The previous blog post covered a number of KPIs entrepreneurs should be tracking. The following represent some additional ones.
Customer acquisition costs (CAC)
The customer acquisition cost is simply how much it costs to acquire a customer. There can be a couple of variations here, as some calculate costs purely on the straight marketing cost, whereas others include attributed overhead as well, e.g. total cost of sales and marketing, number of deals signed. With the latter you are essentially looking at all of the sales and marketing costs (including attributable salaries in a particular period and dividing it by the number of new customers signed up in that period).
One simple rule of thumb here relates to the level of human involvement in the process. If your product is easy to use and understand, it is possible that customers can self-serve where the sales process is essentially automated. The user clicks on a PPC advert for example, looks at a demo video, and signs up. This light touch approach keeps the CAC down.
On the other hand, if the solution is complex and you need a sales team to identify prospects who you then meet with to explain the features leading to conversions, the CAC will be significant and hence the lifetime value will need to reflect this. One thing is clear however: SaaS startups need to get a very good handle on the cost dynamic between their acquisition costs and life time value to ensure the business model is commercially viable.
How do you lower your CAC?
There are a number of ways to address this, including:
1. Product design. if the service is one that the user can understand and self serve the cost is dramatically lower than one requiring human intervention.
2. Improve conversions through the sales funnel. If you can improve your e-commerce conversion rate you can reduce the acquisition cost commensurately.
3. Ensuring marketing spend is tightly managed. If you can get the CAC down (and design a low-touch product) you may be able to produce a product with appeal to the broader (and sizeable) small-to-medium business market who have traditionally not been the types of customers accessing such solutions.This represents a huge opportunity.
Life time value (LTV)
The life time value of the customer is simply the revenue from the service times the number of months they continue paying. The goal is to ensure a LTV far in excess of the CAC (rule of thumb recommends a 3X ratio). The two drivers of LTV are pricing and retention, with the latter representing the primary focus for many SaaS teams.
The primary goals of a SaaS CEO is to drive customer numbers and LTV while reducing churn and customer acquisition cost.
Monthly recurring revenue (MRR)
This is one of the most important numbers to watch. It simply reflects the number of paying customers by the average paid per month.
Recurring revenue business models focus on a series of small, ongoing, subscription-based transactions. Users can consume a service or product as much as they like in an on-demand manner, as opposed to having to buy the entire offering up-front.
You’ll want the number of customers using the service to grow month over month. Hence you’ll need to ensure the numbers of new sign ups exceeds those leaving the service.
Net Promoter Score (NPS)
Word of mouth is an important source of referrals and the NPS is a simple concept which measures how much value your users are gaining from the application. There are essentially three buckets and the goal is to move as many as possible into the last bucket. The ratings are based on responses to the following question: How likely are you to recommend the service?
1. Detractors: 0-6
2. Passive: 7-8
3. Promotors: 9-10
Finally, one additional concept to be aware of is cohort analysis. A cohort is group of people who share a common characteristic or experience within a defined period. Cohort analysis is essentially a longitudinal study, segmenting users into groups to facilitate better analysis. The most common application would entail grouping users by months so you can compare and contrast the performance of, say, the January cohort against the March one. Given product enhancements can occur monthly, cohort analysis helps you understand whether product changes have affected behavior. One would hope the March cohort would be better behaved than the January one!