Customer Success

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Analytics is the foundation of customer success; without the right data powering your decisions, it’s impossible to foster long-term sustainable growth. With Planhat, you get the insights you need to keep your Customer Success team informed and ahead of the curve


Increase customer understanding

Increase retention, and reduce the risk of churn, by using analytics to get a better understanding of your customer's behavior. This gives you better insight into product development, marketing strategies, and sales tactics as a whole.


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Customer Success Analytics

Your customers are the center of everything you do. That much is certain. In the beginning, your company’s ability to grow was directly related to how skilled you were at acquiring new customers. Now, after becoming established, your focus should be directed to maximizing the life-time value of your customers. Like all things in business, this is easier said than done. In today’s globalized and intensely competitive tech market, your customer success (CS) team needs to be armed with the right technology and data to stand out against the competition and create the value your current and potential customers are looking for.

Analytics is the foundation of customer success; without the right data powering your decisions, it’s impossible to foster long-term sustainable growth. And as the CS field continues to adapt and grow, the importance of customer analytics remains paramount. At a lot of companies, sales are the rockstars and CS are deemed to be the friendly support-people. At Planhat we see things differently. We think that CS is actually the most important sales-function, and should be treated as such.

In an effort to provide some clarity, Planhat has created this 2 part customer success analytics guide to help keep your CS team informed and ahead of the curve.

Part 1: The How and Why of Customer Success Analytics

Then and Now: What Is Customer Success? How Is New CS Tech Making Positive Changes?

Gartner defines customer success as a “method for ensuring customers reach their desired outcomes when using an organization’s product or service.” But the customer success field has undergone some significant changes in recent years.

In the past, particularly in the SaaS industry, there was a lack of maturity, no established frameworks, and an inconsistent understanding of CS as a business function. This made it difficult for business leaders to accurately identify the CS team's needs. The results? Years of stagnant technological innovation regarding CS platforms. However, things are beginning to change for the better.

The SaaS industry now recognizes that customer success is a critical component of driving growth for their businesses. New tools and methodologies are being developed for CS teams around the world, especially in regarding the area of customer analytics.

What Is Customer Analytics?

Customer analytics, or customer data analysis, is usually defined as the examination of a customers’ information and their behavior. Using customer analytics is vital if CS teams are to identify, attract, and retain the most desirable customers. Although customer analytics looks different in each organization, there are a few common techniques that are used in most processes, including: data collection and segmentation, data modeling, and data visualization.

Ultimately, the goal for CS teams when monitoring customer analytics is to create a comprehensive and accurate overview of the customer experience. The data collected and analyzed in this process allows CS teams to better understand a customer's buying behaviors and preferences, and adjust the customer journey accordingly. In addition, these data insights are used to inform decisions surrounding customer acquisition and retention, identify high-value customers, and reduce interactions with low-quality leads.

However, without vast amounts of accurate data, any insight generated from the analysis process could be wildly inaccurate and severely hinder progress made by your CS team. And that is where customer analytics tools come into play, but we’ll discuss that in greater detail later on in this article.

Customer Analytics Examples

Before we dive into specific examples of customer data analytics, we need to outline the categories or types of customer analytics that are used throughout this process, and explain how these analytics are used when it comes to generating insights about your customers.

  • Descriptive Analytics

    Descriptive analytics covers anything that gives you insight into past customer behavior. This includes analyzing customer success KPIs like revenue generated per customer, customer attrition, or the average amount of time it takes your customers to pay their subscription bills.

  • Example of Descriptive Analytics

    One percent of customers unsubscribed after a recent update was made to the SaaS product.

  • Diagnostic Analytics

    Diagnostic analytics are all about uncovering the “why” hidden in your customer behavior data. They explain what’s going well and what needs to be improved in your CS process. This can include analyzing market demand, and conducting regular customer health score analysis.

  • Example of Diagnostic Analytics

    Five percent of customers think that the new software extension for your product increased the value of your service overall.

  • Predictive Analytics

    Predictive analysis helps you anticipate and prepare for future customer behavior. This involves things like analyzing data related to market trends, or reviewing historical data to find the correlating factors that predict when a customer is going to churn. In the digital age, customers have greater access to information telling them where to shop, what to buy, and how much to pay. This means it’s even more important for companies to use predictive analytics and data to forecast how their customers will behave as the market continues to evolve and customers' habits evolve alongside it.

  • Example of Predictive Analytics

    In the next quarter, subscription renewals are expected to increase due to XYZ.

  • Prescriptive Analytics

    Prescriptive analytics allow you to formulate your game-plan on how to influence customer behavior. Think of it this way, diagnostic analytics tell you what is wrong with your customer success approach and prescriptive analytics “prescribe” a solution.

  • Example of Prescriptive Analytics

    New support videos and resources have proven to increase customer satisfaction by 10 percent.

Each of the above general analytics are used when assessing the different types of customer data:

  • Customer Engagement Data

    Customer engagement data may reveal insights into two categories:

    1. Data revolving around how your customers engage with service on a micro level.

    2. Data revolving around how your customers engage with your company or brand as a whole (macro level).

    Customer success teams choose to track this in different ways, but most CS teams review data points like usage metrics.

  • Customer Experience Data

    How are your customers feeling? Customer experience data allows you to uncover the answers to this all-important question. Analyzing this data enables CS teams to discover exactly what customers are thinking and experiencing when they interact with your brand and products. Customer support metrics are a critical component in customer experience data. This includes metrics surrounding onboarding, ticket resolution, and bug occurrences in your product. An easy way to access this data is by using a CS platform that tracks support tickets and individual chat conversations your CS team deals with on a daily basis. This can get tricky if this information is spread across multiple platforms. At Planhat, our Customer Success Inbox tool solves this problem. Our single inbox is a searchable place for all your customer conversations that seamlessly integrates with your customer communication tools so you can stay on top of what is said to whom and when. And since Planhat comes with an unlimited seating-model it encourages different departments to collaborate in a single space. This simplifies internal handover between CSMs and makes it easy for different stakeholders to share information and stay on the same page.

  • Customer Journey Data

    Customer journey data focuses on understanding the steps each customer takes to discover, purchase, and commit to using your services. This involves collecting data related to the initial research stage, discovery stage, the final purchase, and the development of loyalty to your brand. Since there is so much data to track in the customer journey, it’s helpful for CS teams to have tools that store and track these insights. For example, Planhat’s Customer 360 tool allows you to view usage trends, subscription dates, health scores, user data, and everything else you need to know about your customers in a single, customizable place.

  • Customer Lifetime Data

    Customer lifetime data really overlaps with the insights derived from both customer journey and customer experience data. However, there is an important distinction: customer lifetime data allows CS teams to accurately calculate their Customer Lifetime Value (CLV). CLV shows you exactly how much revenue a single customer will generate over the lifetime of their relationship with your brand.

  • Customer Retention Data

    Customer retention data delivers insights into how well you are doing with nurturing long-lasting and loyal relationships with your customers. This includes analyzing how many of your buyers are repeat customers and identifying your churn rates. Customer retention data is especially useful when measuring how you stack up against your competitors.

How Do You Use Customer Analytics?

Customer analytics often receives input from multiple departments within an organization. This includes marketing, sales, IT, business analysts, and customer success (of course). To create an effective customer analytics framework, every involved party needs to agree on how the framework should be set up, which tools should be used, and what metrics should be tracked. Below we’ll describe some of the best practices associated with effectively using customer analytics and setting up a comprehensive framework.

What is a Customer Analytics Framework?

Creating a successful customer analytics framework depends on how strong your CS technology stack is. To get the insights you desire, you need comprehensive and customizable tools to access, store, and analyze your customer data. Because without accurate data, any insights you derive and decisions you make might push your CS efforts back instead of accelerating you towards success. Therefore, you need CS tools that allow you to store all of your customer data in one place. And that’s where we come in. Planhat is a Customer Data Platform that allows you to structure, manage, and interact with your customer data all in one space. Our platform easily integrates with the applications you already use and gives you a single view of all your customer data so you can feel confident that any CS decision you make is backed by trustworthy data.

Once you have your data sorted and squared away, you can begin to lay out your actual customer analytics framework. Although this is likely to look different for every organization, there are four general components that contribute to a solid foundation for your customer analytics frameworks.

  1. Creating Individual Customer Success Personas

    Based on real customers, a customer success persona is an imaginary profile describing the characteristics of the ideal customer for your company. It is likely that you will have multiple different customer personas based on customer segmentation.

  2. Creating Comprehensive Customer Journey Maps

    Customer journey mapping is where your CS team works to create a comprehensive map of all possible points of interaction between your customer and your company. Each journey map begins at the brand discovery stage and ends at the post-purchase stage.

  3. Knowing Your Touchpoints

    In this stage you begin to gather your customer analytics data from various touch points like: your website, newsletter clicks, browsing your website, app downloads, interactions with your brand on different forums and on social media. Regarding touch points, it’s important to track and visualise customer usage data in real time to easily visualize how your customer engage with your product. Planhat’s dynamic usage analytics platform makes this process easy. Additionally, Planhat’s Product Analytics feature will help you track interactions and engagements adoption across the customer journey, offering direct insight into how your customers are using your products.

  4. Defining Your Outcomes

    Lastly, you need to define the outcomes you want to see from your data. After you do this, you will be able to define what type (or types) of analytics processes (descriptive, diagnostic, perspective, and predictive) need to be performed on each dataset. Let’s look at some examples:

    • If you want to receive clarity on how something happened with a specific set of data then you should use descriptive analytics.

    • If you want to know why something happened within the data then you should run diagnostic analytics.

    • If you want insight into how to influence future customer behavior based on the data you’re seeing then you should run prescriptive analytics.

    • If you want to know what may happen in the future based on the data you collected then you should run predictive analytics reports.

What Are the Benefits of Using Customer Analytics?

So, how can customer analytics help a business? Gathering, storing, and analyzing the correct customer data is critical if CS teams are to possess an in-depth understanding of the customer success landscape and successfully adapt to the ever changing needs and demands of their customer bases. It’s no coincidence that successful companies are heavily invested in their CS data. According to a study conducted by McKinsey, companies who make intensive use of customer analytics are 2.6 times more likely to have a significantly higher ROI and 3 times as likely to generate more revenue growth than their competitors.

Guesswork and gut feelings simply don’t work when it comes to customer success, data does. That’s because accurate CS data tells you exactly what you need to do to reach your retention and growth goals. From promoting customer loyalty to increasing engagement.

Although the benefits of customer analytics are manifold, here are just a few highlights of what CS analytics can help you obtain:

  • Higher customer satisfaction and retention

  • Lowered lead generation and acquisition costs

  • Increased sales and revenue

  • Increased brand awareness

  • Increased user/customer engagement

  • Increased personalization of your content

  • Focused campaigns that are always sent to the right audience

  • Increased positive experiences throughout the customer journey

  • Better insights into product development, marketing strategies, and sales tactics as a whole

Part 2: Customer Success Analytics in Action

Check Out Emerging Trends in Customer Analytics

As we mentioned before, the CS field is continuing to evolve and develop. Because of this fast development, new trends are popping up all the time. Since there is so much going on and changing, it’s important to stay as up-to-date as possible in the CS world so your team benefits from all of the newest tactics and information. Here are just a few CS data trends we’ve identified after surveying our customers.

CS teams are gearing up to optimize their tech stacks.

Customer success is still relatively new compared to other established departments when it comes to getting data-centric technology. Sadly, many CS teams are struggling to identify and implement the right software. Although the market for customer success software is growing, there is still a shortage of purpose-built tools that are able to deliver CS teams the analytics they need.

CS teams are putting more emphasis on leading indicators.

Customer success teams are laser-focused on metrics like customer churn and revenue since these are the easiest CS metrics to measure and understand - especially with the available CS technology. However, leading indicators like product adoption, usage, and engagement truly reveal how much value your customers get out of your product. As CS continues to develop and technology improves, we expect that CS teams will transition to setting goals for those metrics in mind.

CS teams are (still) fighting for a 360-degree customer view.

CS teams are still storing customer data across several platforms. This makes it difficult to access, understand, and build an accurate representation of customer health. Thankfully, new technology is emerging to give CS and other departments a full understanding of their customers’ journey.

If you would like to learn more about what’s next for customer success, download our detailed report here.

Let’s Learn: Your Handy Customer Analytics Syllabus

We know this can be a lot to take in. So, in an effort to make this information more manageable we created a mini-syllabus with 3 simple lessons to help you level up your customer analytics skills.

Lesson 1: Ask The Right Questions

The first step in using customer success analytics to your advantage is to consult with your CS team and start asking questions. The more brainstorming sessions and insights you can get from the entire CS team, the stronger your customer success analytics framework will be. Here are a few examples of questions you should ask in these brainstorming sessions:

  • Which channels drive the most new customers?

  • What are our most profitable revenue channels?

  • Where do we lose customers and why?

  • What features resonate with which customers?

  • Who are our customers – their age, demographics, general location, purchasing capability, etc.

  • What do customers prefer to buy from us vs competitors?

  • What is their preferred mode of purchase?

  • What is their preferred mode of communication?

  • What are their preferred touch points at different stages of the buyers’ journey?

Lesson 2: Choose The Right Customer Analytics Tools

Although much still needs to develop and improve, there are CS data tools out there and your team needs to take advantage of them. However, it should be noted that not all CS analytics tools are created equal. With that in mind, we’ve created a checklist for you to reference as you search for the customer analytics and CS tools that best meet your needs.

  • Flawless data security

  • User friendly UX

  • Seamless implementation

  • Rapid deployment

  • Easy adoption

  • Extensive customer success toolkit

  • Cloud capabilities

  • End-to-end integration

  • Human-centric approach to analytics

  • Easy collaboration between departments and with customers - everyone sees and shares the same information

Lesson 3: Stay Informed and Educated

From new processes to new tech, with so much changing it’s critical that CS teams stay informed and aware of what’s happening in their industries. This can be as simple as throwing on a podcast, or attending a virtual conference discussing emerging trends in CS analytics. At Planhat we have a variety of podcasts, webinars, and reports designed to help keep everyone educated, increase communication, and benefit ideation.

Case Study: See What CS Analytics Can Do For You

What does all of this look like in action? Here are a few details from a recent customer analytics case study we conducted.

  1. The Data Issue: Our client had their customer data scattered across different platforms, making them feel disorganized, and providing them a limited overview. When pressed about the issue further our client said, “Client centricity has always been important to us, but how do we live and breathe it? Sure, we can track reviews and meetings in Outlook and our numbers in Excel, but that's not scalable. We needed a tool for Customer success to scale.”

  2. Planhat Got To Work: After the initial challenges were outlined, Planhat began evaluating the most critical data and devised a game plan for how to incorporate that into our data module. With the data in place, we were able to provide our client with the customer insights they needed. Those insights then allowed us to help our client establish playbooks and automations within the Planhat platform so their team could better prioritize their time. With Planhat taking an active role in the implementation, it was clear that the customer platform had a significant impact on revenue, resources, and customer experience.

  3. The Results. In the end, our solution delivered:

    • High impact focus from CSMs when automations unload the burden of administration.

    • Strategic insights delivered to leadership with a single source of truth.

    • Increased revenue due to high level of renewals.

This is just a brief overview of how Planhat uses customer success analytics best practices to help our clients skill-up their customer analytics framework. If you’re curious to learn more, you can read all of our case studies here.

Planhat: Dedicated to Your CS Success

Congratulations, you’re on your way to leveraging your CS data more effectively! If you still need help, just reach out—at Planhat, we offer you technology that helps you focus on, understand, and drive value for your customers. At Planhat, we believe that the success of an organization is directly tied to the success of their CS team. By giving CS the tools and insights they need, we aim to help CS teams play an even more crucial role at their companies.

We believe that every team should be focused on the customer at the end of the day, and our platforms let teams do just that. Learn more about our mission to make customer success everyone’s business on our website and sign up for a free demo of our platform. Cheers to a brighter CS future!

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