Startups with imminent or long term ambitions to engage with PE investors need to understand how the culture is changing within these firms and how this has redefined the metrics that are used to assess the value of startups. From there, they can adjust their strategy to ensure they are in the best possible position when they decide to seek investment.
So what are the practical implications of these changes? In a nutshell, investors are now much more interested in the ‘how and why’ behind the numbers. Traditionally, a company’s performance came down to a more basic level of financial and commercial diligence – historical profit margins, revenue and customer analysis based on aggregated data in conjunction with relatively high level market studies and management forecasts. These figures were then supplemented by the ‘strong hunch’ - the importance of relationships, experience and a certain ‘gut’ feeling an investor may have about any given company. Now, challenging economic conditions alongside the accessibility of advanced data analytics tools and AI, mean that investors are going much deeper in their analysis.
All the above is still important, but practically speaking, investors want the data to tell them a clear and detailed story about how and why a company has performed, and crucially, its potential. This means having a clear picture of traditional metrics, as well as a myriad of other information. Everything from customer demographics, purchase history, browsing behaviour, inventory levels, through to pricing data, customer feedback, and supply chain metrics could be relevant. This information then needs to be synthesised into a compelling narrative. It’s not enough to simply collect and present your business’s data, it needs to answer important questions.
- What does your customer behaviour tell you when it comes to purchase patterns or lifetime value?
- How can you leverage insights into inventory management, pricing and customer services?
- What was the ROI of your marketing and where is the capacity to grow?
- How productive is your team?
- What is the average time for products to go to market?
And even more fundamentally, why do your customers buy your product? The list could go on and on, but getting into a position where you can ask and answer these questions requires a sound data strategy.
Every good data strategy starts with the questions you want to answer - never the data. From there you can work backwards to determine the data that you need to collect and analyse. As we have seen there are a lot of questions you can ask about a company’s performance. Your job is to have a firm grasp of what your startup’s real strength and USP actually are to frame these questions. It’s important not to simply rely on your preconceived notions of what makes your startup great. What you’re personally proud of might not actually be the driving force behind your growth, nor could it be the main factor that makes your business attractive to investors. To start, you can do a ‘pre-mortem’, ask your entire team what they believe makes your company work. Different departments may have different answers that are worth examining. It could be the diversity of your customer base – both geographically and by sector. It could be the strength of your recurring revenue figures. Perhaps it’s the longevity of your products or the exponential growth of a new service you have launched. It may even be the approach you have to customer service and marketing and how that links to customer retention and growth.
When you have a clear picture of where your real strength and USP exists, the next step is to develop the data collection, management and analysis systems and policies that will prove what you know to investors. The earlier you start on this journey the better. It is much easier to implement and then scale a data management system and its corresponding resources than it is to retrofit a solution onto an already mature tech stack and business. Remember, the data insight you would give to investors are also incredibly important to the ongoing health and success of your business. It’s a win-win to extract this value early in your company’s journey.
The benefit of this approach isn’t just confined to proving your company’s worth in its initial pitch. Investors are increasingly using near real time data analysis to monitor the performance of their portfolios. If you have your data in order, it is much easier for investors to undertake this type of ongoing analysis. Consequently, it can be more attractive to them knowing that their potential acquisition or investment can easily plug into their existing systems. In addition, for buy and build firms that prioritise the compatibility and synergy of their acquisitions, deep data insights are very attractive. Most importantly, it will make it easier for you as leaders to run the business day-to-day, know where to spend your time and where to place your bigger bets.
Devising the strategy and building the systems is just one half of the equation. To derive maximum value and ensure your success, you also need to execute cultural and organisational changes — such as upskilling your team so they have the skills and knowledge to use data effectively. This is not about learning to code, but having an understanding of the fundamentals of data to be able to use it effectively for their role. This should include everyone, including all senior teams. Even today, it still surprises me how few business leaders are able to understand and interpret their core business data, instead relying on a handful of experts. By building up your own expertise now, you and your senior team will be well-positioned to present your company’s data-led narrative with confidence when the crucial investment or exit pitch comes around, putting you in a much better position for the valuation you deserve.
Natalie Cramp is a Partner at commercial data business JMAN Group,
