How to use customer success analytics to improve retention

Using customer success analytics can be the difference between holding onto a major customer for years and losing them within the first few months.

What are the top customer success analytics your customer success team needs to track?

• 1. Churn Rate

• 2. Net Promoter Score

• 3. Customer Health Score

• 4. Monthly Recurring Revenue

• 5. Average Revenue Per User

• 6. Trial Conversion Rate

• 7. Customer Retention Rate

• 8. Average Days to Onboard

• 9. Product Usage

• 10. Average First Response

What is Customer Success?

Customer success is anticipating the questions or challenges your customers might have then offering answers and solutions proactively. This lets you enhance customer happiness and increase customer loyalty, boosting retention and revenue.

To make more informed customer success decisions, you can collect data from the various sources through which customers interact with your brand. Customer success software is often used to make this a seamless process. A few common types of data collected are as follows:

• Product usage - Features used, login frequency, size of plan

• Sales information - Internal notes on challenges and opportunities

• Support notes - Number of help-desk tickets and average time to resolution

• Customer feedback - Survey responses, CSAT data, product reviews

• Financial information - Contract length, renewal schedule, current payment

• CRM data - Life cycle stage, contact information, prior interactions

The software then converts this data into valuable insights that you can use to create better strategies for your sales, marketing, and product development efforts.

This enables you to offer more relevant experiences to your clients in all their interactions. Providing personalized experiences is what compels your customers to stay with your brand and become loyal clients.

Customer analytics help business-to-business software-as-a-service companies understand if they're meeting—or working toward—key growth metrics and milestones and continuing to grow and gain market share.

Customer Intelligence vs. Customer Success Analytics

Generally, customer intelligence is an attempt to use data to create insights (intelligence) about customers and can be used anywhere in the organization.

Customer success analytics is more specifically related to the common metrics and key performance indicators used in data-driven customer success, e.g., net churn, gross churn, net revenue retention, time to value.

Types of Customer Analytics

There are four categories of customer analytics you’ll want to monitor:

1. Diagnostic analytics: Uncovers why customers feel the way they do or behave a certain way. For example, “Why do clients avoid using our new feature?”

2. Descriptive analytics: Provides insight into the behavior of past clients. This is useful in identifying the root cause of issues and predicting future friction.

3. Prescriptive analytics: Attempts to answer the question “What should we do next?” Data often poses many questions; prescriptive analytics helps guide your future actions based on past and present data.

4. Predictive analytics: Uses past data to predict future customer behavior.

Within the categories there are ten primary analytics you’ll want to monitor.

Churn Rate - Churn is the rate at which customers stop doing business with you over time. Churn rate is calculated by taking your monthly recurring revenue and dividing it by the revenue you lost for the month. Don’t include any upgrades or additional revenue from existing customers.

Net Promoter Score - NPS measures customers experience and can be used to measure customer loyalty. To calculate NPS, subtract the percentage of detractors who scored between 0 and 6 from the percentage of promoters that scored between 9 and 10.

Customer Health Score - Your customer health score helps you predict client behavior, which is key for preventing churn. The formula will vary depending on your business, but the following two questions are the main two you want to answer:

What are my customers currently experiencing? How can I improve this experience before they churn?

Average Revenue Per User - ARPU is useful for calculating your recurring revenue. To find this number, simply divide your monthly revenue by the number of customers for that month.

Monthly Recurring Revenue - Your MRR is your predicted monthly revenue for all your customers each month. To calculate your MRR, multiply your number of subscribers by the average revenue per user.

Trial Conversion Rate - To monitor how many users convert from a trial, divide the number of converted users by the number of trial users.

Customer Retention Rate - Your retention rate is the number of clients who stay with you after a period of time. To calculate this number use the formula below:

S = The existing customers at the starting period.

E = The total number of customers at the end of your measurement.

N = The number of new customers added between the start and the end.

[(E-N)/S] x 100 = CRR.

Average Days to Onboard - This metric can help identity problems in your onboarding process when this number becomes too high. You can calculate this number by adding the onboard days together and dividing that number by the amount of customers onboarded.

Product Usage - Product usage helps determine how engaged a client is with your product. Low usage can indicate churn, while high usage can indicate a loyal customer or potential for upsells. This metric will be measured differently depending on the product. Try to answer these questions:

Is the customer engaging with the product more or less than others?

Has usage changed dramatically in a short period of time?

Does their usage exceed their plan? Do they need more from our product?

Average First Response - Low first response times can cause friction between you and your customers, especially if they have an issue that needs solving. You can calculate your response time by adding all the first response times and dividing it by the number of tickets resolved.

If you want to help your B2B SaaS company keep heading in the right direction, you need to use customer analytics to build a customer experience that meets or exceeds your customers' expectations.

Customer Success Analytics Scoring

There are a few different ways you can transform your customer success analytics into meaningful insights. Below are two popular methods businesses use to monitor their customer success proactively.

Customer Health Scores

You can use customer health scores in your customer analytics efforts. A customer health score is a metric customer success teams use to determine whether a customer is healthy and will remain loyal or is at risk for churn.

Customer health scores can also help you predict future events, including whether or not your customers will continue to subscribe to your services. This helps you determine what you need to do to mitigate any risks or make the most of future opportunities.

Net Promoter Score

In addition, you should monitor the net promoter score, a metric that measures customers’ loyalty to a company, to get insights about how satisfied your customers are. NPS helps you quickly address your customers' concerns and increase retention.

To calculate your NPS, simply add up the number of responses for each score and group those responses.

The group scores 9 or 10 are your loyal brand promoters who are great candidates for loyalty and referral programs. Scores 7 or 8 are passive responses; this group isn’t dissatisfied but could be vulnerable to a better offer. Lastly 0 through 6 are your detractors; this group is unhappy and at risk of churning or leaving negative reviews. Subtract your detractors from promoters to get your NPS.

"[Companies] with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify [customer experience] issues and opportunities in real time," according to McKinsey & Co.

How to Use Customer Success Analytics

Customer data is a powerful tool, but in order to extract value from it, you’ll have to know what questions to ask and where to look for it.

Customer data alone cannot help drive change or improve customer experience without processing it. This is where customer success software comes in to transform silos of data into meaningful customer intelligence for your whole company.

Customer success software ingests internal and external data to produce meaningful metrics success teams can make decisions from. For example, customer success software can combine the number of help-desk tickets along with feature usage data to determine that a customer may churn soon due to technical problems.

Let’s look at different stages of the customer journey and examine how analytics can play a role in improving the customer experience.

Onboarding

Onboarding is the first real impression your customer gets of your product and customer service. Having a smooth onboarding process is key to improving retention and preventing churn. Developing helpful resources, training new users, and having an easy-to-use interface can all help improve metrics during the onboarding stage.

Metrics measured during the onboarding stage are as follows:

• Customer response rate

• Product adoption rate

• Customer progression (how far they completed a training or trial)

• Time to completion

Adoption

Customer success metrics that measure adoption can signal whether or not a client will stay for the long term. A lack of feature use and responsiveness could indicate the product isn’t helping solve their problem or that the customer has moved on to another tool.

You can reach out proactively during low adoption to offer product support or exclusive discounts and offers to sway customers back into your ecosystem.

Metrics measured during the Adoption stage are as follows:

• Customer response rate

• Product adoption rate

• Number of logins per week/day/month

• Time spent with the product

Retention

When reviewing your retention metrics it's key to prioritize customers that are at the most risk of churning. You can further prioritize this at-risk list by account size to avoid larger losses from churn.

Customer churn and renewal rates are important metrics, but the key to improving those metrics is to continuously monitor your customer’s health score. Health scores continuously collect and analyze data to predict future behavior, including risk of churn.

Metrics measured during the Retention stage are as follows:

• Renewal rate

• Churn rate

• Customer satisfaction

• Overall customer health score

Expansion

Monitoring customer behavior and milestones is key to identifying opportunities for expansion into other products and services. Customer data can uncover genuine opportunities for upsells and cross-selling that feel like a natural progression.

Customer success managers can use this data to congratulate customers on their milestones and provide helpful resources for the next stage of their business. This builds trust and improves the take rate of upsell down the line.

Metrics measured during the Expansion stage are as follows:

• Internal customer metrics (email subscribers, website traffic, monthly sales, etc.)

• Feature usage (did a customer suddenly get a spike in new email subscribers?)

• Product usage (did a customer add more team members?)

Advocacy

Advocacy is about turning customers into loyal fans that will promote your product for you. Identifying advocates can help create new social proof of marketing, increase retention through loyalty programs, and even gain new customers through affiliate deals.

So how do you know who’s an advocate? Look for customers who leave great reviews, use your product often, and score high on CSAT surveys. Look for customers that have an overlap in one or more of these metrics to find your best advocates.

Metrics measured during the Advocacy stage are as follows:

• High CSAT scores

• Positive reviews

• High product usage

• High engagement (email opens, ad clicks, etc.)

As your customers generate more data, your team has a more accurate look at how customers may behave. When you combine these insights with customer success playbooks your team will know exactly what to do for each scenario that’s predicted. Playbooks take the guesswork out of knowing what to do with your newly discovered insights and help build a strong foundation for your customer success team.

Benefits Analytics

While swaths of customer data can seem confusing, the benefits of customer success analytics are quite clear. Collecting and understanding your customer data can unlock unique insights that can improve your products, internal processes, and of course your customers’ experience.

Leveraging customer analytics can do the following:

• Lower lead generation and customer acquisition costs

• See higher customer satisfaction and retention

• Increase sales and revenue

• Improve brand awareness

• Increase customer engagement

• Streamline customer service

With customer data insights, you can understand your customers on a level otherwise impossible. This not only allows you to predict churn and see upsell opportunities but also can aid other departments such as technical support and marketing.

For instance, marketing departments can use insights gained from customer success to create better ads with more engagement and high clickthrough rates. This translates to lower acquisition costs and more effective ad campaigns.

Customer Success Analytics Best Practices

Although companies in different industries might approach customer success analytics differently, there are some general best practices you should keep in mind:

• Determining the outcomes you're trying to achieve.

• Identifying the data inputs that mean the most to your company. These will be your key performance indicators.

• Collecting and collating the data you need.

• Cleaning and consolidating the data into one actionable customer view.

• Understanding what the data is saying by looking for patterns and anomalies across your key KPIs.

• Using the conclusions to make smarter business decisions.

Start with your goals in mind and work backward using your customer data. This approach can help keep you focused on your overall goals and help eliminate any confusion when dealing with many data points.

For instance, your goal is to increase your customer LTV, you’ll know to focus on retention metrics and data that indicates upsell opportunities. Setting your goals first also will guide what data you collect and which KPIs you track.

As you begin to track different metrics, be mindful of patterns that emerge and regularly review each customer journey stage. Your product will naturally evolve along with the goals and needs of your customers.

Creating a customer 360 view is powerful, but requires work to maintain. Regularly ensure timely information is getting pulled and consolidate repetitive information to make insights clearer for your team.

How Can Planhat Help My Company?

Planhat can help your company grow and become customer centered by reducing churn, increasing upsell/upgrades, and boosting visibility.

Planhat does this by integrating data from multiple systems into a flexible and powerful platform on top of which you can manage onboarding, renewals and other processes, collaborate and communicate with your customers, and visualize insights to everyone in your company.

Planhat pairs customer success analytics and customer intelligence methods (e.g., standard KPIs) to increase visibility (e.g., through advanced filters) and power action (e.g., through automations based on certain KPIs).

Why Planhat Is the Best Option for Your Business

Planhat is the most powerful customer success platform on the market in terms of the depth and breadth of its features thus offering the flexibility and power to go after mission-critical use cases.

Customer success is a company-wide effort and as such Planhat has included unlimited users in all its plans, unlike other vendors. To start unlocking the potential of your customer data check out our free demo, or watch our webinar on how to identify and prevent churn.