Omnichannel Dashboard Guide
In this guide, we will explain how to use the Omnichannel Dashboard, a powerful tool that allows you to manage all your marketing channels in one place. With the Omnichannel Dashboard, you can easily create, manage and analyze campaigns across different channels such as email, SMS, push notifications, social media, and more.
To be able to view the Dashboard, it is important that the user has the following attributes:
Superadmin: has similar permissions as the Admin but also has the ability to access this Dashboard.
FullCommerce Viewer: has similar permissions as the Viewer but also has access to this new screen.
The data is presented in different modules within the Dashboard. Initially, the information about Unique Customers, Tickets, Average Ticket, and Total Sales is displayed.
Choose:
Period: Here it is possible to modify the time range that you want to make visible. It is initially preset to view the last 30 days.
Store: Allows you to filter according to the nomenclature of the stores that are established by each client.
Unique / Recurring Customers (RCR): Any person who has made a purchase in the indicated period of time, to whom the originating system has linked us through the same ID of its own system. Here we do not rely on the Email to consider it a user, but directly on the source systems that are identifying an ID. For example, a document number). Through such ID, these clients are counted.
Purchase Frequency (PR): measures the number of times a customer makes a purchase during a given period of time.
Customer Value (CV): Calculates the economic value that a customer provides throughout their life cycle as a customer. This involves taking into account both the individual purchase value (Average Ticket) and the purchase frequency over time.
Average Ticket: This is the average amount that customers spend.
Total Sale: It is the total of sales made before shipping (the shipping cost is not included in the total).
Tickets: The total of purchase tickets with a positive amount that were produced in the period and in the store that have been predetermined.
The download button is located in each panel to export the content. If downloaded in Excel, the data that generates the presented graphs will be obtained.
The first module displayed is Customers per Day, which shows the daily evolution of customers.
The same occurs in the Sales by Date section, which indicates the daily evolution of purchases.
In the third graph, Average Sales by Store, the total number of tickets is shown along with the average ticket of each store in a specific time period.
Moreover, the graph allows you to know the particular information of each store, which favors the comparison of the indicated data.
Next, the graph of Total Sales by Store = Tkt is presented, which seeks to provide the same comparison, but considering the total sales for each store. The goal is to analyze the total sale compared to the number of tickets in particular.
Then, the Sales by Store, Sales by Category, and Sales by Product modules are displayed.
It seeks to give quick visibility to how billing is distributed across stores, as a percentage representation to see which are the stores that bill the most. For example:
It is a scatter plot that seeks to visually reflect more than one metric. In this case, the product category, the total amount invoiced in that category, and finally, the total number of products sold are presented. Here, the "Y" graph will represent billing (how much is invoiced), while the size of the bubble will show the number of products sold in that category.
Within the graph, it is possible to include the top 20 categories that have billed the most.
It is a scatter plot that seeks to visually reflect more than one metric. In this case, the products, the total amount invoiced from sales of that type of product are presented. Here, the "Y" graph represents the billing (how much is invoiced), while the size of the bubble shows the number of products sold in that product line.
This allows for a more detailed analysis and includes the total of all categories, stores, and products. There are no limits, and you can create your graphs and tables with all the information.
To be able to view the Dashboard, it is important that the user has the following attributes:
Superadmin: has similar permissions as the Admin but also has the ability to access this Dashboard.
FullCommerce Viewer: has similar permissions as the Viewer but also has access to this new screen.
Informative Modules
The data is presented in different modules within the Dashboard. Initially, the information about Unique Customers, Tickets, Average Ticket, and Total Sales is displayed.
Choose:
Period: Here it is possible to modify the time range that you want to make visible. It is initially preset to view the last 30 days.
Store: Allows you to filter according to the nomenclature of the stores that are established by each client.
KPIs
Unique / Recurring Customers (RCR): Any person who has made a purchase in the indicated period of time, to whom the originating system has linked us through the same ID of its own system. Here we do not rely on the Email to consider it a user, but directly on the source systems that are identifying an ID. For example, a document number). Through such ID, these clients are counted.
Purchase Frequency (PR): measures the number of times a customer makes a purchase during a given period of time.
Customer Value (CV): Calculates the economic value that a customer provides throughout their life cycle as a customer. This involves taking into account both the individual purchase value (Average Ticket) and the purchase frequency over time.
Average Ticket: This is the average amount that customers spend.
Total Sale: It is the total of sales made before shipping (the shipping cost is not included in the total).
Tickets: The total of purchase tickets with a positive amount that were produced in the period and in the store that have been predetermined.
Charts and Metrics
The download button is located in each panel to export the content. If downloaded in Excel, the data that generates the presented graphs will be obtained.
The first module displayed is Customers per Day, which shows the daily evolution of customers.
The same occurs in the Sales by Date section, which indicates the daily evolution of purchases.
In the third graph, Average Sales by Store, the total number of tickets is shown along with the average ticket of each store in a specific time period.
Moreover, the graph allows you to know the particular information of each store, which favors the comparison of the indicated data.
Next, the graph of Total Sales by Store = Tkt is presented, which seeks to provide the same comparison, but considering the total sales for each store. The goal is to analyze the total sale compared to the number of tickets in particular.
Then, the Sales by Store, Sales by Category, and Sales by Product modules are displayed.
Sales by Store
It seeks to give quick visibility to how billing is distributed across stores, as a percentage representation to see which are the stores that bill the most. For example:
Sales by Category
It is a scatter plot that seeks to visually reflect more than one metric. In this case, the product category, the total amount invoiced in that category, and finally, the total number of products sold are presented. Here, the "Y" graph will represent billing (how much is invoiced), while the size of the bubble will show the number of products sold in that category.
Within the graph, it is possible to include the top 20 categories that have billed the most.
Sales by Product
It is a scatter plot that seeks to visually reflect more than one metric. In this case, the products, the total amount invoiced from sales of that type of product are presented. Here, the "Y" graph represents the billing (how much is invoiced), while the size of the bubble shows the number of products sold in that product line.
Table of sales by store/category/products
This allows for a more detailed analysis and includes the total of all categories, stores, and products. There are no limits, and you can create your graphs and tables with all the information.
Updated on: 11/29/2023
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