Customer Segmentation: A Data-Driven Approach To Improving Customer Experience

In this post, you will learn how data analytics enabled one of our clients to build customer segmentation and helped them tailor online marketing strategies and improve the experience in their facilities.

Customer Profile

This company operates a chain of fitness centers across the country. Their membership model is based on affordable monthly fees and offers flexible options, including pay-as-you-go options and long-term contracts. That provides members with access to all the center’s facilities and services.

They pride itself on creating a welcoming and inclusive environment for its customers. They wanted to reinforce that positioning by building segments of their existing customer base driven by data analytics.

Customer segmentation is the process of dividing customers into small groups based on their shared characteristics or behaviors

The Challenge

This company continues to grow, and so is its customer database. Segmenting this database is paramount to improving their experience and loyalty.

They needed to combine different types of data across the digital customer journey to paint the full picture: facility frequentation, social media interactions, and reactions to their email campaigns.

This is where it gets tricky, combining these 3 sources of data together in a reliable way. But that’s just the technical piece of the process. Then, the challenge is to make sense of that data to figure out how customers behave and build those segments. This will allow the marketing department to better plan their actions and as a consequence, to improve customer loyalty.

Our Approach

In this context, we followed this approach to complete the project:

• Combine multiple types of data to
• Analyze the data to define the customer segmentation criteria
• Monitor the results for each facility and also get an overview of all the centers’ results.

Let’s dive into the details of this approach:

Step 1 – Combining different data types

The first thing to do is set up a dataflow in ClicData to connect everything and bring all the data together. In order to define patterns in customer groups, all the data needs to be available at the customer level: visits to their centers, subscription information, profile, and activities on social media. It means that we will cross-reference data and have the full view per customer with data coming from both online and offline.

Step 2 – Analyze all the data to define customer segmentation criteria

Then, we created a “transitional” dashboard to detect patterns and commonalities among these customers. In this dashboard, all the variables that could be used in the customer segment’s rules will be analyzed.

Example: One of the criteria is the frequency of visits in each facility. Creating period brackets since the last visit is a simple way to analyze that. When the last visit was far away, there is a high risk of losing the customer. This criterion alone can be used to build a “Churn Risk” segment for the marketing department to target with hyper-personalized campaigns to make them come back.

Step 3 – Use new customer segmentation to improve marketing campaigns

Once the customer segmentation was decided, the marketing department was able to:

• Create a dashboard to keep track of the evolution of the existing database within those new segments
• Develop targeted marketing and emailing campaigns, and analyze the results thanks to the dataflows in place, in a dedicated campaigns dashboard
• Improve customer satisfaction by tailoring marketing tactics and messaging. This led to increased customer loyalty and positive reviews.

The Value Of A Data-Driven Customer Segmentation

The company has now a full picture of its customer database and is able to measure the campaigns’ performance from a customer, center, and marketing channel (email, social media) point of view. They can now plan the next best marketing activities more efficiently and with more confidence based on their customer segmentation.

They also perceived another positive side-effect.

This project revealed a few anomalies on the data side and helped them fix them with a new process in the centers and improved their tag management on social media.

Building a solid, data-driven customer segmentation has helped them better understand their customers and develop more effective marketing strategies to maintain customer satisfaction, and achieve their business goals. With data analytics, they are now able to look at how their customer database is changing and get a better idea of how their business is doing overall.