How to use customer data analytics to increase sales

The amount of data collected and tracked by companies, websites, and organizations is overwhelming. However, data provides insights that can lead to more sales, better-targeted marketing, and higher customer satisfaction. The collection and use of customer data analytics is a prime example of how analyzing the right data can lead to bigger and better results.

What is customer data analytics?

Customer data analytics is the process of analyzing collected customer data to build a comprehensive picture of how customers interact with a brand’s product or service. 

Using this data often leads to better insights regarding customer usage and adoption, as well as customer retention. What’s more, it can have a significant impact on customer acquisition by providing better insights into how to target and attract like-minded customers looking for a similar product or service.

How to capture customer data for analysis

Capturing and analyzing customer data is a multi-step process and often requires the use of specifically designed customer data analytics tools.

Start with clear objectives

To get the most out of customer data analytics, it’s important to begin with a clear vision of what the outcome should look like. The data and analysis will only be as good as the questions asked. Similarly, applying the analysis will only work if there are clear end results in mind. 

When specifically looking to use customer data analytics to grow sales, two main categories should be the most important: customer acquisition and customer retention.

Identify the necessary data

Companies may already have access to the data they need through tools they use every day. Website analytics, social media tracking, and transactional data are more than likely ready for mining using a few targeted searches and parameters. Once there’s an inventory of the data readily available, it’ll be easier to determine if other resources will be needed.

Use the right customer data analytics tools

Google Analytics is perhaps the biggest and most famous player in the customer data analytics toolbox. However, it only tracks the online behavior of website visitors. Different tools measure different types of behavior, actions, and more. Often the best and most actionable insights are found by analyzing these different sources all together.

To make sense of this data, a customer data platform (CDP) is necessary. A CDP can take all disparate data sources and bring them together into one resource. When combined with a data storage tool, companies can run the type of customer data analysis that’s needed.

Sorting the data into manageable pieces will also rely on building relevant customer profiles and segments. The amount of data available is vast, and sifting through all of it will take time and can lead to a lot of false starts and tangents. By better defining target segments from the start, it’ll be easier to truly evaluate the data for the insight it provides and not worry about missing the mark.

Customer data analysis examples

Successful customer data analysis can have far-reaching effects on business success and growth. Take, for example, Glossier, a makeup and beauty brand that experienced unprecedented growth of 600% over the course of one year.

In order to sustain that growth and success, Glossier knew it would need better insight into its customer base—and that data was the only way to get it. By better understanding their customers through data, Glossier was able to create a customer journey map, outlining key points during the customer lifecycle that could lead to retention or churn. They were also able to better source where their customers came from and their lifetime value. These two metrics alone were enough to help Glossier develop a strategy to retain customers and provide insight into how to find more.

Even a big-name brand like McDonald’s has exploited the power of customer data analytics. By using the data it has collected over decades of being in business, McDonald’s has been able to provide a more personalized approach to its customer base. While fast food restaurants have struggled with changing societal norms and the rise of food delivery, the Golden Arches has been able to carve out a spot by offering more personalization depending on customer location, weather, and time of day.

Customer data analysis drives revenue through insight

Customer data analytics can truly help to shape strategy and develop a better overall picture of what a business’s customers look like—and, most importantly, act like. Putting this information to work can lead to better and more targeted customer acquisition efforts and increase customer retention, leading to more profits and a successful business model. 

Get started with Lytics today and register for a free Cloud Connect account to find out how to use customer data to develop specific segments and eliminate data silos.