4. October 2015 Helmut Poellinger

It´s all about data when predicting success?

calcSaaS startups and innovative companies acting like those oftentimes have one major goal: They aim at establishing drastically scalable businesses for rocket success.

The challenge for these startups is to acquire large number of customers fast and learn whether the business idea works in reality. This insight is costly and requires significant sales and marketing funding. Particularly marketing often seems a discipline everybody regards himself capable, few though truly own a “black belt”. A question to answer that is difficult in most cases is: What is your average Customer Acquisition Cost (CAC) and what is your Customer Lifetime Value (CLV)? Although the underlying math is not really challenging, the readiness to look into the topic requires some additional will from the managers. When it comes to startups – that by design do not have plenty of historic data – projections of CAC and CLV are somewhat guesswork, if the founders bother at all. So the solution is estimation. Vik Singh, CEO from Infer, describes in a profound few step approach on TechCrunch how he is using a rolling sales and marketing period to estimate both LTV and CAC.

In essence it is a multiple of 3 or more that represents a desired value. It means that dividing the estimated LTV (ELTV) by the average CAC (ACAC) should deliver a value of 3 or higher. Especially in SaaS models the monthly recurring revenue and its growth month on month speaks for itself. Keeping customers is constantly increasing the average LTV. Vik´s approach is real-time oriented by analyzing the current opportunities and starting from there. This allows dynamic consideration of recent marketing activities to constantly project and estimate an important health indicator for the company.

In order to implement Vik Singh`s approach startups need to be able to collect data from the very beginning and make them available for analysis. This requires Sales, Marketing, Finance and probably also Development to contribute into the dataset. It is not difficult and can be managed continuously with little effort. It delivers insights at real-time that will make the difference in how effective money is spent to reach the goal. And investors love teams that are aware of the relevant data of their business and manage it professionally.