Welcome to this tutorial of the Customer Explorer.

This report lets you gain unique insights into any customer group by exploring their characteristics and behavior patterns. You can easily fine-tune group selection criteria and, when discovering a group of particular interest, immediately analyze it or save it as a Target Group for campaign targeting.  

The report includes three tabs that enable you to deeply analyze different groups of customers, compare group characteristics, and monitor migrations between them.  

Let’s focus on the first tab. You can start by loading a Target Group that has already been defined or by building a new group using the selection criteria menu.  

When clicking the Add Selection button, you can find all the selection criteria available, including Lifecycle stages, transaction history, predictive analytics, and engagement history.  

Let’s create a group of customers who were reactivated yesterday. It’s well known that reactivated customers tend to churn again quickly, so effectively communicating immediately upon reactivation is crucial. By exploring this group, we can discover opportunities for more relevant and personalized engagement approaches.  

Clicking the Generate button displays details about this particular group so that we can review its characteristics and summary metrics.  

For numeric attributes, the report displays the group’s minimum, maximum, average, and median values. For the categorical attributes, such as lifecycle and region, the report displays the distribution of the most frequent values within the group. You can customize which metrics are displayed using the Select Attributes button.  

Let's take a closer look at the group’s Total Order Value distribution. We can see that the average is much higher than the median, which means that some very high-value customers are pulling the average up. Let’s refine our selection and add a layer of selection criteria for customer activity and top-tier customers. This will focus on the highest value group among our customers who reactivated yesterday. Looking again at the Total Order Value, we can see significantly higher figures than we saw for the entire group.  

This information is very valuable to marketers aiming to retain these big spenders. You can immediately identify an opportunity to engage these customers differently than others.  

This is just one example. By exploring the characteristics of specific customer groups, we can gain a much better understanding of customers and refine selection criteria to create more granular groups that will respond better to focused messaging.  

In the middle of this page, you can see the top 100 customers in the selected group and their specific characteristics for each displayed attribute. Let’s sort by "Total Order Value." Here, you can quickly identify those individual customers who spent the most. Marketers might consider leveraging such a list, for an even further customized offer.  

A complete table containing every customer in the defined group can be exported to a CSV file for further analysis, action, or any other purpose.  

Click any customer ID to enter the Customer 360 page, bringing together all the data available on an individual customer.  

Please note that you can save any group defined here as a Target Group to execute campaigns or continue its exploration at a later point in time.   

The last part of this page is the Cohort Analysis. Cohort analysis is a tool to measure and track how specific, unchanging groups of customers behave over time. In the cohort analysis, we see how customers in the group we built survived over time. When we refer to survivability, we mean the percentage of customers from the original group at the beginning of the analysis period that are still active at the end of it. We can also see the accumulated total order amount generated by an average customer in the cohort. The first data point indicates the order amount per customer in the first analysis period. From then on, the amount displayed indicates the accumulated order amount per surviving customer. Here, we can select the period from which to begin the analysis. The chart continuously extends until the current period.  

In this example, we can see that 33% of the customers in the original group (High Tier Reactivated customers), remained active after twelve months, and accumulated $519 during this time frame.   For further analysis, you have two additional tabs available: Compare Groups and Group Migrations.  

Under the compare groups option, you can create two new groups, load two groups that were previously created as Target Groups, or even the same group in different time frames. The comparison of the customer metric between the groups may result in insightful findings that will help you focus your marketing efforts.  

Let’s look at an example of a retailer: I will load a group of Active customers and compare their main metrics between May 2019 and 2018. First, we can see that the size of this group has significantly increased over the years. Additionally, if we look at some key metrics such as Net Revenue, Total Order Value, Lifetime, and order days, we can see sharp increases in all those metrics. Finally, comparing between groups’ cohorts can provide insights into the differences in survivability and cumulative value over time.  

The group migration tab will help you discover how many customers migrated from one group to another over a specified period. Load two groups you wish to monitor and click generate. For example, let’s understand our reactivation rates over the past six months. I will load the Churn and Reactivated groups to see the percentage of Churned customers who are continuing their activity this year. We can see that 10.6K customers took this migration, which is 14% of the Reactivated group today.  

Using the Customer Explorer frequently will help you become a more data-driven marketer, one who can more effectively engage your customers via highly personalized communications.  

Thank you for watching. If you have any questions, please feel free to contact your Customer Success Manager for further explanation.