Get an Edge in Due Diligence through More Accurate Revenue Forecasting
October 8, 2024
In the high-stakes world of private equity, timing and accuracy are everything. Investors are under constant pressure to evaluate potential acquisitions quickly, while ensuring their projections – assuming the status quo and/or other scenarios that those investors may be considering – are accurate. One of the most critical aspects of this process is forecasting future revenues. We’ve all been there – executives at the target company provide revenue projections in the data room, and you’re left wondering how much to trust them. More often than not, you take them with a grain of salt because the target company is strongly incentivized to paint the rosiest picture they can to garner the highest possible valuation. So just how much salt should we sprinkle on these forecasts, and how can we do so in a principled, effective manner?
Whether you get these projections from the target or not, step number one is to come up with a reasonable projection of your own. This forecast is fundamental because it not only influences the projected return on investment (ROI) but also informs risk assessment and potential growth strategies. Corporate valuation methods spend a lot of time and energy on the problem of how to go from revenue projections to projections of profitability and cash flow, but spend remarkably little time on the more fundamental problem of what that revenue projection should be in the first place. Traditional forecasting methods, while commonly used, often fail to account for the underlying dynamics of customer behavior – leading to inaccuracies that can affect deal outcomes.
In our previous post, we discussed how a Customer Lifetime Value (CLV) analysis, powered by Theta’s most advanced and accurate CLV Ultra model, can provide private equity investors with a strategic edge in the bidding process by offering better insight into the health of the customer base. In this post, we explore how this model enables more accurate revenue forecasting and how it can serve as another game-changer for PE investors.
How Not to Forecast Revenue
Traditionally, investors have relied on top-down revenue forecasting methods. This approach typically involves analyzing historical growth rates, making assumptions about future performance, and extrapolating these figures over a set period, often five years. While this method offers simplicity, it often lacks depth and precision. It overlooks the intricate dynamics of the customer base – the very source of revenue – thereby missing critical insights into future performance.
Consider two companies with identical historical growth rates. On the surface, they might appear equally promising. However, if one company boasts a loyal customer base with high repeat purchase rates and the other relies heavily on continually acquiring new customers due to high churn, their futures could diverge significantly. Traditional forecasting methods would treat them similarly, potentially leading investors to make misguided decisions. Companies that cannot retain customers well burn through their total applicable market (TAM) a lot faster, depleting the pool of potential future customers more quickly. Companies that retain more of their customers have more durable revenues after they inevitably hit peak customer adoption, and retain more growth options on those customers because they have active relationships that can be leveraged for mutual benefit.
Customer-Based Revenue Forecasting Powered by CLV Ultra: A More Accurate and Insightful Approach
At Theta, we’ve successfully applied a more granular, bottom-up approach to revenue forecasting – one that focuses on customer behavior to offer a more accurate and strategic perspective. Customer-based revenue forecasting breaks future revenue into two key components: revenue from existing customers and revenue from future customers (and more generally, revenue from each and every cohort of customers that the company has acquired), while modeling the underlying behaviors of those customers that generate revenue – purchase frequency, and average order value. By predicting how many customers a company will acquire, how long they will stay with the company, how often they will make purchases, and how much they will spend, we can generate a more accurate forecast and evaluate different scenarios.
This approach provides investors with a forward-looking view that better reflects a company’s revenue potential. It has been featured in McKinsey’s seminal Valuation book, was extensively discussed in our recent webinar and successfully used in multiple projects when we helped our private equity clients make more informed investment decisions.
To implement this approach, we use a CLV model to forecast revenue from both existing and future customer cohorts, integrating these insights with predictions for future customer acquisitions. Our latest model, CLV Ultra, is designed to deliver highly accurate forecasts with speed. Its ability to identify trends in customer cohort behavior, accounting for seasonality and other factors impacting revenue dynamics, enables us to make more accurate predictions about how future cohorts will perform. By rigorously validating the model on historical data with a 95%+ accuracy, CLV Ultra ensures that forecasts are rooted in the real performance of customer cohorts. In addition to that, the model’s automation ensures that these insights are generated quickly, providing valuable forecasts in as little as a week – an essential advantage in the fast-paced environment of private equity due diligence.
Case Study: Customer-Based Revenue Forecasting for a Consumer Apparel Company
A private equity firm approached us to provide a five-year revenue forecast in addition to a complete CLV-based analysis for a target consumer apparel company.
We began by analyzing the company’s transaction log using a CLV model, which allowed us to project how long each existing customer would continue purchasing, how often they would buy, and how much they would spend on each transaction, resulting in a detailed cohort-level revenue projections for existing customers.
To forecast revenues from future customers, we used the company’s projected marketing budget and expected customer acquisition cost (CAC) to estimate how many new customers would be acquired each month over the forecast period. We then predicted how these new customer cohorts would behave by analyzing trends observed in previous cohorts. This allowed us to model various scenarios – such as how revenue might change if the company reduced its marketing efforts, if CAC increased faster than anticipated, or if the company raised prices by a certain percentage – and compare these projections to management forecasts.
The result? A set of five-year revenue forecasts that factored in customer retention, acquisition, and spending behavior – giving the PE firm a comprehensive understanding of how the company’s revenue might evolve under various scenarios. Armed with these insights, the firm was able to refine its investment strategy and move forward with greater confidence.
Driving Smarter Investments
The benefits of CLV Ultra go beyond accuracy and speed. By providing granular insights into customer behavior, this model enables PE firms to ask critical questions during due diligence, such as:
- What factors are driving revenue growth – repeat purchases from existing customers or new customer acquisition?
- How will changes in marketing spend impact future revenues – should the company consider putting the pedal to the metal on acquisition spend, or ratchet it back, at least in certain channels?
- What needs to happen for the company to meet its revenue and valuation targets?
These are the kinds of insights that allow investors to make smarter, more informed decisions. Consider question 2, for example. Let’s say that you knew that after acquisition, you are considering tapering back on how much the company is spending on customer acquisition. Understanding what may happen to future revenues is not going to be possible if top-down data is all that is available because you won’t know how much how revenue retention patterns differ for new customers versus existing customers, when tapering that marketing spend will by construction sharply tilt the composition of the customer base towards existing customers, driving a potentially significant deviation between historical revenue trends from what might have been expected given the historical data. Moreover, by integrating CLV Ultra’s customer-based forecasts, PE firms can better assess risks, avoid overpaying for deals, and identify high-potential opportunities.
With CLV Ultra, your revenue forecasts aren’t just educated guesses or a check-the-box exercise – they’re a key source of competitive advantage.
