Quality over quantity
Nearly everything in modern business is measurable, but often companies are relying on legacy metrics that can obscure the truth about what is happening in the business. For example, measuring customer acquisition cost (CAC) without considering the time-to-payback may cause marketing to over or under spend to acquire customers. Optimizing for renewal rate without understanding usage and adoption metrics can lead to account managers or renewal teams to be limited to discounts to retain customers. Traditional marketing and sales conversion metrics track quantity, but by adding quality metrics to track engagement and value over time companies can level-up sales, marketing and service team performance and processes.
Monthly Active Users
A key subscription business metric to determine the health of your installed customer base is the "active user" metric, which for most will be Monthly Active Users (MAUs). This gives the company a much better understanding of who deserves attention, but this metric also allows you to cluster/segment customers by usage levels for better marketing/sales engagements.
At minimum, your existing customers should be segmented into three major groups - power users, average users, and at risk users - based on active usage data. For example, users who haven't logged in over 2-3 months are probably at risk, while customers who use the product/service daily are your power users. This makes sense if you are a SaaS software business, but there are plenty of other businesses that can benefit from looking at active users instead of treating all paying customers the same.
Time-to-value can be a difficult concept to grasp, let alone measure.
- What does value mean?
- How do we validate this value?
- Isn't the value different for each customer?
Most businesses understand the value they provide a customer. Luckily, we live in a post-"Big Data" world so capturing data should be the easy part. If you have access to a data scientist, give them access to your customer data and tell them you are trying identify moments of truth early in the customer lifecycle that are correlated with long-term customer success, and then validate that with observation and/or interviews with customers. Having personally seen a number of different software products, sometimes the answer is a single event ("Completed a meeting") or a number/count of events ("Completed 4th meeting") or the combination of an event and a timeframe ("3 meetings in 2 days.")
One of the best benefits of learning about key success events early in the customer lifecycle is that you can use engagement tools to test changes to the time-to-value metrics. There are two primary goals for understanding time-to-value:
- Understanding the features/capabilities that correlate with long-term success helps you better understand why people use your product/service
- Implementing tactics to help new customers capture more value sooner from a product/service, thus reducing the likelihood to churn later
With a litany of marketing automation options available for email, mobile, social and more, it has never been easier to engage users directly and change behavior.
Customer Lifetime Value
Depending on your industry, customer lifetime value may be represented as CLV or LTV. There are essentially two types of LTV metrics, although the actual calculations will vary dramatically:
- Deterministic, which measures the net-present value of a customer based on dollars spent or contract value
- Predictive, which uses an algorithmic method of calculating the future lifetime value
From an analytical perspective, they both have value, but deterministic CLV is great for segmenting for retention and loyalty, while predictive is great for personalizing the marketing experience.
Companies have become much more mature about using CLV as a lighthouse metric, finding ways to tie everything in the customer journey back to this KPI. With a fully integrated CRM stack, all marketing, sales and support activities have the potential to be connected with CLV to measure their impact. The key to this is CRM integration, since to accurately achieve success measuring and improving CLV requires a single source of truth for the whole organization. This can be done a number of different ways such as via CRM or company BI platform, but any method should enable you to determine the success of any moment of the customer lifecycle on CLV to test activities that improve the outcome.
These metrics can change a business, driving transformation from a siloed organization with each team focused on a slice of the business and a set of metrics unique to them, to an organization aligned around delivering value to customers throughout the entire lifecycle. Innovations around data, analytics, and AI have the potential to transform every business, provided they are all trained on the right metrics. Understanding the time it takes for a new customer to derive value, knowing how many of your customers are active at any given moment, and optimizing customer engagement over time based on the ability to improve the lifetime value of a customer not only create a more customer centric business, but help optimize the business on real value metrics rather than being potentially misled by vanity metrics.