How to Analyse Customer Data: 7 Steps to Glean Actionable Insights

Virtually every modern business has gone to considerable lengths to begin to collect data about their customers and operations. The problem is, many organisations struggle with how to translate that data into insights that can be used to improve the customer experience and make smarter business decisions. In this post, we outline seven tips on how to analyse customer data and discuss the role of Customer Data Platforms (CDPs) that support usability, visibility, and control. 

Establish objectives

Establishing a good set of initial objectives can focus your work and help define future objectives. Some common objectives for customer data analysis include:

• Increasing customer retention

• Identifying opportunities for cross-selling and upselling

• Improving the effectiveness of marketing campaigns

• Testing messaging and gathering customer sentiment 

Keep in mind that different types of data will be better suited to answer different types of questions. For example, transactional data will be helpful in understanding what products are being purchased together, while survey data can reveal how customers feel about your brand.

Identify the data path within your CDP

Establish the framework for the data analysis you will be conducting within the Customer Data Platform (CDP) you are using. Look at the customer touchpoints you have in place and create the analysis path within your CDP. 

The data path you establish will help you understand which interactions and customer behaviors to focus on. By understanding how customers interact with your brand, you can begin to improve the customer experience. 

Segment your model to include: 

• How customers interact with each other

• How they interact with your brand

• How you can improve the customer experience

By understanding how customers interact with your brand, you can begin to improve the customer experience. For example, you may notice that customers who come from a specific website are more likely to purchase a certain product. You can then focus on how to improve the customer experience on that website in order to increase sales.

Organise and clean the data

Start organising and cleaning the data by first inspecting it for any errors and filling in missing values. This is important to do before starting any analysis, as errors can skew the results. Remove and fix any errors before continuing.

Also, be sure you are properly protecting the data and following any relevant compliance rules. Compliance is important when handling customer data because it ensures that the data is being used in a legal and ethical way.

Model the data

Next, you will utilise your CDP to filter your data into actionable insights. Data modeling is the process of representing data in a format that can be used for analysis. This step is important because it allows you to make sense of the data and how it relates to your objectives.

There are many different ways to model data, so it is important to choose the right approach for your specific needs. Some common methods include: 

• Clustering: This method groups together data points that are similar to each other. This can be used to segment customers or understand how products are related to each other.

• Regression: This method is used to identify relationships between different variables. This can be used to predict customer behavior or understand the impact of marketing campaigns.

• Time series: This method is used to identify trends over time. This can be used to understand how customer behavior changes over time or how seasonal factors impact sales.

• Association: This method reveals relationships between the data points

Once the data has been modeled, it is important to validate the results by ensuring consistency throughout and that you’re meeting formatting standards. This helps ensure that the conclusions you draw from the data are accurate.

Analyze the data

You can conduct various analyses of the data once you have everything in place. Decide what type of analysis works best for your use case. Examples of analysis types include the following:

• Cluster analysis: This is done by first finding groups of similar customers and then analyzing the groups to look for trends.

• Cohort analysis: With cohort analysis, you group customers together based on when they joined your brand. This allows you to track how each group behaves over time.

• Predictive analysis: Predictive analysis looks at past customer behavior to predict future behavior. This can be helpful in identifying potential customer churn or opportunities for upselling and cross-selling.

• Regression analysis: This is a statistical technique that can be used to understand how different variables relate to each other.

Once you have decided on the type of analysis, you can begin to look for insights. You can then focus on how to improve the customer experience on that website in order to increase sales.

Present data to stakeholders

Numbers often speak for themselves; however, it is important to present the data in a way that is easy for stakeholders to understand.

One way to do this is to create visuals, such as charts and graphs. This will help stakeholders more easily understand the data in different ways. Another way to present the data is to create a story around it. 

By presenting the data in a way that is easy for stakeholders to understand, you can get buy-in for your customer experience improvement plans. Use data to propose ideas or strategies to stakeholders and highlight the insights that will help you reach objectives.

Optimise, refresh, repeat

Now that you have an analysis path established, optimize it.  This may mean automating tasks or changing the way data is collected. For example, you may want to consider real-time data collection if you are doing predictive analysis.

Once you have optimised your process, it is important to refresh the data regularly. This will ensure that the insights you glean are up-to-date and accurate. By regularly refreshing the data, you can be sure that your customer experience improvement plans are based on the most current information.

Remember to repeat this process on a regular basis. By continually analysing customer data, you can adapt your plans as needed and ensure that you are always focused on improving the customer experience.

Consider branching into different data sets for new insights.  For example, you may want to look at social media data to see how customers are talking about your brand. This can be done through social listening. You can also consider surveys and customer feedback to get an understanding of how customers feel about your brand.

By expanding the types of data you are looking at, you can get a more well-rounded view of the customer experience. This will allow you to make more informed decisions about how to improve it.

Final Thoughts

There are many benefits of customer data analysis, including improved customer experience, brand loyalty, and increased sales. However, conducting this analysis can be time-consuming and difficult and it’s important to have the right tools on board. This is where a high-quality Customer Data Platform (CDP) comes into play. 

Zeta Global’s CDP+ sits at the core of the Zeta Marketing Platform, providing you with more control over data so you can activate campaigns with greater speed and effectiveness. Zeta CDP+ delivers a single, actionable view of customers and prospects enriched with intent-based scoring and top activities pulled from Zeta’s Data Cloud. This platform is also powered by an architecture that works with existing tech via a low-code visual interface, providing total data transparency and control. 

When Polkomtel Sp. s o. o., a leading telecommunications operator in Poland adopted the Zeta Global platform to analyze customer data for actionable insights, they achieved a 200% increase in close rate and a 300% increase in transaction volume.