Types of Customer Success Analytics

12 min read

A business without customers isn’t really a business at all. And a business without satisfied, loyal customers operates in a precarious position. When uncertainty around customer satisfaction or outcomes begins swirling, it puts company leaders and customer success teams in a tricky position. After all, how can you improve what you don’t fully understand—in this case, customer relationships?

In this blog, we’re taking a look at customer success metrics and analytics. How do you measure customer success? What metrics or key performance indicators (KPIs) should customer success managers be tracking, and how can you apply analytics to uncover deep insights into the customer journey (and how to improve it)?

Keep reading for an overview of what to measure and how to apply customer success analytics to turn raw data into actionable insights.

How To Measure Customer Success

Customer success attempts to measure how satisfied customers are, as well as how likely they are to become loyal customers over time. Here, it’s worth distinguishing between customer service and customer success:

  • Customer Service: Receives and responds to customer outreach—questions they have, issues or problems they encounter, and so on. Think product and technical support. Customer service is largely transactional, so a satisfied customer is one whose question or issue is resolved efficiently and effectively.

  • Customer Success: Takes a broader and more strategic role. Customer success teams work to better understand how customers are engaging with the product or service, creating satisfied customers by proactively understanding and addressing issues that might hinder retention efforts. Customer success works to optimize the entire customer journey—when they do their job well, they create loyal customers and prevent costly churn.

How do you quantify customer success? It starts with defining and tracking the right metrics.

What Are the Key Customer Success Metrics?

A few of the most common customer success metrics examples include the following:

  • Average Revenue Per User (ARPU): Calculated by dividing a period’s total revenue by the total number of users or subscribers over that period. This provides insight into the value of individual customer relationships—and of cultivating loyalty (in the form of renewals, for example).

  • Customer Churn Rate: Churn rate is the percentage of customers who have discontinued their relationship with your business—obviously, the lower the better. It can be calculated by simply dividing the number of customers you lost (over a given period, such as a month or quarter) by the total number of customers. This is a vital customer success KPI for SaaS companies, since converting a new customer can cost 5x as much as retaining an existing customer.

  • Customer Retention Cost (CRC): Customer retention is a direct measurement of customer loyalty, and CRC quantifies how much it costs to retain an existing customer. CRC is calculated by dividing the total amount spent by customer success to support retention efforts by the number of total customers. Some of the factors that might be relevant include engagement programs and resources, customer onboarding and training costs, marketing spend, and so on.

  • Net Promoter Score (NPS): NPS is widely considered one of the most important customer success metrics, and it’s a very simple measurement. Customers or subscribers are asked to answer one simple question, “How likely are you to recommend [brand] to a friend or colleague?” Customers answer from 0 (not at all likely) to 10 (extremely likely). Based on their response, they fall into one of three categories: promoters (9-10), passives (7-8), and detractors (0-6). The idea is to maximize promoters and minimize detractors. Especially when companies follow up with customer responses, they can gain timely insights to prevent further churn.

  • Net Revenue Retention (NRR): This metric quantifies changes in net revenue which is the #1 priority for company growth and valuation (learn more about it in our blogpost about the future of customer platforms). NRR can be a little tricky to calculate, but here’s a basic formula:

    1: Add together your monthly recurring revenue (MRR) and customer upgrades (dollar amount).

    2: Subtract the dollar value of any downgrades or customer churn.

    3: Divide that total by the original MRR figure, and multiply the resulting outcome by 100 (to result in a percentage).

Tracking these key customer success metrics provides a foundation for improving the customer journey, a sizable step toward mitigating churn and improving retention, cross- and up-sells, and the likelihood of gaining new customers through referrals. Once the right KPIs for customer success have been identified, leaders can create a customer success scorecard to assess individual interactions and outcomes or a customer success KPI dashboard to increase visibility and accountability team-wide.

What Is Customer Success Analytics?

The easiest way to differentiate between customer service metrics and analytics is to define the purpose of each. Customer success metrics are measurements of key performance indicators. Tracking specific metrics results in raw data, and customer success analytics refers to the process of deriving actionable insights from that data. Customer analytics involves evaluating the customer experience and journey—to better understand the “how” and “why” of improving key performance metrics.

What Are the Benefits of Customer Analytics?

Especially for SaaS companies, one of the primary goals of customer success managers (CSMs) is to improve customer retention and revenue growth. Through tracking customer success metrics and analytics, CSMs gain deep, actionable insights into the factors that cause some customers to remain loyal (retention) while others churn. Because customer success analytics examines and illuminates connections and correlations between different data types (basic KPIs vs. usage and behavior trends, for example), it can be tough to bring all the necessary data and insights together.

By digging deeper into customer behavior—how they engage with your product or service, for example, or the features they like/dislike—customer success (CS) teams can take tangible steps to better understand and meet their needs. They can identify risk signals while gleaning insight into what drives customers to renew subscriptions and refer colleagues, and so much more.

Planhat was designed for analytics-ready organizations who are looking to forge long-lasting customer relationships, optimize and enhance customer journey maps, improve workflows, and unite business teams as a well-oiled customer success machine.

What Analytics Are Important for Customer Success?

For CSMs who are looking to be more proactive in generating loyal customers and managing better relationships, there are a few different types of customer success analytics worth tracking. There are three main types of customer success analytics, each of which has its own metrics and applications: customer analytics, product analytics, and customer journey analytics.

Customer Analytics

Customer analytics are useful in guiding the direction of future product or service offerings, marketing materials and brand awareness, and even customer onboarding and support offerings. While the phrase “customer analytics” is broad, it’s easier to understand in terms of its three main components or types of analytics: descriptive, predictive, and prescriptive.

  • Descriptive: Analyzes historical customer behavior, which can then be interpreted to determine, essentially, what works and what doesn’t. By applying descriptive analytics, businesses can better understand and visualize trends and break them down based on specific product areas or features, seasons, and so on.

  • Predictive: Applies statistical modeling techniques to identify correlations between different variables and predict future trends and outcomes. The ability to collect and understand historical customer data makes predicting (and catering to) their future behavior much easier.

  • Prescriptive: Identifies the next, best actions to take with specific customers, depending on where they are in the customer journey and what their use-cases and desired outcomes are. Prescriptive analytics help customer success teams segment customers and get an idea of their likelihood of conversion or churn—and what can be done to intervene, in the case of churn.

Product Analytics

Just like the name suggests, product analytics is the process of learning about how customers are using the product or service. It’s important for customer success teams to not only understand what features customers are using, but to develop insights into the challenges or pain points they’re experiencing, features they don’t understand, capabilities they desire, and so on. By aggregating customer feedback, the product and related processes (onboarding, for example) can be optimized to more proactively meet customers’ needs.

Under the umbrella of product analytics, there are a few different types of analysis that can be applied:

  • Trends: Quantifies whether product feature adoption is increasing or decreasing, and can help leaders better understand the factors that drive use/non-use. For example, are there technical limitations or roadblocks customers experience? Are certain features outdated or of little use to customers? If the answer to either of these questions is “yes,” then business leaders and customer success teams can work to better-tailor product features to the actual needs of their customers.

  • Attribution: Digs into what makes successful customers successful. Learning about how loyal, happy customers use your product or services can help optimize how future customers are attracted, engaged, and onboarded. While attribution analysis mainly focuses on those factors that drive customer retention and loyalty, it can also be applied to fine-tune sales and marketing efforts, product design, and more. And while a number of factors ultimately impact customer retention and churn, attribution analysis helps leaders to prioritize factors with the most outsized impact.

  • Cohort: Adds a layer of segmentation to trends and attribution analysis. Especially relevant for companies that have been in business for a considerable time and have launched a number of product updates and developments, cohort analysis works to break product/feature usage down by different segments. It can help to identify areas where the product features have advanced, for example, and determine whether elements of the customer journey have been updated in kind. For example, if there have been major changes to significant features, have sales and marketing messages been updated? What about the customer onboarding process?

Customer Journey Analytics

When companies take the time to really understand the holistic customer experience across all touchpoints within the customer journey, they can take steps to optimize that journey and add value at each stage. For SaaS companies, the customer journey is often thought of as having three segments, each with multiple touchpoints: acquisition, activation, and adoption. By engaging with customers in the right ways at each stage, companies can improve loyalty, increase customer lifetime value (CLV), and drive revenue growth.

  • Acquisition: When a potential customer is in the acquisition stage, they’re exploring their options. They might engage with sales or marketing collateral or general website content, to get a sense of what you offer and what it can do for them.

  • Activation: Then, the activation stage involves new customer onboarding and training—the first real stages as a bonafide customer.

  • Adoption: Finally, the adoption stage is when the new customer gets to know the product’s ins and outs, creating processes and workflows to integrate with existing systems, and so on.

For many organizations, the concept of customer journey analytics can feel overwhelming. That’s because to really understand the customer journey requires a few different types of insight all at once. For example, pairing concrete product usage statistics with organic customer feedback provides a better understanding of how customers derive value from each journey stage than what you’ll get from either component on its own (without the other for added context).

What Are the Must-Have Features of a Customer Analytics Platform?

If you’re thinking this all sounds like a lot of data to track and analyze, we get it. In many cases, even industry-leading companies often find themselves having to cobble systems and data together to develop insights they can trust and base future decisions on.

At minimum, this often means some combination of Salesforce or another customer relationship management (CRM) system, Excel spreadsheets or other databases, and any number of organization-specific tools. It can also mean a great deal of time and effort, pulling data together, identifying correlations and trends, and considering potential solutions. And even though most CRMs and other systems are capable of integrations, there’s still a decent amount of work involved in getting those components to play nicely together.

Planhat Provides a Single Source of Truth You Can Act On

Planhat offers a better way forward, with an all-in-one customer success and data platform to collect all relevant customer and account data in one place—a 360-degree customer view, in other words. Planhat’s platform is also much more than this, offering a diverse feature set to power your customer success analytics, including:

  • Usage analytics and customer health score: Track and visualize how customers are using your product/service, benchmark usage trends, and alert team members when things change. Create your own customer health scoring methods, and experiment with variations based on customer segmentation or other factors.

  • Customer journey management and optimization: Optimize the customer onboarding process by creating customer playbooks to design clear, repeatable processes and clear milestones for each stage of the customer lifecycle.

  • Customer portals and centralized interactions: Keep customer conversations centralized with a customer success inbox and track product issues and feature requests throughout the customer lifecycle.

  • Management reporting and team performance: Build custom dashboards to track customer journeys, measure CSM efficiency, and keep everyone in the know. View team performance as a whole or break it down by individual team members.

  • Task management and automations: Make it easier for customer success teams to develop personalized and lasting relationships with customers with our task management system. Keep everyone in the loop on customer actions and communication, configure real-time alerts to ensure that nothing falls through the cracks, and set up automations to let team members know when they need to reach out.

When you review the list of features and functions above, keep in mind that Planhat is unique in that we’ve collected all of these (and more) in a single platform. Not only is it comprehensive, but it also makes accessing and understanding data easy, with data security you can count on. With Planhat’s innovative tools for CSMs, data isn’t just accurate and reliable, it leads to actionable insights.

Learn More About Planhat

We’ve really only scratched the surface of what all Planhat can do for your customer success team and organization as a whole. Learn more about our complete customer success toolkit and schedule a demo today!

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