Once you launch a *Web In-line campaign* through the channel or a journey, its impact on user engagement and revenue can be analyzed in detail through *Campaign Overview.* ## How to Access Let's quickly show you how you can access campaign analysis through the various sections of your dashboard: **Access Campaign Overview through List of Campaigns** A list of all the campaigns created through the channel can be found under the *[List of Campaigns](web-in-line-hub) section* or the central hub of *Web In-line Content.* You can click on the (hyperlinked) *Campaign’s Name* to analyze a campaign in further detail. **Access Campaign Overview through Segment Overview** A list of all the campaigns sent to a segment of users can be found under *Campaigns* in the *\[segment's overview]\(analysing-segments #section-4-campaigns) section.* You can click on the (hyperlinked) *Campaign Name* to analyze the *Web In-line campaign* in further detail. Before we dive into campaign analysis, let's get you acquainted with the top panel. ## Understanding Top Panel {1436}

Click to enlarge As highlighted above, the top panel provides a quick snapshot of the campaign with key details like: * **Campaign Name:** As defined at *[Step 1: Audience](creating-web-personalization-campaigns#step-1-define-the-audience),* while creating the campaign. * **(Target) Audience:** Indicates the segment targeted by the campaign, as defined at *[Step 1: Audience](creating-web-personalization-campaigns#3-define-target-audience-show-to),* while creating the campaign. * **(Campaign)[Type](campaigns#section-campaigns):** Given the highly targeted nature of Web In-line, all campaigns created through the channel are considered to be *[Triggered campaigns](campaigns#section-triggered-campaigns)* by default. * **(Campaign)[Status](campaigns#section-campaign-status):** Indicates the current status of the campaign. * If you save a campaign without launching it, then it will be saved as a draft. The same is reflected by the status, *Draft.* Now, let's get you acquainted with its features: ### Modify Campaign As shown below, using the icons placed next to the *Campaign's Name,* you can choose to *Edit, Delete, Duplicate, Pause/Resume* and *Tag* the campaign, as per your marketing needs. {1425}

Click to enlarge * Once you *Pause* a *[Running](campaigns#section-running)* campaign, you can choose to *Resume* it's delivery to the target audience by clicking the *Resume icon,* as highlighted above. > 🚧 Related Read: Modifying Web In-line Campaigns > > [A step-by-step guide](analyzing-web-in-line-campaigns#modify-campaign) on how you can modify *Web In-line campaigns.* ### Download a Report > 🚧 Please Note > > Reports can be downloaded only by *Admins* who have access to *Account Management.* If you are unable to do so, then please get in touch with the account owner. You can easily download a copy of campaign stats for offline analysis, upload it to another platform, and so on. Here's how you can go about it: {1425}

Click to enlarge **Step 1: Click the*Download icon*** As shown above, in doing so, you will be prompted to specify the kind of data you want to analyze. **Step 2: Specify the time frame for which the stats should be shown*(Data to Include)*** As shown above, you can specify the time frame as: * *Campaign's Start Date till Present Date* * *Custom Dates* > 🚧 Detailed Read > > * How *[Data to Include](schedule-reports#section-data-to-include)* works > > * *[Understanding Report Stats](schedule-reports#section-understanding-report-stats)* **Step 3: Select Data Breakdown** In this dropdown you can choose from 4 options i.e. \_Day-wise, Week-wise, Month-wise, and 30 days rolling window. \_Each of these have been explained below. * **Day-wise**: When you choose the day-wise option the report that will be sent to you will have an aggregate of each day i.e. each row will consist of each campaign or variations day wise statistics.\ For example, if you want to download a report for the last 7 days, by choosing the day wise data breakdown option, you will receive a report with 7 rows each containing data for each of the days. * **Week-wise**: When you choose this option the report will consist of the aggregate of each week, i.e. Every row will contain the week-wise statistics for each campaign or variation, with each week defined from Monday to Sunday.\ For instance, if you download a report for the past 1 month on the 30 Nov ’23 , and set the data breakdown option to week-wise, then you will receive a report that consists of 5 rows of aggregated data for each week starting from Monday to Sunday. Whereas, if a month starts during the middle of the week, for example the 1st of the month is on a Thursday, then the row for the first week will be from Thursday to Sunday. * **Month-wise**: On choosing this option your statistics will be aggregated for each month i.e. each row will consist of month wise statistics. (calendar month will be considered)\ If you wish to download a report spanning from January 1st to November 30th and select the month-wise data breakdown option, you will receive 11 rows. Each row will contain aggregated data for each respective month, providing a comprehensive breakdown of information (based on calendar month). * **30 day (Rolling window)**: On choosing this option, your statistics will be aggregated based on every 30 days irrespective of the calendar month, i.e. Every row will include the rolling window statistics for each campaign or variation for up to 30 days.\ For example, if you choose a date range from 15th June to 14th August, two rows will be generated i.e. 15th June to 14th July and 15th July to 14th August. **Step 4: Specify Level of Data Granularity** You can [create up to 5 versions of a campaign](campaign-variations-testing#section-variations) and test each one to determine the copy that resonates with your audience. Thus, while downloading a report, you can choose to analyze *consolidated campaign stats OR a variation-wise breakdown.* As shown above: * Select **Campaign Level Granularity** to view the average performance of all campaign variations. * Select **Variation Level Granularity** to view the individual performance of each variation and identify the top-performing version. **Step 5: Click Download** ### Schedule a Report In our quest to make campaign reporting a seamless experience, we built *Scheduled Reports -* customizable and automated reports delivered straight to your inbox!

Click to enlarge As highlighted above, using the *Reporting icon,* you can set up a periodic report to have all the stats shown under *[Overview](analyzing-web-in-line-overview),* emailed to multiple team members. **Here's how you can go about it:** {1425}

Click to enlarge **Step 1: Click on the*Reporting icon*** * As shown above, in doing so, you will be prompted with a pop-up allowing you to customize the report's settings. * The report's name will be the same as the campaign's name. This will be included in: * The emailed report's subject line * under the *[Scheduled Reports](schedule-reports#section-how-to-schedule-channel-reports)* section of your dashboard **Step 2: Define the*Frequency* of report delivery** You can choose to have a report delivered on a *Daily, Weekly, or Monthly* basis, as per your needs. * As shown above, we have selected *Weekly* as the *Frequency* for our report. **Step 3: Specify the time of delivery*(When)*** As shown above, you can choose to have the report delivered at a specific time of the day. The same can be defined in an AM/PM format against the field, *When.* **Step 4: Specify the time frame for which the stats should be shown*(Data to Include)*** Too many data points always lead to chaotic insights. This is why we have made it possible for you to specify the time frame (number of days) for which you'd like to receive campaign stats. {1425}

Click to enlarge As shown above, you can select a time frame between *Last 1 Day* to *Last 30 Days\_against \_Data to Include.* > 🚧 Detailed Read > > * How *[Data to Include](schedule-reports#section-data-to-include)* works > > * How a [combination of *Data to Include* & *Frequency* determine the date of stats](schedule-reports#section-understanding-the-date-of-report-stats) included in the report > > * *[Understanding Report Stats](schedule-reports#section-understanding-report-stats)* **Step 5: Select option for Data Breakdown** In this dropdown you can choose from 4 options i.e. Day-wise, Week-wise, Month-wise, and 30 days rolling window. Each of these have been explained below. * Day-wise: When you choose the day-wise option the report that will be sent to you will have an aggregate of each day i.e. each row will consist of each campaign or variations day wise statistics. * Week-wise: When you choose this option the report will consist of the aggregate of each week, i.e. Every row will contain the week-wise statistics for each campaign or variation, with each week defined from Monday to Sunday. * Month-wise: On choosing this option your statistics will be aggregated for each month i.e. each row will consist of month wise statistics. (calendar month will be considered). * 30 day (Rolling window)- On choosing this option, your statistics will be aggregated based on every 30 days irrespective of the calendar month, i.e. Every row will include the rolling window statistics for each campaign or variation for up to 30 days. For example, if you choose a date range from June 15th to August 14th, two rows will be generated i.e. June 15th to July 14th and July 15th to August 14th. Click [here](https://knowledgebase.webengage.com/docs/schedule-reports#use-case-for-each-data-breakdown-option) to know more about the use cases of each. **Step 6: Select Level of Data Granularity** At WebEngage, you can [create up to 5 versions of a campaign](campaign-variations-testing#section-variations) and test each one with a small test segment to determine the copy that resonates with your audience. Thus, while scheduling a report, you can choose to analyze *consolidated campaign stats OR a variation-wise breakdown.* As shown below: * Select **Campaign Level Granularity** to view the average performance of all campaign variations. * Select **Variation Level Granularity** to view the individual performance of each variation and identify the top-performing version. {1425}

Click to enlarge **Step 7: Add team members as*Subscribers*** As shown above, you can choose from a list of all the team members who have access to your WebEngage dashboard. Doing so will ensure that all the added users receive the campaign performance report as per its settings. * However, if a team member is not a part of your WebEngage account, then you will not be able to add them as a *[Subscriber](schedule-reports#section-subscribers).* **Step 8: Click the*Schedule* button** * A notification will pop-up on the bottom right corner of the page, confirming that the report has been scheduled. *([Understanding Report Stats](schedule-reports#section-understanding-report-stats))* * You can modify or delete the report anytime you like through the *[Scheduled Reports](schedule-reports#section-how-to-modify-a-scheduled-report)* section, nested under *Settings* in your account. > 🚧 Please Note > > * Reports can be configured by only those *Admins* that have access to *Account Management.* Please get in touch with the account owner if you're unable to do so. > > * You can schedule additional reports for campaigns sent through *Web In-line Content* and all the channels combined through *[Settings > Scheduled Reports](schedule-reports#section-how-to-schedule-channel-reports)* in your dashboard. ### See Campaign Details As demonstrated below, clicking *Show Details* reveals a thorough recap of all the rules and settings specified while creating the campaign. This section can be minimized by clicking *Hide Details.* {1425}

Click to enlarge Here's a list of all the details shown here: * **Campaign ID:** A unique campaign ID is generated by us each time you create a campaign. * **Start Date:** Indicates the date-time on which the campaign was launched or will be launched (if the campaign is still scheduled), as specified at *[Step 2: When](creating-web-personalization-campaigns#step-2-specify-when--where-to-show-the-campaign),* while creating it. * **End Date:** Indicates the date-time on which the campaign will end or ceased getting delivered. An *End Date* can be specified at *[Step 2: When](creating-web-personalization-campaigns#step-2-specify-when--where-to-show-the-campaign)* while creating it. * **Tags:** Tags is a handy feature that helps you categorize your campaigns as per their purpose, target audience, frequency, or any other parameter that makes them easier to search for. * *If a campaign is tagged:* All the tags added to the campaign will be shown here. You can click on a hyperlinked tag to add more tags to the campaign or remove existing ones. * *If no tags have been added:* As shown below, you can click on *Add Tags* to create new tags for the campaign and add existing ones to it. {1425}

Click to enlarge * **Audit Logs:** Click *View* to access a history of all the interactions your account admins have had with the campaign. Each action, including campaign creation, message edits, and changes made to its settings is recorded here. *[(Detailed Read)](audit-log)* * **[Conversion Event](conversion-tracking#section-conversion-event):** Indicates the action users are expected to perform on your app after receiving the campaign, as specified at *[Step 4: Conversion Tracking](creating-web-personalization-campaigns#step-4-conversion-tracking),* while creating it. * If you have not set up *Conversion Tracking* for the campaign, then the same will be indicated by the status *Disabled,* here. * **[Conversion Deadline](conversion-tracking#section-conversion-deadline):** Indicates the duration till which the campaigns' *Conversion Event* will be tracked after it has been delivered. The same can be specified at *[Step 4: Conversion Tracking](creating-web-personalization-campaigns#step-4-conversion-tracking)* while creating the campaign. * If you have not set up *Conversion Tracking* for the campaign, then the same will be indicated by the status *Disabled,* here. * **Control Group:** Indicates whether or not a *[Control Group](control-groups)* was set up to measure the campaign's effectiveness against organic user behavior at *[Step 4: Conversion Tracking](creating-web-personalization-campaigns#step-4-conversion-tracking),* while creating it. The same is indicated by: * Enabled * Disabled * **Send Winner:** Indicates whether or not automated *Variation* testing was configured for the campaign while creating it, by setting up *[Send Winning Variation Automatically](campaign-variations-testing#section-send-winning-variation-automatically-how-it-works)* at *[Step 4: Conversion Tracking](creating-web-personalization-campaigns#step-4-conversion-tracking).* The same is indicated by: * Enabled * Disabled * If *Send Winner* is enabled, then the following details will help you out with a quick recap of the rules of testing: * **[Size](campaign-variations-testing#section-size-of-test-audience):** Indicates the size of test audience with which all the \_[Variations](campaign-variations-testing#section-variations) \_of the campaign will be tested with to determine a winning variant. * **[Time to Test](campaign-variations-testing#section-time-to-test):** Indicates the duration for which all the *Variations* of the campaign will be tested for. * **[Win Criteria](campaign-variations-testing#section-win-criteria):** Indicates the parameter *(Impressions, Clicks, Conversions)* based on which a winning *Variation* will be determined. * **Created By:** Indicates the name of the user who created the campaign. * **Created On:** Indicates the date-time on which the user started creating the campaign. * **Last Edited By:** Indicates the name of the user who has made the most recent edit to the campaign. * **Last Edited On:** Indicates the date-time on which the most recent edit was made to the campaign. Now, let's show you how you can analyze a *Web In-line Campaign* to gain valuable insights into its impact on user engagement, conversions, and revenue. ## How to Analyze > 🚧 Must Read > > Please refer to *[Campaign and Channel Performance Indicators](channel-campaign-performance-metrics#web-personalization)* for a complete list of all the user-channel interactions tracked for *Web In-line campaigns.* As shown above, using the navigation bar, you can toggle between the following sections to slice-and-dice data and analyze multiple aspects of the campaign: * **[Overview](analyzing-web-in-line-campaigns#overview)** * **[Analyze](analyzing-web-in-line-campaigns#analyze)** * **[List of Users](analyzing-web-in-line-campaigns#list-of-users)** ### Select a Time Frame of Analysis > 🚧 Please Note: You Cannot Analyse 'List of Users' for a Specific Duration > > *[List of Users](analyzing-web-in-line-campaigns#list-of-users)* includes campaign engagement details for all the users that have been a part of the campaign's target audience throughout its lifetime. By default, all stats under *[Overview](analyzing-web-in-line-campaigns#overview)* and *[Analyze](analyzing-web-in-line-campaigns#section-analyze)* are shown from the campaign's *Start Date* till present day (if the campaign is [running](campaigns#section-running)) or till *upto 45 Days from the campaign's End Date* (if the campaign has [ended](campaigns#section-ended)). {1430}

Click to enlarge > 📘 Using the date range filter, you can analyze the campaign's performance for the following durations: > > * Today > > * Yesterday > > * Last 30 Days > > * Last 90 Days > > * Custom Dates (as selected by you) Now, let's dig into each section, starting with *Overview.* ## Overview Here you can analyze the campaign's performance from its *Start Date* till the present day (if the campaign is [running](campaigns#section-running)) or *till 45 days from the campaign's End Date* (if the campaign has [ended](campaigns#section-ended)), against key performance indicators like *Impressions Clicks, Conversions, Revenue* for each *[Variation](campaign-variations-testing#section-variations).* You can alter this by selecting a [time frame](analyzing-web-in-line-campaigns#select-a-time-frame-of-analysis). ### 1. Performance Overview This subsection presents a summary of the campaign against key performance indicators like *[Unique Impressions](analyzing-web-in-line-campaigns#unique-impressions), [Unique Closes](analyzing-web-in-line-campaigns#unique-closes), [Unique Click](analyzing-web-in-line-campaigns#unique-clicks), [Unique Conversions](analyzing-web-in-line-campaigns#unique-conversions),* and *[Revenue](analyzing-web-in-line-campaigns#revenue).* {1439}

Click to enlarge Let's go over each card: #### Unique Impressions Indicates the users that have seen the *Web In-line Banner* at least once within the selected [time frame](analyzing-web-in-line-campaigns#select-a-time-frame-of-analysis) during a session. #### Total Impressions Indicates the total number of times users viewed the *Web In-line Banner* on the web browser within the selected [time frame](analyzing-web-in-line-campaigns#select-a-time-frame-of-analysis). #### Unique Clicks * **Unique Click Rate:** Calculated against the number of *Impressions* it indicates the share of users that clicked on the *Web In-line Banner* within the selected [time frame](analyzing-web-in-line-campaigns#select-a-time-frame-of-analysis). * The card also indicates **the number of users that clicked on the*Web In-line Banner*** helping you gauge the campaign's true impact on user engagement. #### Unique Conversions * **Unique Conversion Rate:** Calculated against the total number of messages *Delivered,* it indicates the share of users that performed the campaign's *[Conversion Event](conversion-tracking#section-conversion-event)* at least once within the selected [time frame](analyzing-web-in-line-campaigns#select-a-time-frame-of-analysis). * The card also indicates **the number of users converted through the*Web In-line Banner,*** helping you draw a fair comparison between the number of users who clicked on the campaigns, users who converted, and the total *[Revenue](analyzing-web-in-line-campaigns#section-revenue).* {1439}

Click to enlarge > 👍 Comparing the number of Clicks - Conversions and Total Revenue of an Web In-line Campaign > > For example, a quick look at the campaign's *Performance Overview* above reveals that contrary to the popular assumption, there is no correlation between the number of users who clicked on the banner (4,353) and those who made the purchase (8,109). > > * This is an extremely valuable insight as it could mean that most of these users had a high intent to purchase and would have booked the hotel stay irrespective of the offer. > > * The hypothesis can easily be tested by adding a *Control Group* to the campaign's target audience and comparing the conversion rate of the two groups (since control group users will not receive the campaign). > 🚧 Please Note > > *Conversions* will not be tracked for your campaign if you have not specified a *[Conversion Event](conversion-tracking#section-conversion-event)* at *Step 4: Conversion Tracking,* while creating it. > > *[Detailed Read on how Conversion Tracking works.](conversion-tracking)* * **Compare Unique Conversions with Control Group:** If you have enabled *[Control Group](control-groups)* while creating the campaign, then as highlighted below, you will be able to draw a scientific comparison between the campaign's conversion rate and the organic conversion rate - helping you understand its true impact. {1438}

Click to enlarge > 👍 Analyzing Web In-line Campaign's Conversion Rate against Control Group > > For example, in the above visual, we have analyzed the conversion rate of a Web In-line campaign nudging users to search for summer clothing under the clothing section. > > The campaigns *Performance Overview* reveals that: > > * *Unique Conversion Rate of the campaign: 2.38%* > > * *Unique Conversion Rate of CG: 2.26%* > > * *Conversion Uplift: 5.30%* (negative uplift is always indicated in red) > > Clearly, the *Web In-line campaign* performed well but did not contribute majorly against the organic conversions. Thus, it's always advisable to test your campaigns with a *Control Group* before sending it to the entire target audience to ensure that it helps amplify the desired user behavior, and not hinder it. Here's how you can [automate *Variation* testing with a *Control Group*](campaign-variations-testing#section-send-winning-variation-automatically-concepts). **Now, let's walk you through the metrics shown here:** * **Unique CG Conversions:** Calculated against the total number of users included in the *Control Group (CG),* it indicates the share of users that performed the campaign's *[Conversion Event](conversion-tracking#section-conversion-event)* at least once within the selected [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis), even though they didn't receive the campaign. * **Conversion Uplift:** Indicates the percentage increase or decrease in the *Unique Conversion rate* achieved by the campaign, calculated against the *Unique Conversion rate* of the *Control Group.* * It's shown beside the campaign's *Unique Conversion rate,* in *Green for positive uplift,* in *Red for negative uplift,* and *in Grey for no uplift.* > 👍 Fact Check > > *[Conversion Uplift](control-groups#section-conversion-uplift)* equals *[(Unique Conversion Rate of Campaign/ Unique Conversion Rate of CG) x 100]- 100* #### Revenue Indicates the amount of *Revenue* tracked for the *[Total Conversions](channel-campaign-performance-metrics#section-total-conversion)* that have occurred within the selected [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis). * *Revenue* numbers are always shown in the currency selected by you while setting up *[Revenue Mapping](revenue-mapping)* > 📘 Please Note > > *Revenue* will not be tracked for your campaign if: > > * You have not set up *[Revenue Mapping](revenue-mapping)* for your account > > * You have not specified a *Conversion Event* while creating the campaign > > * You have not mapped the specified *[Conversion Event](conversion-tracking#section-conversion-event)* as a *[Revenue Event](revenue-mapping#section-step-2-select-revenue-event)* in your account > > *Detailed Read on how[Revenue tracking works](revenue-tracking) and how [Revenue is attributed](conversion-tracking#section-impact-of-conversions-on-revenue-attribution) to a campaign.* ### 2. Variation Comparison {1440}

Click to enlarge The most insightful section of *Campaign Overview, Variation Comparison* helps you analyze the impact of each message *[Variation](campaign-variations-testing)* against key metrics like their *Layout* (each *Variation* can have a different template), *Dismisses, Clicks, Conversions, Revenue* and so on, for the chosen [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis). However, depending on the settings specified while creating the campaign, the stats shown here may vary. Let's get you acquainted with the impact of each: > 🚧 > > **Skip to:** How to [customize & analyze](analyzing-web-In-line-campaigns#section-step-1-customize-table) this section as per your strategic needs. ### 2.1. When Variation Distribution is Set Manually If you have [manually specified the audience size](campaign-variations-testing#section-set-variation-distribution-manually) for all the *Variations* at *Step 4: Conversion Tracking,* then using this section, you will be able to identify a winning *Variation* by analyzing the performance of each. The following aspects of the test will be highlighted here: * Percentage of the audience targeted by each *Variation* (calculated against the total target audience) * The performance of each *Variation* against all the performance indicators tracked for *Web In-line campaigns*. Let's demonstrate a short use-case to show you how it works: > 👍 Analyzing Manual Variation Distribution Test Results > > Let's take the example of an e-commerce app. In the visual below, we have analyzed the performance of a Web In-line campaign offering free shipping to all users who have added products to their cart but have not completed checkout yet. > > Marketers of the app wanted to compare the effectiveness of different variations in the message on the banner and thus, created three versions of the campaign. > > * [Variation A](campaign-variations-testing#section-variations) > > * [Variation B](campaign-variations-testing#section-variations) > > * [Variation C](campaign-variations-testing#section-variations) > > Both variations consisted of the same message and were equally divided amongst the target audience. > > **End Goal:** Identify which message style leads to higher conversions > > **Key takeaways:** > > * *Variation C* emerges as the winner as it resulted in maximum *Unique Conversions - 6.08%,* contributing *$ 295,435* in *Revenue!* While *Variation A* achieved a *Unique Conversion rate* of *5.4%,* contributing *$1,7,896* in *Revenue* and *Variation B* achieved a *Unique Conversion rate* of *4.84%,* contributing *$166,476* in *Revenue.* {1439}

Click to enlarge > 🚧 > > **Skip to:** How to [customize and analyze](analyzing-web-In-line-campaigns#section-step-1-customize-table) this section as per your strategic needs. ### 2.2. When Send Winning Variation is Enabled If you have enabled *[Send Winning Variation Automatically](campaign-variations-testing#section-send-winning-variation-automatically-concepts)* at *Step 4: Conversion Tracking* while creating the campaign, then you will be able to analyze the test results through this section. As shown below, the following aspects of the test will be highlighted here: * The winning variation (which was automatically sent as the only campaign to the entire target audience) * The performance of each *Variation* that was tested with the test audience. {1439}

Click to enlarge For example, in the above visual, we can see that *Variation C (6.08%)* achieved a higher *Unique Conversion rate* and was determined the winner. This is because *Clicks* was selected as the *[Win Criteria](campaign-variations-testing#section-win-criteria)* of the test while setting it up, and *Variation C (3.28%)* achieved a higher *Unique Click Rate\_than \_Variation A (2.47%).* and *Variation B (2.72%)*. Similarly, you too can select a custom *Win Criteria* for testing the *Variations*, as per your business goals. > 🚧 > > **Skip to:** How to [customize and analyze](analyzing-web-in-line-campaigns#section-step-1-customize-table) this section as per your strategic needs. ### 2.3. When Control Group is Enabled If you enabled Universal Control Group or added a custom/campaign control group while creating the campaign, you can compare the performance of your campaign against each Variation created. You can compare each *Variation's* conversion rate with the *[Control Group's](control-groups)* organic conversion rate to determine its true impact on driving conversions. This can be done by enabling *Control Group* at *Step 4: Conversion Tracking* while creating the campaign. As shown below, the extent to which a *Variation* influences users to perform the *[Conversion Event](conversion-tracking#section-conversion-event)* can be measured through its *[Conversion Uplift](control-groups#section-conversion-uplift),* indicated alongside *Total Conversions* and *Unique Conversions,* in *Green, for positive uplift,* in *Red, for negative uplift* and in *Grey for no uplift.* {1440}

Click to enlarge > 👍 Analyzing Variations Against Control Group through Conversion Uplift > > In the above visual, we have analyzed *1 Variation* of a *Web In-line campaign* and a *Control Group* against *Unique Impressions, Unique Clicks, Unique Conversions* and *Revenue.* > > * Since *Control Group* was enabled for the campaign, *Unique Conversion rate* was the sole *[Win Criteria](campaign-variations-testing#section--1-conversions-)* used to identify a winner version of the message. > > **On comparing the*Variation* against the *Control Group* we learn that:** > > * Users who did not receive the campaign as a part of the *Control Group* converted at a rate of 2.08%, contributing *$324,114 in Revenue.* > > * *Variation A* influenced the maximum number of users to convert, achieving a *Conversion Uplift of 2.25%* and contributing a whopping *$345,689 in Revenue!* > > * As a result, *Variation A* was identified as the winner and was sent to the entire target audience of the campaign (indicated by the test results highlighted above). > 🚧 Related Reads > > 1. Why you should (almost) always use a [Control Group](control-groups) to measure the effectiveness of your campaign and how it works. > > 2. How you can test *Variations* with a smaller test audience and [automatically send the winner](campaign-variations-testing#section-send-winning-variation-automatically-concepts) to the entire target audience of the campaign! **Now, let's show you how you can slice-and-dice this section to gain relevant insights:** #### Step 1: Customize Table {1439}

Click to enlarge By adding values to the query bar highlighted above, you can choose to analyze *Variations* split by the *Browser, Device Type, and OS* or vice-versa, against all the performance indicators tracked for *Web In-line campaigns.* In doing so, you will be shown a secondary view of each *Variation,* split by the selected option, helping you analyze user-campaign interactions in further detail, as shown below. {1154}

Click to enlarge > 📘 The following options can be selected under both the fields: > > **Variations:** Helps you compare the performance of all the *[Variations](campaign-variations-testing#section-variations)* for the selected [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis). > > **Technology** > > * *OS (iOS, Android)* > > * *Browser (Chrome, Edge, Opera, Firefox, Safari, and so on)* > > * *Device Type (Mobile, Desktop)* #### Step 1: Select Performance Indicators {1425}

Click to enlarge As shown above, using the filter icon placed on the top right, you can select/deselect metrics to customize the table as per your analytical needs. > 📘 The following metrics can be selected here: > > **Totals** *(Total Impressions, Total Closes, Total Clicks,[Total Conversions](conversion-tracking#section-how-total-conversions-and-revenue-are-calculated-for-a-campaign-and-its-channel), [Total Click-through Conversions](conversion-tracking#section-clicks-highest-priority-))* > > **Uniques:** *(Unique Impressions, Unique Closes, Unique Clicks, Unique Conversions,[Unique Click-through Conversions](conversion-tracking#section-clicks-highest-priority-))* > > **[Revenue](revenue-tracking)** *(Total Revenue,[Click-through Revenue](conversion-tracking#section-impact-of-conversions-on-revenue-attribution))* > > *([Detailed read on what each performance indicator denotes](channel-campaign-performance-metrics#section-web-in-line))* #### Step 3: Preview Variation's Message & Layout

Click to enlarge As shown above, using the *View icon\_placed next to each \_Variation's header,* you can preview the *Web In-line Banner* for both, *Desktop* and *Mobile* users. This is a great way to understand how varied copies and layouts impact user-campaign interactions. #### Step 4: Select a Data Format {1439}

Click to enlarge While the default view shows you percentage values (%) for all the campaign performance indicators, using the overflow menu highlighted above, you can choose to analyze these stats as numbers (by selecting #). * Percentage value of all the performance indicators like *Closes, Clicks, Conversions and Click-through Conversions* is calculated against the total number of Impressions.\* ### 3. Engagement Trends {1436}

Click to enlarge This section helps you analyze day-wise user engagement trends for each *[Variation](campaign-variations-testing#section-variations)* against the following [performance indicators](channel-campaign-performance-metrics#section-web-In-line), as per the chosen [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis).: * Unique Impressions * Unique Clicks * Unique Conversions **Let's analyze a use-case to get you acquainted with the workings of this section:** > 🚧 > > **Skip to:** [How you can customize the stats shown here](analyzing-web-In-line-campaigns#section-step-1-select-a-variation) as per your analytical needs. > 👍 Use-case: Analyzing Engagement Trends for a Cart Recovery Campaign > > In the visual below, we have analyzed the engagement trends of a *[Journey](campaigns#section-journey-campaigns) Web In-line campaign* for the *last 7 days (Nov 28 - Dec 21)* as a *[Funnel View](channel-campaign-performance-metrics#section-funnel).* > > **About the campaign:** > > * The campaign aims at driving purchases for an e-commerce app by offering free shipping to all users who have searched for a product but have not purchased it even after 30 minutes. It's triggered for all users who perform the *[custom event](events-and-event-attributes#section-custom-events) - Product-Searched,* after a *Wait Time of 30 minutes* if the user has not performed the *custom event - Checkout-Complete.* > > * The *[Conversion Event](conversion-tracking#section-conversion-event)* is also set as *Checkout-Complete* with a *[Conversion Deadline](conversion-tracking#section-conversion-deadline) of 2 Days.* > > * The campaign was launched on *November 28.* {1440}

Click to enlarge **On analyzing engagement trends, we learn that:** * The highest number of *Unique Impressions, 43,597* and *Unique Conversions, 5,518* occurred on *Nov 30.* * Interestingly, the engagement trends suggest that most of the users who performed the campaign's *[Conversion Event](conversion-tracking#section-conversion-event) - Checkout Complete,* did not actually click on the notification. They simply viewed it and converted. * Such conversions are tracked as *[Impression-through Conversions](conversion-tracking#section-views-mid-priority-)* in your dashboard and can be further analyzed through a campaign's *Analyze* section. > 👍 > > Now, let's widen the scope of analysis to a month (Oct 13 - Nov 13) to gain a better perspective on the campaign's engagement trends. **It reveals that:** * The highest *Unique Impressions* occurred on *Nov 2, 600,* followed by *Oct 20, 556.* * The *Unique Conversion* and *Unique Click* trends throughout the month suggest that there is no correlation between the clicks and conversions contrary to popular assumption. Even though a low number of users clicked on the campaign, the conversion numbers consistently remain high. * Further, the highest *Unique Conversions* also occurred on \_Nov 2, 326.\_suggesting that users targeted by the campaign on this day had a higher intent to convert compared to other users. We hope this has equipped you with a robust understanding of how you can leverage *Engagement Trends* to gain valuable insights into user behavior and the campaign's overall performance. **Now, let's show you how you can customize this section as per your analytical needs:** #### Step 1: Select a Variation {1440}

Click to enlarge By default, engagement trends are shown only for *Variation A* (the first version of the campaign). Using the dropdown placed on the top left, you can choose to analyze trends for each *Variation* against - *Unique Impressions, Unique Closes, Unique Clicks* and *Unique Conversions.* * If you have enabled *[Control Group](control-groups)* for the campaign, then you can choose to analyze its *Unique Conversion* trends by selecting *'Control Group'* from the dropdown highlighted above. #### Step 2: Select a Format of Visualization {1440}

Click to enlarge While the default format of visualization is a *Line Graph,* as shown above, using the overflow menu placed on the top right, you can change this to a *Bar Graph* or a *Table,* as per your analytical needs. ## Analyze While *Overview* presents a comprehensive view of the campaign's performance, this section helps you slice-and-dice data in multiple ways to gain maximum insights into user-campaign interactions. **First Impression** The default view of this section shows you a *Day-wise* trend for *Total Impressions* recorded for all the *Variations* from the campaign's *Start Date* till the present date or till *up to 45 days from its End Date* (whichever occurs first). {1440}

Click to enlarge Using the query bar highlighted above, you can choose to analyze the campaign's performance against a specific performance indicator, split by 2 parameters for a custom [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis). ##### Let's Analyze a Use-case to Show You How it Works > 🚧 > > **Skip to:** [How you can customize the stats shown here](analyzing-web-In-line-campaigns#section-step-1-select-a-variation-control-group) as per your analytical needs. > 👍 Use-case: Analyzing a Web In-line Campaign Nudging Users to Search for University Enterance Exam Practice Material After University Search > > Let's take the example of an international Edtech website. They recently launched university entrance exam study material as a new feature, and in a bid to drive adoption, they decided to contextually nudge users to try it out. > > * Marketers of the website created a campaign that nudges all users who have searched for universities to search for a university entrance exam material. > > * The end goal was to drive adoption and not necessarily purchase (as an outcome of great product adoption). Hence, the campaign's *[Conversion Event](conversion-tracking#section-conversion-event)* was set as the *[custom event](events-and-event-attributes#section-custom-events), University-Search.* Now, let's analyze the campaign to understand it's users and their behavior better. > 👍 Analysis 1 (in continuation of the use-case) > > Identifying the top countries and type of users who searched for universities entrance exam material after searching for universities, over the last 30 days. For this, we'll analyze Total Impressions over Countries, split by User Type.\* {1425}

Click to enlarge **As shown above, here's how they went about their analysis:** **Step 1:** Since the campaign was launched in past, we'll select *Last 30 days* as the time frame of analysis. **Step 2:** Select *Country* under the field - *Over.* **Step 3:** Select *User Type* under the field - *Split By* and click enter! **It reveals that:** * The notification was viewed 91,896 times within the selected time frame. * The maximum number of users engaged through the *Web In-line Banner* are located in *India,* out of which The highest number of *Impressions* were recorded for \*[Known Users](understanding-users-in-webengage#section-unknown-and-known-users), 79,913. > 👍 Analysis 2 (in continuation of analysis 1 of the use-case) > > Now, let's analyze users who performed the conversion event of the campaign *(Total Conversions),* against the same variables, *User Type* and *Country.* This will help us draw a fair comparison between the users who viewed the notification and those who converted. {1425}

Click to enlarge As shown above, we'll select *Total Conversions* under the field - *Show* and click enter. **It reveals that:** * Users engaged through the notification performed the campaign's *[Conversion Event](conversion-tracking#section-conversion-event) - Hotel-search* a total of *6,048 times* within the selected time frame. * Keeping up with the *Total Impression* trends, the maximum number of converted users are located in *India* and are *[Known](understanding-users-in-webengage#section-unknown-and-known-users).* > 👍 Analysis 3 (in continuation of analysis 2 of the use-case) > > Now that we have a fair idea of the campaign's impression and conversion trends, let's dig into the time of the day and country where the users interact with the website. For this, we'll analyze *Total Conversions over Hours of the Day, split by Country.* {1425}

Click to enlarge **As shown above, here's how they went about their analysis:** **Step 1:** Select Hours of the Day under the field - *Over.* **Step 2:** Select *Country* under the field - *Split By* and click enter! **It reveals that:** * Maximum users engaged by the *Web In-line* are from India *India.* * The highest *Conversions* occurred between *11 pm - 12 am (as per the account's timezone),* followed by a spike in *Conversions* between *12 am pm - 1 am.* This indicates that most users prefer to engage with the app during late-night as they unwind from a tiring day. We hope this has equipped you with a broad understanding of how you can gain in-depth insights into your campaign, its users, and their preferences through *Analyze.* Now, let's walk you through all the steps of analysis: ### Step 1: Select a Variation/ Control Group {1440}

Click to enlarge If you have created multiple *[Variations](campaign-variations-testing#section-variations)* for a *Web In-line campaign,* or have enabled *[Control Group](control-groups)* for it, then using the dropdown highlighted above; you can choose to selectively analyze the performance of each. However, if your campaign consists of just one *Variation*, then this dropdown gets hidden. > 📘 Any one of the following options can be selected here: > > * **Overall:** Helps you analyze the combined performance of all the *Variations* against each metric for the selected time frame. > > * For example, if you choose to analyze *Revenue, Overall,* then we will sum up the *Revenue* tracked for each *Variation* to help you analyze their collective impact. > > * **Variation A/B/C/D/E:** Helps you analyze the performance of a *Variation,* against a selected performance indicator, as per the chosen [time frame](analyzing-web-In-line-campaigns#select-a-time-frame-of-analysis). > > * **Control Group:** If you have enabled *[Control Group](control-groups)* at *[Step 4: Conversion Tracking](creating-web-In-line-campaigns#step-4-set-up-conversion-tracking-variation-testing)* while creating the campaign, then selecting this option will help you its *Unique Conversions/ Total Conversions.* > 🚧 Related Read > > Please refer to [Campaign Variations and Testing](campaign-variations-testing) for a quick read on how you can test multiple *Variations* of a campaign with a smaller audience and automatically send the winner to the entire target audience! ### Step 2: Select a Performance Indicator {1425}

Click to enlarge You can choose to analyze the overall performance of the campaign or each *[Variation](campaign-variations-testing#section-variations)* against a performance indicator or [campaign event](channel-campaign-performance-metrics) (like Impressions, Clicks, Conversions, Revenue) by selecting an option under the field - *Show.* * As highlighted above, the total value of the selected performance indicator is also indicated under the query bar. > 📘 Any one of the following campaign events can be selected here: > > * **Totals** *(Total Impressions, Total Closes, Total Clicks,[Total Conversions](conversion-tracking#section-how-total-conversions-and-revenue-are-calculated-for-a-campaign-and-its-channel), [Total Click-through Conversions](conversion-tracking#section-clicks-highest-priority-))* > > * **Uniques:** *(Unique Impressions, Unique Closes, Unique Clicks, Unique Conversions,[Unique Click-through Conversions](conversion-tracking#section-clicks-highest-priority-))* > > * **[Revenue](revenue-tracking)** *(Total Revenue,[Click-through Revenue](conversion-tracking#section-impact-of-conversions-on-revenue-attribution))* > > *([Detailed read on what each performance indicator denotes](channel-campaign-performance-metrics#section-web-In-line))* ### Step 3: Select the Dimension(s) for Analysis *(Over & Split By)* {1425}

Click to enlarge As demonstrated for the use-case discussed above, by selecting an option under the fields - *Over* and *Split By,* you can choose to analyze the selected performance indicator ([campaign event](events-and-event-attributes#section-campaign-events)) against two dimensions ([system attributes](events-and-event-attributes#section-how-different-events-and-event-attributes-interact)). This is a great way to dig into multiple aspects of the campaign like: * The top *Locations* and *Devices* used to perform the selected campaign event * The *Types of Users* who performed the campaign event and their *Browser preferences* * The trend of occurrence of selected campaign event against *Hours of the Day, Days of the Week, or Months of the Year* and so on. > 📘 The following options can be selected under both the fields: > > * **Time** *(Days, Weeks, Months)* > > * **Time Block:** Trends against time, *Hours of Day, Days of Week, Months of Year,* are always shown in your project's timezone, as set by you while setting up your WebEngage account. > > * **User Type** *([What it means](understanding-users-in-webengage#section-unknown-and-known-users))* > > * **Location** *(Country, City)* > > * **Technology** *(Device Type, OS Name, Device Manufacturer, Device Model, App Version, App ID, Platform)* ## List of Users > 🚧 Must Read > > Please ensure that you have a good understanding of the [various types of Users and related concepts](understanding-users-in-webengage) before proceeding. Doing so will help you understand the workings of this section, better. This section has been specially designed to help you track down campaign engagement details for each user targeted by the campaign over its entire lifetime. By default, *List of Users* shown you a *Summary* of user-campaign interactions for *All Users* who have been included in the campaign's target audience, from its *Start Date* till the present date. {1440}

Click to enlarge ### Filtering the List Here's how you can filter the *List of Users* as per your analytical needs: #### **Select a Parameter** {1425}

Click to enlarge As shown above, using the dropdown placed on the top left, you can filter the *List of Users* by selecting a parameter. > 📘 The following options can be selected here: > > **1. Summary:** Shows you an overall summary of all the users included within the target audience, against the following columns: > > * **Latest Message Status:** Indicates the status of the message sent to the user with a date-time stamp as any one of the following: > * Impression on (date-time) > * Closed on (date-time) > * Clicked on (date-time) > * Converted on (date-time) > * Added to Control Group on (date-time): Shown for all users who were not sent the campaign as they were added to the *[Control Group](control-groups),* as specified by you while creating the campaign. > > * **Total Impressions:** Indicates the total number of times the user viewed the *Web In-line Banner.* > > * Latest Impression Date:\*\* Indicates the date-time on which the latest view occurred. > > * **Total Clicks:** Indicates the total number of times the user has clicked on the *Web In-line.* > > * **Latest Click Date:** Indicates the date-time on which the latest click occurred. > > **2. Impressions:** Helps you analyze all the users that have seen the Web In-line campaign against the following columns: > > * **Total Impressions** > > * Latest Impression Date\*\* > > **3. Clicks:** Helps you analyze all the users who have clicked on the campaign over its entire lifetime against the following columns: > > * **Total Clicks** > > * **Latest Click Date** > > **4. Conversions:** Helps you analyze all the users that have performed the campaign's [Conversion Event](conversion-tracking#section-conversion-event), over its entire lifetime, against the following columns: > > * **Total Conversions:** Indicates the total number of times the user performed the *Conversion Event* within the specified *[deadline](conversion-tracking#section-conversion-deadline).* > > * **Latest Conversion Date:** Indicates the date-time on which the latest conversion occurred. > > * **Latest Conversion Type:** Indicates the campaign event *[Views, Clicks](conversion-tracking#section-how-conversions-are-attributed-to-a-campaign)* to which the conversion was attributed to as *[Click-through Conversion](conversion-tracking#clicks-highest-priority) [Impression-through Conversion](conversion-tracking#views-mid-priority) or [Send-through Conversion](conversion-tracking#sent-lowest-priority).* > > * **Revenue:** Indicates the total amount of *[Revenue](revenue-tracking)* contributed by the user (calculated against *Total Conversions*). > > *([Detailed read on what each performance indicator denotes](channel-campaign-performance-metrics#web-personalization))* > > **5. Control Group:** Lists down all the users who have been added to Control Group. Here you can view the control group (Universal, Custom) of which the user is a part of along with the date and time in which they were added to it for the campaign ### Download List {1425}

Click to enlarge As shown above, using the *Download icon* placed on the top right of the table, you can choose to download a user-wise engagement report for all the users targeted by the campaign over its entire lifetime. Here's how you can go about it: **Step 1:** Click the *Download icon.* * In doing so, you will be prompted with a pop-up to re-confirm your decision. **Step 2:** Click the *Download button* on the pop-up window to proceed. * An email containing a link to the downloadable file will be sent to your registered email address shortly. #### **Search for a User**

Click to enlarge As shown above, using the search box placed on the top right, you can dig into the engagement status of a particular user by searching for their *User ID.* > 🚧 Why is the same User ID shown in multiple rows? > > As visible in the above visual, multiple rows get populated with the same *User ID on searching for a user.* Each row contains a different message status with a varying date-time of *Impression* and *Conversion.* > > This is because the user chose to perform the trigger event twice and was sent the [triggered Web In-line campaign](campaigns#section-triggered-campaigns) twice as a result. Thus, if a triggered campaign is sent to a user multiple times, you will be able to analyze their interaction with each run of the campaign, as shown above. #### **Access User Profiles**

Click to enlarge As shown above, you can access the profile of a user targeted by the campaign by clicking their (hyperlinked) *User ID.* Doing so will help you analyze the user's [channel preferences](analysing-user-profiles#section-channels), [personal details](analysing-user-profiles#section-basic-information) [device preferences](analysing-user-profiles#section-devices), [latest interactions](analysing-user-profiles#section-events) with your campaigns, app, website, the technical aspects of each interaction much more! > 🚧 Must Read > > Please refer to *[Analyzing User Profiles](analysing-user-profiles)* for a robust understanding of how you can dig into the preferences and behavior of individual users. We hope this has equipped you with a robust understanding of how you can mine valuable insights into a campaign to understand its real impact on user engagement and revenue. Please feel free to drop in a few lines at *[support@webengage.com](mailto:support@webengage.com)* if you have further queries. We're always just an email away!