Discover more from Maverick
Cracking the code
Unpacking the value of sales data by customer, period, and product
Warren Buffett once humorously observed, "Business schools reward difficult complex behavior more than simple behavior, but simple behavior is more effective."
Drawing from my own MBA experience, I can attest to the allure of complexity! However, over time, I've come to truly appreciate the wisdom underpinning Buffett's humor. Today, in line with Mr. Buffett's advice, we'll be embracing simplicity.
Building upon our previous post on qualifying conversations with business owners, we'll focus today on a straightforward yet immensely powerful aspect: sales data, broken down by customer, by period, and by product. While this topic might not be the centerpiece of an MBA curriculum, it's certain to provide valuable dividends on your search fund journey.
In today's rapidly evolving economic climate, accentuated by rising interest rates, developing a robust framework for evaluating companies is not a mere advantage; it's a necessity. When appropriately dissected and comprehended, granular sales data unveils crucial insights into a business's health, prospects, and potential—it's akin to discovering a gold vein in an information mine.
Remember, a business cannot sustain itself in the long run without revenue (sorry VCs). Indeed, one of the first entrepreneurship lessons I've learned over the years is the imperative of securing paying customers to validate your business idea—the sooner, the better. Therefore, that’s where we'll anchor our discussion today, beginning and ending with the essential element—revenue.
Just as an aside before we move forward, for those who wish to go beyond these informational posts, consider becoming a part of Maverick's paid membership community. Our exclusive content offers you more than just insights. It gives you an inside look into real-world search examples, along with a myriad of practical resources to help you navigate your search fund journey with greater ease and effectiveness.
Ensuring Data Quality
Let's start with the accuracy of data. As we often hear in the realm of data science, "garbage in, garbage out." Simply put, if the input is flawed, so will be the output. Hence, before performing any analysis, cross-check the data for mismatches, missing values, or incorrect entries. Any discrepancies can distort your interpretation and lead to faulty decisions. Remember, if this is the only data you request from a business owner, you might have to initially trust its accuracy, reserving a more detailed verification for later. Usually I am able to compare with the company’s income statement to see if the two tie.
The Power of Three: Customer, Period, Product
This dataset provides a view of performance trends through time, product, and customer. These fields are not only pivotal for understanding recurring revenue models like in SaaS companies but also in other types of businesses with repeat customer relationships. Even in instances where customer identities aren't revealed, data in this structure can yield critical insights into the business's operational rhythm.
When you make the request for this data, aim to align the time periods with the duration of a customer contract. For SaaS companies, this is usually monthly or annually depending on how they offer solutions to customers.
As a primer, here's an illustration of what you might initially encounter in such a dataset for a company that sells annual subscriptions.
What to Look For
With this dataset, you can delve into a myriad of analyses, each offering its own unique lens through which to view the business. Here are a few techniques that can help you understand different facets of the company:
This analysis allows you to assess customer behavior over time, understanding the retention and churn rates within distinct groups or 'cohorts'. This can be especially insightful if the business operates on a subscription model, or has recurring revenue. You might find that newer customers behave differently than older ones, or that certain cohorts exhibit patterns worth investigating further. For example, if a particular cohort's retention rate suddenly drops, it may signal an issue with the product or service that needs addressing.
Time Series Analysis
By conducting a time series analysis, you can track sales revenue trends over various periods. This can be crucial in identifying seasonality effects, growth rates, and potential cyclicality in the business. It's also a great way to assess the company's performance consistency, and whether there are any sudden shifts that might suggest an external event or internal change impacting sales. Did I forget to mention customer concentration?
This form of analysis focuses on identifying the best-selling and under-performing products or services. By understanding which products are driving revenue and which aren't, you can begin to formulate questions about why this might be. Is it related to the market demand, the product's quality, or perhaps its price? This insight can also guide discussions on potential product line expansion or contraction.
The strength of these analyses is their collective power. They are not standalone techniques but interlinked components, providing a comprehensive and in-depth view of the business when used together. Stay tuned for future posts, where we'll delve deeper into some of these analytical techniques.
Creating Insightful Questions
In gaining a comprehensive understanding of this dataset, we're not just arming ourselves with facts and figures — we're opening doors to more informed discussions with the business owner. It brings me back to a particular instance where, during my own search, I discovered a significant customer concentration in the sales data of a potential acquisition. One customer was contributing an overwhelming 40% of the total revenue.
This kind of concentration can be a red flag as it signals high dependency on a single customer, but it also provided a fantastic starting point for our next conversation. I was able to delve into the nature of their relationship with this key customer, asking questions like:
'How have you cultivated and maintained this relationship over time?'
'What contractual protections exist to ensure continued business with this customer?'
'What measures are in place to mitigate the risk should we lose this customer?'
This line of inquiry not only gave me a clearer understanding of the risks involved but also offered the business owner an opportunity to articulate their customer relationship strategies. Additionally, it triggered a brainstorming session on how we could diversify the customer base and reduce this concentration risk.
The depth of our discussions could easily branch out further, exploring why certain products are outperforming others, strategizing to combat customer churn, or even delving into the potential for product line expansion. The key takeaway is that this dataset, when scrutinized effectively, is more than just numbers on a page. It becomes a map, guiding you through the intricate landscape of the business, helping you identify areas of risk, opportunity, and growth.
Valuation and Business Health
The sales by customer, by period, and by product dataset is a pivotal element when determining a company's enterprise value, which represents the total worth of the company, including its equity value and net debt (debt minus cash). Although it does not provide a full view of the company’s financial health (i.e., think of a situation where a business has very strong revenue quality but operates at a significant loss), here's how this data can provide insights from the demand side:
Consistent, robust revenue growth can increase the company's enterprise value, as it reflects the potential for future cash flow increases. However, erratic revenue trends or cyclical sales might necessitate protective measures in the deal structure, such as earn-outs or a lower financial leverage ratio to account for uncertain future performance.
Customer Churn Rate
A high churn rate could indicate a potential risk to future cash flows. This instability might depress enterprise value, but it could also introduce opportunities for deal structuring adjustments. For instance, a portion of the purchase price could be placed in escrow or tied to reducing churn in the future. A consistently low churn rate tells the opposite story.
Understanding which products are driving revenue and which are underperforming informs potential for future growth or contraction. Strong product performance can bolster enterprise value, while weak performance could be a point of negotiation in the deal structure. For example, seller financing might be negotiated to allow time for underperforming product lines to be improved or phased out.
If a business has steady, recurring revenue, it can command a higher enterprise value due to the predictability and stability of its future cash flows. This stability can also influence the buyout structure. If recurring revenue is strong, lenders might be more comfortable with higher financial leverage. Conversely, if recurring revenue is weak or uncertain, the deal might require more equity or seller financing.
Procuring this dataset can occasionally pose a challenge, especially when the business's data management practices lack sophistication or if they're reliant on outdated software systems. Yet, in these instances, a measured persistence can be valuable. It's important to thoughtfully communicate with the business owner about the role this data plays in your decision-making process, reinforcing your commitment to maintaining data confidentiality. Remember, this step is not solely about gathering data; it's also about nurturing trust and building a foundation of mutual understanding.
Sales data by customer, period, and product offers a wealth of valuable insights. This information can guide you through the complexities of the acquisition process, helping you identify risks, opportunities, and areas of potential growth. Stay tuned for future posts where we'll delve deeper into each of these analytical techniques, providing step-by-step guides and revealing their key insights. Until then, remember that this data is more than just numbers—it's your map to navigating the intricate landscape of your search fund journey.