With competition for deals at an all-time high and hold periods continuing to compress, private equity fund managers are feeling heightened pressure. The average hold period in 2014 was six years; today, it’s less than five. Uncertainty related to evolving valuations and the interest rate environment require funds to have increased conviction that their value creation plans will generate the returns they expect. Timelines have accelerated for private equity-backed software company assets to produce results. Owners and operators of software companies have less time than ever to deploy revenue growth strategies to realize their investment thesis and secure desired returns from their investors.
As these timelines to realize value are shrinking, access to an increasingly important asset is growing. More data is being generated and collected than ever before about how and why customers buy, use, share, upgrade, or leave software products.
The key to accelerating revenue growth lies within that data.
Insights unlocked by analytics reveal revenue opportunities through identifying and targeting specific customer segments based on a combination of their value, firmographic/demographic attributes, and behaviors.
The good news? That information is being collected.
The bad news? It’s not being used nearly enough.
"Many fund managers are missing an opportunity to use their data to optimize revenue from an existing platform by identifying cross-selling and upselling opportunities," said Managing Partner Sean Adkins."I would challenge investors to consider this question: If you weren’t able to make another acquisition, how could you use data to optimize revenue? The data is there, as are the capabilities for analyzing it."
Most fund managers have a revenue growth playbook—and one that has served them well. But today’s fast-paced market requires an update. New technology and products aren’t always necessary. No existing platforms need to be massively disrupted.
What’s needed? Embedding data science and analytics into growth strategies—with an intent to deploy these advanced customer segmentation capabilities within the first 100 days.
A strategic, value-centric approach to customer segmentation is the precursor for data science and analytics to go to work. It entails breaking down customer segments that have distinct interactions with the product and value to the company to target revenue opportunities with precision.
All customers are not created equal, and all customers do not represent the same value to a company. Segmenting customers based on a combination of quantitative and qualitative dimensions enables software businesses to target, acquire, support, retain, and expand customer relationships more quickly and at lower cost. Data science and analytics allow companies to pinpoint and address the most important drivers of revenue growth (e.g., sales, velocity, margin growth, cross selling, LTV/CAC) and at the most important stages along the customer continuum: customer acquisition, retention, and expansion.
An important precursor for customer data to deliver returns is to have unified standard operating procedures for how data is collected and KPIs are determined. If critical business units along the customer journey (e.g. product, customer experience, sales, marketing/comms) all measure different metrics or calculate the same metric differently, there is no single source of truth about customers. When each department is relying on their own specialized view of the customer, knowledge becomes siloed, handoffs between departments weaken, and departments that need to be working in harmony to trace customer lifecycles fall victim to mismanagement and misalignment.
Without standardized metric definitions and calculations, the picture of who the customers are, where they are, and what they do becomes fuzzy. An organization operating in this mode can fall into the trap of treating all customers the same with, for instance, a single message or product offer. Or, an executive’s “gut” drives significant investment in a product upgrade that isn’t supported by any customer demand data.
We’ve provided examples in which value-centric customer segmentation, achieved through data science and analytics, had a significant impact at the three core inflection points along the customer journey.
Incorporating data science into an organization’s operating practice is imperative for revenue growth in 2022. It will increase your efficacy in identifying and pulling the right levers that generate revenue based on precise, value-centric customer segmentation. The pressurized, fast-paced environment of private equity—specifically the software sector—are only accelerating. In order to keep pace with revenue expectations, executing a customer data growth strategy is vital.
There could be billions of dollars lying dormant within your existing captured customer data. Instead, put that data to work through advanced segmentation to increase revenue at the pace the market demands.