CUSTOMER SEGMENTATION

Customer segmentation is the module to analyse the different information stored in the platform organised by the different segments created.

1. Introduction

This module has been designed to analyze the different information saved in the platform organized by the different segments created. Every segment is a rule applied to all the databases which divides and groups the information into different characteristics:

• Customer: Groups the information by the profile of the client, the number of tickets sold, the evolution of the client purchases,...

• Shop: Allows to evaluate the information sorted by location.

• Product: Enables the analysis of the purchase sorted by the type of products.

• Ticket: Gives the possibility to group the information by the price and other values related to the ticket data.

It is very important to set the period of time to analyze the information with the desired perspective.

Advanced SQL

In this section you can write the SQL query you want to query the data freely.

2. Customer Segmentation

1. How to create a new segment

It is important to have a specific logic to create every segment. First, you have to fill in the general information by giving a name that can be easily recognized and linked to the limitation desired to create with the segment.

After that, it is necessary to understand the logic wanted to send by the segment using the rules and then add them one by one according to the different categories: customers, products, shops, and tickets.

Finally, it is very important to combine them depending on if you want them to cooperate (using AND) or if you want to differentiate between one condition or the other (using OR).

2. List of segments

Once you have created the different segments, it is important to make sure that they are active. In the list of segments created you need to be aware of all the information given so that you can make sure it is working correctly.

• ID: It goes by numbers so it can be easily recognized.

• Name: It should be representative of the information used in the segment.

• Time since the last refresh: Allows to see the days since it has been applied.

• Status: It can be Active or Inactive.

• Customer: Number of customers with the characteristics applied in the segments.

• Subscribed: In the number of customers of the segment, how many of them are subscribed to get information.

In order to ensure that the rules in the segment are working, it is necessary that the green ACTIVE button is shown.

3. Actions in a segment

• See details: It is linked to the General Statistics of the segments.

• Edit: It allows editing the segment to add or delete some rules.

• Refresh: It refreshes the platform so all the customers of the segment are updated.

• Delete: To delete a segment.

• Create Campaign: It is linked to the Email Campaign Automations, a module used to be aware of the information related to the campaign. (Emails sent, clicked, opened,...)

• Add user values to the group: Define a new value to the selected segment.

• CSV: it allows the user to download the archive with all the data used to classify the different customers segments into a CSV file.

3. Data Visualization

This section of the platform allows for a more visual understanding of the information based on all important aspects of each segment.

1. General Statistics

This function allows to see the difference between each KPI (Number of Active Clients, Number of Tickets, Total Revenue,...) for every segment made. This module is very useful to see de different impacts and compare the results.

It also gives an overview of the general statistics of the selected segment that allows having a more global vision of the data and how it relates to the different categories.

2. Evolution

It shows the evolution of active customers, total sales, number of tickets, and average tickets of the customers analyzed during the established period of time.

To add information about the data used in the graphics, a table it is also shown with the main data of the aspects analyzed.

3. Purchase moment

This section shows the average distribution of the number of tickets between the different days of the week and distributed by hours.

4. Products top 100

The propensity described in the following graphics responds to the percentage of customers who have purchased this product during the established period of time.

• Top revenue products: products with the highest turnover during the analyzed period.

• Top units sold products: products with the most units sold during the analyzed period.

• Top products with the highest repeat: products with the highest repeat purchase per customer during the analyzed period.

• Top unique users products: products with the most unique customers during the analyzed period.

For each category, the module creates a graphic with the desired products and it provides a table with all the data gathered to provide the information.

5. CLV Distribution

The information shown is the distribution of the number of active customers during the analyzed period, classified by segments based on the Customer Lifetime Value score. The longer the period analyzed, the more profiles at risk or hibernation profiles will appear.

To find more information about the Customer Lifetime Value Segmentation: Customer Lifetime Value Segments

6. GDPR

This section analyzes the volume of customers who have agreed to receive commercial communications in a specific segment. This information must be ingested in the subscribed field of the client.

Remember that you can only send commercial communications to those customers and registries that have agreed to receive commercial communications.

7. Demographic

The following graphics show the demographic data within a segment, sorted into different categories that give essential information about the customers:

• Age distribution: Number of customers organized by age groups. If you want to group by different bands you can create the segments in the segmentation section.

• Stores distribution: stores or sales channels with the greatest weight among the customers analyzed during the selected period. Below is the number of customers for each of the stores or sales channels.

• Cities distribution: cities with the greatest weight among the clients analyzed during the selected period.