The focus is less on static board oversight and more on continuous data-driven iteration.
All in all, the transition that has occurred is that analytics is no longer a support function; it has become a core driver of how investment theses are formed and executed.
How has your experience in Finance and Tech shaped the way you use analytics to guide executive decision making?
My time within the Finance & Technology sectors has influenced the way I utilize analytics in a fairly practical way.
In finance, one is trained to be cautious of downside risk and detail-oriented. Much of this stems from having an understanding of where things can break, pressure testing assumptions, contemplating the downside, and being crystal clear on what drives returns.
On the other hand, Tech feels different. One regularly works with incomplete information, but real-time data is constantly coming in, including growth patterns, user behavior, and retention. One must react quickly. Tech is more iterative, and decisions start to unfold as data rolls in.
Utilizing both of these experiences, I try to keep things simple at an executive level. I focus on the really important metrics: unit economics, growth efficiency, and retention. I use these to steer the discussion, which helps bypass noise and keeps decisions aligned with what drives the business.
How do you assess whether a companies strategic initiatives are on track to create measurable value, and which metrics do you prioritize in your analysis?
I focus on what drives value, usually growth quality, margins, and capital efficiency. I’ll then check if the plans are actually shifting those.
I like to focus on a few metrics like revenue quality, contribution margins, CAC vs. LTV, and retention. These demonstrate fairly quickly whether the strategy is effective or just adding spend.
If these metrics are improving consistently, things are on track. If not, the strategy is usually underperforming.
In early stage or newly acquired portfolio companies, which key metrics and analytics do you focus on in the first 60-90 days to identify growth opportunities and assess technology potential?
In the first 60–90 days, I concentrate on what is actually driving the business. From the revenue side, I look at unit economics like CAC vs. LTV, margins, and retention. This is to see if growth is real and not just being bought.
On the product/tech side, I track engagement, usage, and drop-offs to spot friction or opportunities. I also make sure to check scalability: how much growth can scale versus relying on manual effort.
If retention, unit economics, and usage are solid, there is a foundation that can be built on; if not, that is where the focus goes first.
When evaluating AI capabilities for a company, how do you decide whether to build in-house, buy off-the-shelf, or partner with a third party?
First off, I look at the strategic impact and core competency. If AI plays a central role in the company’s value, I lean toward building in-house. If AI is a supporting function, off-the-shelf solutions or partnerships usually make more sense to me.
I also take into account speed, cost, and talent. Building in-house is lengthier and involves more specialized skill sets; on the other hand, buying or partnering can speed up deployment. Ultimately, the final decision balances control, differentiation, and execution risk.
In early stage or alternative asset investments, how do you identify which technologies or business models have the highest potential for value creation?
I focus my attention on where a technology or model can truly move the needle on value instead of just being trendy. Some of the early signs I look for include clear advances in:
- Revenue efficiency
- Retention
- Margins
I also look for evidence that the solution can scale without continuous hands-on effort. Additionally, I weigh how defensible or unique the approach is—something that competitors can’t easily replicate because it has a strong moat. This usually leads to a higher potential for long-term value creation.
From your perspective as a Tech expert, which AI technologies or applications do you see driving the most value over the next 3 years?
Over the next three years, the most value will be derived from AI that directly improves business results. This will include tools for dynamic pricing, demand forecasting, and customer retention. These will make revenue and margins more predictable.
Value will also be derived from AI that accelerates product development. This will be achieved through code generation, automated testing, and workflow optimization.
Finally, value will be extracted from AI that helps operators and investors make faster, data-driven decisions by combining structured and unstructured data, which will help create a measurable impact.