One of the biggest ongoing obstacles for SaaS companies is customer churn. If the cost of customer acquisition is high, retaining your current customers is always better than going through the work and expense of earning new ones. High churn rates can have an especially significant impact on revenue.
One of the best methods for combating churn is to take a proactive approach by leveraging usage data signals. Historical data analysis can help your team decipher early warning signs around customers who are about to churn and put a data-driven plan in place to avoid it.
Part of this plan can include usage-based billing which incentivizes customers to engage with products exactly on their terms. This approach allows customers to feel they’re getting their money’s worth from your software, and the real-time data it generates allows your team to respond quickly to churn signals.
In this post, we’ll break down everything you need to know about converting software usage analytics into SaaS revenue growth, including how to implement usage-based billing. Don’t just collect data and funnel it into a silo where it will never see the light of day– put it in the hands of your customer-facing team members who can take action with it.
Software usage analytics can provide a wealth of knowledge not only for product and engineering teams but also for sales and customer success teams who can act on that data in powerful ways. Seeing exactly how customers not only move through a trial, but interact with products and features throughout their lifecycle empowers teams to make data-driven decisions that directly impact customer satisfaction, inform product roadmaps, reduce churn, and ultimately increase revenue.
Usage analytics include but aren't limited to:
Usage analytics allow teams to build out more informed customer segments based on anything from demographics or usage behavior to other relevant areas, allowing for more targeted, personalized communications throughout the buyer’s journey and customer lifecycle.
With enough usage data, teams can work to predict when and if customers are likely to renew based on their product usage patterns and other behaviors. Customer success teams can use all of this data to build personalized customer training, marketing teams can build out customized content for sales teams to use during trial periods and beyond, and product and engineering teams can prioritize features on roadmaps accordingly.
Software usage analytics also allow teams to evaluate their pricing model and decide if usage-based pricing makes sense for an organization's pricing options. First, here's how to capture that data.
Set your teams up for success in gathering the right data, processing it, and then making it available in easily accessible, actionable formats tailored to each team. The right tools and processes for your teams will depend on the specifics of your organization and industry, but there are certain best practices everyone can tap into.
Depending on the complexity of your product offerings and available features, your team might have a little or a lot to track. Either way, teams should consider tracking some mix of the following when it comes to software usage analytics:
Engagement metrics are the most valuable; things like click-through rates and button clicks, time spent with specific features, and how frequently users log in and use the product or specific features overall. These help teams assess how involved customers are with your products. If they aren’t interacting with certain products or features at all, it’s up to the team to find out if that’s because they aren’t necessary for what the customer is doing or because they’re too difficult to understand.
Teams should also track related metrics that are outside of direct interaction with the product and its features, but part of their overall customer journey. A bad product experience can have a huge impact on any of the following metrics:
No matter what you’re tracking, be sure you’re prioritizing data accuracy alongside security and compliance.
Your team will need to evaluate your current tech stack and decide on the system that makes the most sense for capturing the data that’s most important to you and your organizational goals. Ideally, embedded software usage analytics will be integrated with your product so tracking, monitoring, and analyzing key data is as streamlined as possible.
Data collection can be done in real time for customized, flexible, actionable insights once it’s analyzed. It’s easier to track unique aspects of your products and features, building out reporting that caters to your organization’s specific business needs. Real-time data also flags performance issues and changes in user behavior faster, allowing teams to respond faster to issues. That should increase customer satisfaction and reduce churn— two things that increase revenue.
No matter what system you decide to implement, data security and compliance should be top priorities for your team. Embedded software usage analytics have a lot of advantages- flexibility, customization, and more- but there’s an increased need for security and compliance around sensitive customer data.
Teams need to ensure they comply with data protection regulations like GDPR and CCPA by employing standard best practices around secure data transmission, obtaining user permission, and anonymizing data where needed.
It’s helpful for teams to decide what their ideal usage metrics and benchmarks are as a different kind of ideal customer profile. How often would an ideal customer log in, what would their user journey map look like, and how would they move through products and features?
Having this ideal in mind can help teams design trials and onboarding that help facilitate this ideal experience– and as friction and drop-off points are discovered, teams can work collaboratively to provide customers with the product knowledge they need to succeed.
Teams can also build user personas, segmenting them out by level of engagement. New users might be quick studies who become power users quickly, for example, so it might not make sense to group them with other new users. Long-time customers might be light users because there is only a specific feature they rely on for their job, or their interaction is more seasonal because of their industry.
Understanding these different customer use cases can help teams design different approaches to customer relationships throughout the customer lifecycle. Usage analytics are the first step to cultivating this deeper understanding and crafting customized trials, outreach cadences, and more.
Real-time usage analytics that are effectively processed, analyzed, and presented to the right teams in an actionable way enables those teams to do their best work. Customer-facing teams in particular can detect signals of churn risk and take action to mitigate it.
Some of those signals might include:
High numbers of error rates or other technical issues could cause some of these problems. If customer-facing teams know about those issues and proactively reach out to let customers know they’re being handled and won’t happen again in the future, that’s one way to provide reassurance and reduce churn.
A drop in communication between customers and customer-facing teams like sales and customer success can also be an indicator of potential churn. This is why cross-team collaboration is crucial; without real-time software usage analytics, customer-facing teams won’t have the information they need to take action and prevent churn before it's too late.
That has an impact on revenue that affects everyone.
As your team reviews your software usage analytics, consider how what you learn can be applied to your pricing model. One strategy for teams is to implement usage-based billing. Instead of just selling different tiers of access to your product and features or a certain number of user seats, consider aligning your pricing to value from usage levels.
This strategy ensures customers feel like they’re getting their money’s worth, especially if their industry means they’ll be more seasonal users of your product. It reduces churn and incentivizes engagement and even product and feature overuse during the trial period– after all they want to know how they can maximize their value once they’re paying customers.
The key to success with this strategy is offering flexible pricing models to meet diverse customer needs.
Reducing customer churn to prevent revenue loss is one thing, but SaaS teams also want to expand revenue. Software usage analytics are key to building out revenue expansion pathways that give customers the best possible experience.
Flexibility in pricing is key to closing deals with SaaS customers. Usage-based billing is just one approach teams can take, and it doesn’t have to be the only option offered to customers either.
Consider offering customized packages as part of your pricing model, especially when it comes to enterprise customers or anyone willing to sign a longer-term contract. Your team can consider mixing and matching:
Consider how complex your product and features are to determine the mix of pricing dimensions that make sense for your company to offer.
Once you’ve determined which pricing models make sense for your organization, consider how different packaging approaches and promotions can help incentivize customers to upgrade. Once you have some software usage analytics to work with, empower sales and customer success teams with playbooks based on various usage personas.
Customized, personalized approaches focusing on different features that will help each customer’s specific goals are more likely to convert than using the exact same communications and cadence with every customer.
Real-time usage analytics can also help product teams deeply understand user journeys and match actual usage data to feature requests to determine what to prioritize. Customers will often say they want one thing when their behavior indicates they either don’t know how to access the features that could already help get a specific job done or they’re actually trying to solve something entirely different.
Access to rich, accurate, real-time data helps teams understand and clarify what it is users are doing inside of the product. Combining that with insights from customer-facing teams, in-depth customer interviews, and focus groups gives teams the information they need to build out comprehensive product roadmaps that will get users excited for what’s coming next– and eager to upgrade so they have access to it.
Allowing beta testing and limited trials of new products and features is also a good way to expand revenue when done with a data-driven approach built on software usage analytics.
Done right, software usage analytics can be successfully converted into SaaS revenue growth. Tracking the right metrics and building the right system to translate that data into actionable steps for customer-facing teams like sales results in decreased churn over time.
While churn is just one factor to contend with on the road to increased SaaS revenue, it's a big and important one. It's often less expensive to retain customers- and increase their lifetime value through upsells- than to acquire new ones.
Real-time customer usage analytics also show teams the pathways they can take to increase the expansion of revenue by enchanting customer understanding, further increasing customer lifetime value. The road to increased revenue is paved with data-driven decisions. Software usage analytics are an important part of that journey.
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