by Evangelos Simoudis, Ph.D., Senior Managing Director at Trident Capital
As a two-time entrepreneur and current venture investor, I have learned to work with a variety of data (incomplete, partially correct, rapidly changing and often conflicting) in the process of making business decisions. That was definitely not the case when I started out to build my first company. I didn’t always appreciate the importance of determining which data was most important, how to collect it, and how to compensate when I couldn’t have access to it.
The right data can give you valuable insight into how your business is performing, but first you need to figure out which indicators will help you measure that performance. This process is called instrumenting your business, and, for entrepreneurs, it’s as important as building product and establishing the right business model for their startup. For data to be effective, startup managers must not only collect it, but also be able to analyze it and derive action-generating insights that impact the business.
In order to properly instrument their startup, entrepreneurs must first identify the right Key Performance Indicators (KPIs) that drive the business and then determine the data that is necessary to derive each of these KPIs.
For example, I invest in companies that develop SaaS applications, and a KPI for such companies is the Customer Acquisition Cost, or CAC. CAC data can be collected from every sales interaction between the company and a prospective customer, and this information helps SaaS companies determine if they are getting a good ROI on their sales efforts.
As these companies oftentimes have both field sales and inside sales teams, and the interaction with a prospect starts with the inside sales team and then migrates to the field sales team, data must be collected from each team. Moreover, since many companies employ pre-sales engineers to help a prospect better appreciate the solution they are offering, the interactions between each such engineer and a prospect must be taken into account in calculating the CAC. In complex processes such as this, collect detailed data where possible to ensure greater accuracy and because you don’t yet know how the resulting data may be used in future KPIs or further analyses.
In fact, it is not always necessary to establish the most precise calculation of a KPI from the beginning, since KPIs will most likely need to be refined over time as the business and its model mature. Therefore, it is more important to start capturing correct data quickly, even if the data is initially high level, as opposed to waiting until the business is perfectly instrumented.
Taking Your Data to the Next Level
Collecting the right data is only the first step. Learning to analyze the collected data is just as important because it leads to decisions, and it helps with the future instrumentation of the business. You may need to perform a few types of analyses to secure the information you need. If you run into an issue where it’s not possible to determine the information you need from one of your KPIs, then it may be necessary to change the instrumentation and start capturing more detailed or more relevant data.
Continuing with the CAC example: From this KPI, I perform a couple of different analyses. First, I determine whether it is increasing or decreasing over time, and whether it varies by type of customer. Second, I establish its relation to the Annual Contract Value (ACV). For example, is it less than, equal to, or greater than the ACV? If it is not possible to determine whether the CAC differs by customer type, then it may be necessary to change the instrumentation and start capturing more detailed sales data.
Some analyses may be simple comparisons, like the one I described above. Others may be more sophisticated, such as sales forecasting. When the right data is captured over time, it may be possible to create a predictive model that calculates the probability that a prospect will sign up as a customer within a specific time frame.
To complete the job, one must be able to derive the most impactful insights from the completed analyses to drive the necessary actions on the business. For example, when an analysis shows that an area of the business is underperforming, a CEO will have to use this information to make educated decisions on what steps to take to address the issue.
What if the analysis of the SaaS company sales process data shows that the CAC is greater than the ACV? Then the CEO has a decision to make. Should he take steps to reduce the CAC by implementing a self-service sales process for specific customer segments, or should he continue with the same sales process, believing that the CAC will be reduced as the sales team becomes more efficient over time? Sometimes such insights and their associated actions may be based on experience — the entrepreneur’s or the board’s — and other times they may require additional testing, which in itself may require additional instrumentation.
Inexperienced entrepreneurs frequently overlook properly instrumenting their businesses. Remember that business instrumentation doesn’t automatically imply collecting a lot of data, but collecting the right data, and understanding how it will help in the various tactical and strategic business decisions you’ll have to make on a daily basis.
Evangelos Simoudis, Ph.D. is a Senior Managing Director at Trident Capital, where he focuses on investments in Internet and software businesses. Find him at blog.tridentcap.com and on Twitter: @esimoudis.