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  • Kuba Płonka

The Secret to Customer Success? Data Analytics

man with magnifying glass in front of charts
Image by vectorjuice on Freepik

Without a doubt, empathy, quick problem-solving skills and technical expertise are essential skills for a Customer Success team. These skills will help with day-to-day operations, but how do we ensure customers are delighted with products and services and offer the same quality of support to every user? The answer lies in data and analytics.

In today’s article, I’ll tell you what role both of these things play in the work of a successful team and give ideas on how you can start collecting and using them in your teams. Enjoy your reading!

Why data and analytics are critical to customer success

Data and analytics play a crucial role in helping companies understand their customers, track key metrics and make informed decisions to improve customer satisfaction and success.

One activity where data analytics is essential is understanding your product or service’s needs and pain points. With data, you can track user behaviour and further understand what works well and what doesn’t. With analytics, you can see where users spend the most time and whether they go through the entire process within the tool or abandon it at some point. Along with analytics, it will work great here to collect additional assessments such as NPS or CES to get an even broader overview.

Another area that can go to a new level with data analytics is personalising our customers’ experience. By collecting details about the people who use our products, we can create better marketing messages about, for example, product changes that interest them. We will create better opportunities for cross-sell and up-sell activities with such data. Most importantly, such experiences will lead to aha moments, i.e. situations in which the user realises the benefits our product or service offers. Such moments are among the most “valuable” cases, and CS and product teams should make users achieve them as soon as possible. Thanks to such moments, the loyalty of our users increases and thus the likelihood that they will stay with us for longer. If you want to read more about this topic, I wrote a separate article about it some time ago with suggestions for experiments.

Measuring customer satisfaction is another topic only possible with data processing. Customer satisfaction is one of the most critical metrics in customer success. Tracking customer satisfaction over time allows you to identify trends and patterns and make appropriate changes. For example, if you notice that customer satisfaction is declining, you can use this information to conduct a customer satisfaction survey to find out what is causing the problem. I recommend the article on the HubSpot blog describing how you can quickly start measuring customer satisfaction.

Finally, improving user retention rates is closely related to the previous two points. One of the most critical tasks facing the customer success team is to make, preferably, all users renew their licenses for the next period. Data, in this case, could be more valid, but we need it to discuss any structured approach to this issue. Thanks to the data, the numbers representing customers who have abandoned our product are transformed into the specific problems behind the decision to leave, e.g. too little support, at some stage, failure to achieve the expected benefits, or the set of functionalities compared to the competition is unsatisfactory. Any such information is essential to turn a single loss into a long-term success for our team.

Tips for collecting and processing data

Here are some tips on getting started and improving data work in your teams.

Identify the right metrics

The first step in using data effectively is identifying which metrics are most important to your business and customers. The CS team’s work starts with identifying metrics and tracking them. Not to hold individual team members accountable for their work but to see how our product performs and proactively manage changes. Some standard metrics you can track are customer satisfaction (CSAT), churn rate, Net Promoter Score (NPS) and usage metrics like Monthly Active Usage (MAU). Not too long ago, I wrote a whole article about popular metrics and when it’s a good idea to use them, so if you have yet to learn or would like to know more, read this article.

Collect data from multiple sources

Collecting data from various sources, such as customer surveys, support tickets and usage data, is essential to get a complete picture of your customers. This will give you a full view of the customer experience and enable you to make more informed decisions.

Use a customer relationship management (CRM) system

A CRM system can help you collect, store and analyse customer data in one place. This will make tracking metrics, identifying trends, and making informed decisions more straightforward. You can read about CRM and other tools I use.

Collaborate with teams across the organisation

Data and analytics should be a collaborative effort among teams across the organisation. For example, the marketing team can provide insights into customer behaviour and preferences, while the customer service team can provide insights into customer pain points. By working together, a more comprehensive picture of customers can be obtained, and better decisions can be made. The customer satisfaction team should bring all data and customer-centric initiatives together in one place, but they can do some things themselves. Remember that the CSM’s job is to be the voice of customers (VoC) in your organisation; use all the help you can get to achieve this goal!

The Final Word

Data and analytics play a crucial role in customer success. Using data to understand your customers and their needs better, you can create more personalised experiences, increase satisfaction, reduce churn, and drive revenue growth.

Here are some book suggestions and additional articles for those who want to dive deeper into the topic:

  • “The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses” Eric Ries. The book provides a framework for using data within a company to validate and create new products. Suitable for those who want to make business decisions based on data.

  • “Data-Driven” Hilary Mason, DJ Patil. This book shows you how to create a data-driven (Data-driven) organisation. You will find much practical advice on using data to drive business decisions.

  • “Hooked: How to Build Habit-Forming Products” Nir Eyal. The book describes a framework for creating products and services users will love.

Do you guys collect and analyse data in your companies? Where did you start, and what are the most significant advantages you observe in your work? Let us know in the comments!


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