Revenue stands out as the most crucial metric for gauging the success of any business. Therefore, it is imperative to analyze the overall revenue generation and its sources. NotifyVisitors offers a suite of diverse tools designed to facilitate a comprehensive examination of your revenue.

This guide will elaborately cover each analytical tool, encompassing filtering options, graph types, various modes of revenue generation (including broadcast campaigns, onsite campaigns, and automated journeys), and the diverse channels contributing to revenue generation.

In addition to monitoring revenue, we will also delve into the analysis of transaction volumes and paying users. Let's embark on this journey.

To begin, navigate to the "Real Impact of NotifyVisitors" dashboard. Here, you'll encounter three distinct tabs dedicated to revenue analysis: "Revenue," "Campaign Revenue," and "Onsite Revenue." 

• The "Revenue" tab provides a holistic overview of revenue generated, incorporating both broadcast and onsite campaigns. 
• The "Campaign Revenue" tab offers insights specifically into revenue generated from broadcast channels such as email, SMS, push notifications, and more.
• The "Onsite Revenue" tab delves into the revenue generated from website popups and personalization.

Tab 1: Revenue

The revenue section gives you a complete summary of the overall revenue, transactions, and paying users generated, along with revenue, transactions, and paying users generated specifically from NotifyVisitors. Moreover, this section showcases the revenue generated from broadcast and journey campaigns such as email, sms, WhatsApp, and push notifications, and revenue from onsite campaigns like website popups.

Before proceeding further into the revenue analytics, make sure your revenue events are mapped. 

Map revenue events

It is essential for the system to know which of your events and their specific attributes are considered as conversion events and based on this our system will show analytics. For instance, a revenue event could be an "order fulfill," and a specific attribute for this event might be "Total price after tax."

To do this, click on the “Map Revenue Event” button in the filter section, and fill in the required information as shown in the image below.

For a detailed understanding of revenue mapping and instructions on how to map revenue events, you can click here.

Once all of your revenue events are mapped, we can proceed towards analytics. Let’s discuss how this works.

Filter section

At first, we have the filter section that provides three distinct options for filtration:

1. Date filter: This allows you to choose a specific date range for analyzing revenue.

2. Choose event: In this dropdown, you’ll see all your mapped revenue events, and you can select a particular event for filtration, and all subsequent graphs will exclusively display revenue generated from the chosen event. Additionally, for more precise analytics, you can specify attributes related to the selected event by clicking on the filter icon right next to choose event dropdown. 

For instance, your "order placed" event has an attribute "Country" with options such as USA, Canada, and India. Now you wish to focus on analyzing revenue from users in the USA. You can accomplish this by selecting "order placed" from the event menu and choosing "Country: USA" from the attribute filter.

3. Graph type: This option allows you to decide the type of graph you want, whether it's a daily, weekly, monthly, or annual comparison of revenue.

After the filter section, we encounter three distinct sections - Revenue, Transactions, and Paying users. Each section presents three graphs - a comprehensive graph, a graph for campaigns, and a graph for onsite campaigns. Now, let's delve into each section and explore them in depth.

Revenue section

1. Total revenue chart

In this section, we begin with a chart that displays the revenue generated in the previous days, weeks, or months, depending on the selected "graph type" filter from the filter section. Above this chart, you will find the Total revenue generated within the chosen date range, as well as the total revenue specifically generated via NotifyVisitors. Additionally, an average of both is also provided.

Within the graph, the total revenue is represented in a dark green color, while the revenue generated through NotifyVisitors is depicted in light green.

2. Chart for total revenue generated from campaigns

As you continue scrolling, you will come across the second chart, which displays the overall revenue generated by campaigns along with a breakdown of the revenue generated specifically from broadcast campaigns and journey campaigns.

The dark green bar represents the total revenue generated from all campaigns, while the light green bar represents the revenue generated solely from broadcast campaigns. Lastly, the blue bar represents the revenue generated exclusively from journey campaigns.

3. Chart for total revenue generated from onsite campaigns

Next, we have a graph showing the overall revenue generated from onsite campaigns. It provides a detailed breakdown of the revenue generated from both onsite broadcast campaigns and onsite journey campaigns.

Transactions section

In addition to analyzing the generated revenue, you can also compare transactions that occurred over previous days, weeks, etc., based on the applied graph filter.

To access transaction details, click on the "Transactions" tab located in the top right corner. Similar to the revenue analysis, all three graphs now compare transaction volumes.

• The first graph illustrates the total number of transactions and the subset of transactions generated through NotifyVisitors. 
• The second graph provides a breakdown of transactions generated specifically through campaigns, distinguishing between broadcast and journey campaigns. 
• The third graph outlines transactions resulting from onsite campaigns, further categorizing them into onsite broadcast campaigns and journey campaigns.

Paying users section

To analyze and compare the unique paying users over the past few days, weeks, or months, click on the "Paying Users" tab. Once you've selected this tab, all three graphs will display information specifically related to unique paying users.

• In the first graph, you'll find details about the total count of unique paying users, along with a breakdown of the total unique paying users through NotifyVisitors.
• Moving on to the second graph, it outlines the total unique paying users originating from campaigns, further categorizing them into broadcast campaigns and journey campaigns.
• The third graph outlines the total unique paying users originating from onsite campaigns, providing a breakdown into onsite broadcast campaigns and journey campaigns as well.

To conduct an in-depth examination of revenue, transactions, and paying users derived from both campaigns and onsite campaigns, we've designated two distinct tabs for a detailed exploration. Let's delve into each of these tabs to unravel comprehensive insights.

Tab 2: Campaigns revenue

Within this tab, we will provide a detailed analysis of the revenue generated from campaigns.

Filter section

Similar to the revenue tab, the initial section is a filter section offering four distinct filtering options:

1. Date filter: Choose your preferred date range to view analytics.

2. Event filter: Select a revenue event and its attributes from all mapped revenue events if you wish to apply a filter specifically for a particular event.

3. Attribution model: Next, choose the type of attribution model for crediting revenue to each channel. There are four options available: simple, last, first, and linear interaction attribution models.

Imagine you've sent three marketing campaigns: an email, an SMS, and a push notification. A user receives all three messages but interacts with them in a specific sequence. In this scenario, the user first clicks on the push notification, then engages with the email, and ultimately places an order worth $45.

This user's sequence started with the push notification, followed by the email interaction, culminating in the successful purchase. Let’s see how each attribution model works in this case:

• In the simple attribution model, the revenue credit is equally given to both the email and push, reflecting the $45 revenue in the analysis of both channels.
• In the last interaction attribution model, all the $45 credit is given to the email, as the customer last interacted with it.
• In the first interaction attribution model, the entire credit goes to the push, as the customer first interacted with it.
• In the linear attribution model, the revenue credit is evenly divided between both channels, i.e., $22.5 to the email and $22.5 to push.

For a deeper understanding of attribution models, you can click here.

4. Split by filter: The "Split by" filter offers a distinctive approach to analysis by splitting data based on a chosen attribute. This differs from the attribute filter for events, where you can specifically filter analysis for a particular condition of an attribute. In the case of the "Split by" feature, you simply select an attribute, and the analysis automatically divides graphs based on various conditions of the chosen attribute.

To illustrate, if you use the attribute filter and select the attribute "Platform" with the condition "includes" "android app," the analysis will exclusively display revenue, transactions, and paying users generated through the Android app. On the other hand, when utilizing the "Split by" feature with the attribute set as "Platform," the analysis will showcase revenue generated from all platforms, with a breakdown of specific platforms, as depicted in the accompanying image.

After filters, we have 3 different options overview, campaign, and journey

Overview section

In this section, you get an overview of the revenue, transactions, and paying users originating from both broadcast and journey campaigns across all channels.

• When navigating the revenue section, the table positioned above the graph provides insights into the total revenue and average revenue derived from all communication channels. This data encompasses revenue generated from both broadcast and journey campaigns.

The accompanying graph illustrates the revenue generated across all campaign channels, offering a split between journey and broadcast campaigns. By default, the graphs display the split between journey and broadcast campaign, but you have the option to apply the split filter to tailor the view according to your preferences.

• Likewise, within the transactions section, a table presents the total and average transactions across all campaign channels. Accompanying this, a graph illustrates transactions across all channels, with a breakdown between campaigns and journeys.

• In the paying users section, both tabular and graphical representations showcase the total count of unique paying users across all campaign channels.

Concluding the overview section, at the bottom, a summary highlights the top 5 performing campaigns. This includes details such as campaign name, channel, mode, along with comprehensive information on total revenue generated, average daily revenue, transactions, paying users, and average revenue per user.

Campaigns section

Following the comprehensive overview, the campaigns section allows you to focus on revenue, transactions, and paying users exclusively generated from all channels of broadcast campaigns.

To access the data, simply choose the channel type, and you'll be presented with related information in a tabular format. This includes details such as campaign name, revenue generated, average daily revenue, transactions booked, paying users, and average revenue per user.

For a graphical representation of any specific campaign's data, click on the corresponding campaign ID, as illustrated in the image below.

To delve into transaction details, click on the transaction number, and you'll access logs specific to that campaign. The search box allows you to find any particular transaction swiftly. The logs table presents key sections such as User ID, Device ID, NV UID, Attribute, Country, and Time. If you desire additional fields, click the "View more fields" filter button at the top right and select your preferences.

To export these logs, click on the "Export Logs" button, input your email address, and submit. A CSV file containing all logs will be sent to your email address.

Journey section

The journey section shows data in tabular form, including Journey ID, name, total revenue, average daily revenue, transactions, and average revenue per user.

You can click on the Journey ID to view this data in graphical form, or you can click on the Journey name to view detailed analytics of this journey, such as sent, delivered, unique open, unique click status, and more.

Tab 3: Onsite revenue

In this section, you can easily access information about the revenue, transactions, and paying users that are generated through website popups.

Firstly, there is a filter section where you can customize your view by selecting options such as date, event, attribute, and graph type. These options function in the same way as described in the revenue tab above.

Following that, you will find a table displaying the total revenue, transactions, and paying users in a clear and organized format. Additionally, there are graphs available for revenue, transactions, and paying users. To view a specific graph, simply choose the desired option, as shown in the image below.

Lastly, there is an overview of all your website popup campaigns presented in a table format. This table provides details such as the popup name and ID, mode, revenue generated, average daily revenue, transactions booked, paying users, and average revenue per user.

If you wish to view any campaign in graphical form, just click on the campaign ID, as we have previously discussed in the campaign revenue section.

Conclusion

In this article we have covered an array of powerful tools providing a robust platform for comprehensive revenue analysis. We walked through each analytical tool, ranging from filtering options to diverse graph types and modes of revenue generation, offering a thorough exploration of the channels contributing to revenue.

By analyzing not only the overall revenue but also transactions and paying users, businesses can gain actionable insights into their financial performance. The dashboard's intuitive design, coupled with detailed breakdowns of campaigns and onsite campaigns, ensures a subtle understanding of revenue sources.

This guide is a roadmap for businesses looking to harness the full potential of revenue analytics, offering insights and practical steps to unlock meaningful data-driven decisions.