After selling my ecommerce business at the end of 2022 I was at a crossroads with which direction to go. I enjoyed the process of developing a physical product and selling it online however I realised analytics was where my passion and skillset were more aligned.
Additionally, I was interested in dipping my toe into Angel investing with some of the capital I’d received. After a number of recommendations, I went through the Angel Investing School at the start of 2023 which led me down the rabbit hole of learning more about Venture Capital and eventually Private Equity.
Through attending events, pitching & working with clients for Dolphin Analytics I’ve condensed my learnings into 4 challenges being experienced by VC & PE funds:
How do I get a more diverse pipeline of startups? (VC)
A lot of startups are introduced to VC through warm introductions and improving your digital analytics alone isn't going to solve that. However, a significant proportion come through submissions through their website. More often than not these are from founders who have less access geographically or don't have the connections for warm introductions.
Smaller VC’s often don’t have an in-house analytics team to set up tracking and monitoring online submissions to understand which channels and campaigns are driving the highest quality and quantity of deals. By focusing efforts on the marketing channels which are attracting the best deals you can also increase your reach to founders with less access, where the real gems are found.
Non-technical founders should set up an analytics stack from the jump (VC)
Technical founders by nature are more accustomed to setting up KPI’s, tracking and automating reporting for those goals. In the case of non-technical founders, it’s more likely that they’re more driven by the day-to-day of scaling the start-up and setting up an analytics stack becomes more of an afterthought.
This causes 2 drawbacks:
Without a consolidated view of your marketing, app/website, sales and CRM data, startups can lack a direction and an understanding of what success and growth look like when scaling.
If this data is disorganised, confusing & the different platforms are not talking to each other, manual reporting processes for investors are inconsistent, inefficient, time-consuming, unscalable and inflexible.
Digital Due Diligence: Men lie, women lie, numbers don’t (VC & PE)
VC & PE funds spend a lot of time running financial due diligence before writing any cheques, but I’d argue that digital due diligence is just as important to ensure the juicy metrics claimed by these businesses are watertight.
Is the stated super low CAC cost coming from an ad group in a specific campaign or across their whole funnel?
Are the user numbers legitimate or are they just unique website visitors which are mainly bot traffic?
There was a famous case last year where JPMorgan Chase bought Frank, a buzzy fintech startup, for $175 million. Unfortunately, they’re now suing the founder for creating an enormous list of fake users (millions) to embellish its scale and success. Some digital due diligence would have stopped this in its tracks.
Brick & mortar PE acquisitions are just coming out of the 21st Century (PE)
Multi-location businesses lack Integrated Data Platforms to offer visibility into operations & performance of multi-location businesses. To fully digitise these businesses need:
Data extraction from different software solutions currently used by the businesses (Marketing, Product, Finance, HR, Sales, CRM etc)
Data storage in cloud data warehouses, with regular data refreshes and backups
Development of business intelligence dashboards built on top of the warehouse
Development of machine learning models leveraging the data warehouses to support business operations
We have a case study of a chain of dental clinics where we eliminated manual/error-prone process of accessing KPIs for each clinic location.
This saved 20 hours/week of effort for the CXO team (equivalent to 12.5% improvement in efficiency)
By tracking trends of dentists' efficiency who are on a variable compensation plan, the chain could further hit profitability and revenue targets and drive cross/upsell leading to 10-20% estimated uplift in revenue
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We live in a time where data is ever more important in all elements of business but never more so In VC and PE.
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