--- title: Understand your influence analysis slug: analytics/docs/understand-your-influence-analysis docTags: createdAt: 2026-07-20T15:44:41.242Z --- This article explains how to interpret the results of an Influence analysis in Optimizely Analytics. It covers the [key statistics](docId\:sk7UgSz7PtCTqk2nCIF4V) calculated by the template and describes how to read and understand the individual modules within the exploration template. For information on how to set up an Influence analysis, see [Influence analysis overview](docId\:HiN4fXPWeq2MOSFpWsRZy). The Influence template has the following modules: - [Actors module](docId\:sk7UgSz7PtCTqk2nCIF4V) - [Test module ](docId\:sk7UgSz7PtCTqk2nCIF4V) - [Target module](docId\:sk7UgSz7PtCTqk2nCIF4V) - [Filter module](docId\:sk7UgSz7PtCTqk2nCIF4V) - [Visualization module](docId\:sk7UgSz7PtCTqk2nCIF4V) ## Actors module The Actors module lets you define the primary entity for your analysis and specify which subset of actors to include. For example, set the **Actor** to **Accounts** and include only the **Enterprise Customers** cohort to analyze how group-level activity drives their renewal rate. The **Actors** module has the following fields: - **Actor** **field** – Selects the dataset that is the fundamental unit of analysis, determining what entity events and metrics are attributed to. When set to **Users**, every event or metric is attributed to a specific person, making it ideal for optimizing individual user journeys. For example, a streaming site can determine if a user who **Watches a Trailer** is then more likely to **Play Content**. - **Included Actors (Optional)** – Filters the actor population to a specific cohort rather than analyzing all actors. Select a previously defined cohort or create a new one by clicking **+ New Cohort**. - When your **Actor** is **Users**, the template lets you compare influences across audience segments. For example, you can analyze if **Using a Discount Code** (test event) increases **Average Session Length** (target metric) for **New Users versus Loyal Customers**. - When your **Actor** is something else, like **Accounts**, the template lets you focus on specific business segments or tiers. For example, you can analyze if **Adding a Collaborative Document** (test event) leads to **Subscription Renewal** (target event) for only your **Enterprise Accounts**. ::Image[]{src="https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/u0q-QMG6Bl3_fhSDNUfUM_qbgqz5-7pimfxmmzvxq0-20260710-141004.png" size="40" alt="oa-inf-10.png" isUploading="false" sha="15cf34440d20dbe6472fce2f5f283f19f3e62bd6" initialPath="../../../assets/QBGQZ5-7pImf_XMmZVXq0-20260710-141004.png" githubPath="docs/assets/QBGQZ5-7pImf_XMmZVXq0-20260710-141004.png" darkWidth="465" darkHeight="399" width="465" height="399" showCaption="false"} ## Test module The **Test** module is where you define the events, metrics, or cohorts you want to analyze as potential drivers of your chosen target. To get the most value, align your **Test** type with your specific [analytical goal](docId\:HiN4fXPWeq2MOSFpWsRZy). Use the **Type** field to specify whether your test is an **Event**, **Metric**, or **Cohort**. For information, see [Test types](docId\:sk7UgSz7PtCTqk2nCIF4V). You can select the following test types: ### Event When you select **Event** as the test type, you analyze how specific events influence a target (event, metric, or cohort) by comparing the likelihood or impact for users who did or did not perform those events. For example, you can analyze if an **Add to Cart** event influences a **Make Purchase** target differently depending on whether it occurred on a **Mobile** or **Desktop** device. When you select **Event** as the **test** type, the following fields display: - **Events** – Lets you select one or more events as your test variable, such as **Add to Cart**. - **Group by Test Property** – Groups the analysis by a property of the selected test event. Defaults to **Event Type** but can be changed. For example, grouping **Add to Cart** by **Device Type** shows how influences vary across **Mobile**, **Desktop**, and **App**. ### Cohort When you select **Cohort** as the test type, you analyze how belonging to a specific user cohort influences your chosen target. The **Cohorts** field lets you select one or more cohorts as your test variable and compare them against your target to determine their impact. For example, a streaming service can set **Premium Subscribers** as the test cohort to analyze if they are significantly more likely to **Start a New Series** compared to the average user base. ### Metric When you select **Metric** as the **test** type, you analyze how the value of a specific metric influences your chosen target. This can involve correlation analysis (for **Metric → Metric**) or impact analysis (for **Metric → Event/Cohort**). For example, you can analyze if a higher **Average session duration** (test metric) strongly correlates with an increase in **Monthly recurring revenue** (target metric) for your subscription platform. The **Test Metrics** field lets you add one or more metrics as your test variable by clicking **+**. ::Image[]{src="https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/zTqw1nvlWaFLjII2Iu8Xr_glondgwlbnt303u3ltex-20260710-141004.png" size="40" alt="oa-inf-11.png" isUploading="false" sha="12defec8bf62333f2339299093b7a8b40c071e98" initialPath="../../../assets/gl_onDgWLBnt303U3LTEX-20260710-141004.png" githubPath="docs/assets/gl_onDgWLBnt303U3LTEX-20260710-141004.png" darkWidth="451" darkHeight="231" width="451" height="231" showCaption="false"} ## Target module The **Target** module is where you define the specific outcome or behavior you want to understand or influence. For example, define **Made Purchase** (an event), **Average Session Length** (a metric), or **High-Value Customers** (a cohort) as your target depending on your analytical goal. ::Image[]{src="https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/WzPtES-lV9dxKXtH-pE55_jnuqvq1nagbpd8srlni-r-20260710-141004.png" size="40" alt="oa-inf-14.png" isUploading="false" sha="c1bf1418d4faf7910c5603685cd216fa06052747" initialPath="../../../assets/jNuQVQ1naGbPD8sRlNi-R-20260710-141004.png" githubPath="docs/assets/jNuQVQ1naGbPD8sRlNi-R-20260710-141004.png" darkWidth="461" darkHeight="308" width="461" height="308" showCaption="false"} Use the Type field to specify whether your target is an Event, Metric, or Cohort. For information, see [Target types](docId\:sk7UgSz7PtCTqk2nCIF4V). You can select the following target types: ### Event Select **Event** as your target to define a specific user action or conversion point as your primary outcome, and analyze how various test factors (events, metrics, or cohorts) influence the likelihood of it occurring. The following fields display: - **Events** – Selects the single event you want to set as your target. - **Direction** **(Required, if Test is Event)** – Defines the temporal relationship between your test and target events: - **Causes (Before Target)** – Analyzes test events that occur before the target event. - **Effects (After Target)** – Analyzes test events that occur after the target event. - **Max time between test and target (Required, if Test is Event)** – Sets the maximum time window (for example, 7 days) within which a test event must occur relative to the target event to be considered relevant. When **Event** is your target, you can combine it with the following test types: - **Events → Event** – Select one or more test events that you suspect might influence your target event. You can also select **All events** to explore broadly. Use **Direction** (**Causes** or **Effects**) and configure the **Max time between test and target** to define the time window. The stats used in this configuration are [Likelihood impact](docId\:sk7UgSz7PtCTqk2nCIF4V) + [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V). - **Metrics → Event** – Select one or more metrics that you suspect might influence your target event. The stat used in this configuration is [Metric impact](docId\:sk7UgSz7PtCTqk2nCIF4V). - **Cohorts → Event** – Select one or more cohorts that you suspect might influence your target event. The stats used in this configuration are [Likelihood impact](docId\:sk7UgSz7PtCTqk2nCIF4V) + [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V). For example, if your target is **Make purchase** and your test is **Add to cart**, the template calculates the [Likelihood Impact](docId\:sk7UgSz7PtCTqk2nCIF4V) by dividing the purchase rate of users who added to cart by the purchase rate of all users; a result of **2x** indicates that users who added to cart are twice as likely to complete a purchase compared to the average user. ![oa-inf-20.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/7mwonkFqn6iKuL6hTG3FH_ntjx0cqgk2jdkqy28q1vj-20260710-141004.png) ### Cohort Select **Cohort** as your target to define a specific user group as your primary outcome of interest. This is ideal when your goal is to grow a user segment, improve retention, or understand the factors that define important user groups. The **Cohorts** field selects a single user cohort as your target. The analysis identifies how test factors associate with belonging to that cohort. When **Cohort** is your target, you can combine it with the following test types: - **Events → Cohort** – Select one or more events that you suspect might be associated with your target cohort. The stats used here are [Likelihood impact](docId\:sk7UgSz7PtCTqk2nCIF4V) + [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V). - **Metrics → Cohort** – Select one or more metrics that you suspect might be predictive of membership in your target cohort. The stat used here is [Metric impact](docId\:sk7UgSz7PtCTqk2nCIF4V). - **Cohorts → Cohort** – Select one or more cohorts that you suspect might be associated with your target cohort. The stats used here are [Likelihood impact](docId\:sk7UgSz7PtCTqk2nCIF4V) + [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V). For example, if your target cohort is **Loyalty program subscribers** and your test is **First time visitors**, the template calculates the [Likelihood Impact](docId\:sk7UgSz7PtCTqk2nCIF4V) by dividing the percentage of first-time visitors who are in that cohort by the overall percentage of users in that cohort; a result of **1.5x** indicates that first-time visitors are 50% more likely to be loyalty members than the average user. ![oa-inf-19.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/H5oh8nZnjClBoomDxgePN_hjnpy3fuegnisxrcov4q-20260710-141004.png) ### Metric Select **Metric** as target when your primary objective is to optimize a quantifiable business outcome. This mode helps you understand the drivers of performance indicators. When you select **Metric** as the **target** type, you define a specific quantifiable metric as your primary outcome of interest. The **Metric** field lets you select a single metric as your target. You can add only one metric. When **Metric** is your target, you can combine it with the following test types: - **Events → Metric** – Select one or more events that you suspect might influence your target metric. The stats used here are [Metric impact](docId\:sk7UgSz7PtCTqk2nCIF4V) + [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V). - **Metrics → Metric** – Select one or more metrics that you suspect might correlate with your target metric. The stat used here is [Correlation](docId\:sk7UgSz7PtCTqk2nCIF4V). - **Cohorts → Metric** – Select one or more cohorts that you suspect might influence your target metric. The stats used here are [Metric impact](docId\:sk7UgSz7PtCTqk2nCIF4V) + [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V). For example, if you select **Total purchase revenue** as your target metric and **Average order value** as your test metric, the template calculates the [Correlation (Pearson r)](docId\:sk7UgSz7PtCTqk2nCIF4V) between them; a result such as **r = 0.85** indicates a strong positive relationship, meaning that as users' average order values increase, their total purchase revenue also tends to rise significantly. ![oa-inf-18.png](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/UcDfKj0GmG8M0VhsuwxGh_7hwp-vsb71uica3mjudq-20260710-141004.png) ## Filters module After setting up your influence template by defining your target, test, and actor modules, the **Filters** module lets you narrow down the data presented in your visualizations. This makes it easier to answer exploratory questions and drill down into specific segments of your analysis. For instance, if your main analysis is for **All Users**, you can use a filter to quickly see results for **Mobile Users** or users from a specific region without having to reconfigure your entire **Actor** module. This provides flexibility for ad-hoc exploration and deeper dives into your insights. Analytics also lets you choose **JSON** columns in this module. When you click a JSON column, it expands to display the available keys for that column. You can choose a key and click **Apply**. The selected end key becomes the display name for that column. ## Visualization module The impact template's visualization module lets you run and view the analysis in a chart. It also lets you edit the visualization, examine the underlying SQL query, and add the chart to a dashboard. There are five important sections in this module. - **# and %** – Switch between numbers and percentage values on your chart. - **Top 'n' segments** – Choose the number of top segments you want to display in the visualization. - **Time range and time grain** – Configure the time range and time grain for the analysis. - **Sampling** – Configure sampling and responses for the analysis. - **Edit visualization (other actions)** – Inspect a query, edit visualization, add an analysis to a dashboard, and download as CSV. ### Switch between numbers and percentages These options let you switch the values on the Y-axis from absolute values to percentages and vice versa in one click. This feature is particularly useful when dealing with multiple segments, as using absolute values lets you see a clearer breakdown of how many individuals are in each segment. ### Top 'n' segments To choose the number of top segments you want to display in your impact analysis, click the **Show top** drop-down list and enter the required number. You can also use the **+** and **–** symbols to enter your input. The total count of selected events defines the top attribute values. You can also set the number of top 'n' segments when you create beelines. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/kJ8_pZF36jwu95mjgI3Qd_opti-imp-10.png) ### Time range and grain - **Time Range** – The complete period during which events are considered for the analysis. Examples include the last 2 years or the time range between two specific dates. - **Time Grain** – The granularity of analysis, such as daily (1 day), weekly (7 days), and so on. You can set the time range using a drop-down list or choose from the quick options and iterate through different choices of time range and time grain without leaving the chart. It is also possible to set a lag by clicking **Offset** and setting the **Ending**. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/t1d-3TsHGtrBJu8XkBX18_opti-imp-11.png) ### Sampling section In the visualization module, you can configure sampling modes for your exploration by clicking the lightning icon at the top of the exploration. The drop-down list displays two sampling modes: **Enabled Faster Response** or **Enabled Higher Precision**. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/V1UgjZViK3e2G7pNEbgEB_opti-imp-12.png) ### Edit visualization section - **Inspect Query** – Lets you change the SQL and NetScript queries. The query inspector has **SQL**,**Warehouse SQL**, and **NetScript** tabs. You can make changes by clicking the **Open as SQL Explore** and **Open as NetScript Explore** in the respective sections. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/RyjrL0OA1ClU4tEbcMU-8_opti-imp-13.png) - **Add to Dashboard** – Add your created exploration to a dashboard by choosing **Pick a dashboard** or **New Dashboard**. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/OgMpi0niz-AYC9VDRFcs2_opti-imp-14.png) - **Edit visualization** – Contains the chart settings categorized under two sub-sections: **Data** and **Style**. You can configure the following for each: :::Paragraph{indent="1"} **Data**: ::: :::Paragraph{listStyleType="disc" indent="2"} Automatic refresh for your chart ::: :::Paragraph{listStyleType="disc" indent="2"} Chart type ::: :::Paragraph{listStyleType="disc" indent="2"} X-axis and Y-axis configuration ::: :::Paragraph{listStyleType="disc" indent="2"} Tooltip ::: :::Paragraph{listStyleType="disc" indent="2"} Size configuration ::: :::Paragraph{indent="1"} **Style**: ::: :::Paragraph{listStyleType="disc" indent="2"} Enable the sorting option on the graph ::: :::Paragraph{listStyleType="disc" indent="2"} Configure sorting rules and the limit ::: ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/f_a7qtmjXIWni9XcvtdTR_screenshot-2025-05-21-at-54159-pm.png) - **Download As** – Download your analysis in **CSV** or **Excel format**, with column formatting preserved. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/v9MKEK1R2DqC1aVDuPGjw_screenshot-2025-05-21-at-54212-pm.png) ## Statistics interpretation When you define your target, test, actors, and time range, the Influence template automatically calculates key statistics to quantify the relationships in your analysis. This module provides a detailed breakdown of these statistical outputs, helping you understand the "why" and "how much" behind the influences you identify. The specific statistical interpretations available depend on the combination of your chosen test and target types. At a high level, these statistics help you understand: - [Likelihood impact](docId\:HiN4fXPWeq2MOSFpWsRZy) – Measures how much more (or less) probable an outcome is given a specific behavior or group. - [% of actors who did test](docId\:sk7UgSz7PtCTqk2nCIF4V) – Quantifies the proportion of your audience engaging with a particular test. - [% of actors who are in test cohort](docId\:sk7UgSz7PtCTqk2nCIF4V) – Quantifies the proportion of your total audience that belongs to a specific test cohort. - [Metric impact](docId\:sk7UgSz7PtCTqk2nCIF4V) – Determines the quantitative change in a metric's value due to a test factor. - [Correlation](docId\:sk7UgSz7PtCTqk2nCIF4V) – Identifies whether two metrics tend to move together. ### Likelihood Impact This statistic measures how much more (or less) likely users who performed a test action are to also perform the target action, compared to those who did not. It helps you identify which user behaviors or features are most influential in driving a target outcome. It applies to the following test → target types: - Events → Event - Cohorts → Event - Events → Cohort - Cohorts → Cohort **Calculation** – `(Conversion rate of test group) / (Baseline conversion rate)` - **Conversion rate of test group** – The percentage of users who performed the test event (or belong to the test cohort) and subsequently performed the target event (or belong to the target cohort). - **Baseline conversion rate** – The percentage of all users who performed the target event (or belong to the target cohort). **Example** If users who trigger the **Product Video Viewed** event are 3x more likely to also trigger the **Add to Cart** event, it shows videos strongly drive purchase intent. Imagine you have *100* total users included in the analysis. Out of these *100* users, *20* (*20%*) **Add to Cart**. This is your baseline conversion rate. Now, let's say *50* users watched the **Product Video Viewed** (your test group). Out of those *50* video watchers, *30* (*60%*) went on to **Add to Cart**. The likelihood impact is calculated as: `(60% conversion rate for video watchers) / (20% baseline conversion rate) = 3x. This means video watchers are three times more likely to add to cart. ` A result of **3x** means video watchers are three times more likely to add to cart, highlighting the video's strong influence. The following **Events → Event** analysis tests multiple events against the **Make Purchase** target for **Users** within a **7-day** window. The scatter plot shows that **Browse**, **View Content Details**, and **Search** are the strongest drivers of purchase, sitting in the **Higher Impact**, **Higher Reach** quadrant — meaning they are both widely performed and highly influential. **Pause Content**, **Resume Content**, and **Play Content** have an even higher likelihood impact but are performed by fewer users, while **Marketing Touchpoint** shows comparatively limited reach and influence. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/dBHmHRS8jXF0WtzZoowax_imfluence-timeline-chart.png) ### % of actors who did test This statistic tells you what percentage of your total audience actually performed a specific test action or belonged to a certain group (cohort). It is the proportion of your entire user base that engaged with the test. This helps you understand the reach or adoption of a particular feature or campaign, giving context to other impact metrics. It applies to the following test → target types: - Events → Event - Cohorts → Event - Events → Cohort - Cohorts → Cohort - Event(s) → Metric - Cohort(s) → Metric **Calculation** – `(Count of users who did test event) / (Count of all users)` **Example** If only 10% of your total user base triggered the **Promotional Banner Clicked** event, it tells you the banner's reach is limited. Imagine you have *100* total users. Only *10* of those users clicked on **Promotional Banner Clicked**. The % of actors who did the test is calculated as: `(10 users who clicked) / (100 total users) = 10%.` This shows that only a small fraction of your audience interacted with the banner. The following screenshot shows a **Cohorts → Cohort** analysis that tests three cohorts — **Made Purchase within 7 Days of Signup**, **Regular Watchers**, and **Viewed Recommended Content** — against the target cohort **Loyalty Program Subscribers** for **All Users**. The scatter plot shows that **Viewed Recommended Content** is the strongest predictor of loyalty program membership, sitting in the **Higher Impact, Higher Reach** quadrant — meaning it is both widely adopted and highly influential. **Made Purchase within 7 Days of Signup** shows moderate reach and impact, while **Regular Watchers** is the weakest predictor of the three. Overall, engaging with recommended content appears to be the most significant behavioral driver of loyalty program membership, though these relationships should be validated through further experimentation. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/nxCzNPWAB9WjrV7BkcAiP_6ibzwegggzg96fehpsiq-20260710-141006.png) ### % of actors who are in test cohort This statistic tells you what percentage of your total audience belongs to a specific test cohort. It is the proportion of your entire user base that falls within the defined group you are testing. This helps you understand the size and reach of a particular cohort, giving context to other impact metrics and helping you assess how representative or niche the group is within your overall audience. It applies to the following test → target types: - Cohorts → Event - Cohorts → Cohort - Cohort(s) → Metric **Calculation** – `(Count of users who belong to test cohort) / (Count of all users)` **Example** If only 15% of your total user base belongs to the **Loyalty Program Members** cohort, it tells you this is a relatively small but potentially high-value segment of your audience. Imagine you have 100 total users. Only 15 of those users belong to the **Loyalty Program Members** cohort. The % of actors who are in the test cohort is calculated as: `(15 users in cohort) / (100 total users) = 15%. ` This shows that while the cohort represents a small fraction of your overall audience, any strong influence this group has on your target outcome is particularly meaningful given its limited size. ### Metric impact This statistic measures how much a specific test action changes a key number (a metric) for users who performed that action, compared to the average for all users. It is a ratio comparing the average metric value for the test group versus the overall average. It applies to the following test → target types: - Metrics → Event - Metrics → Cohort - Events → Metric - Cohorts → Metric **Calculation –** `(Average target or test metric value for test group / target cohort) / (Average target/test metric value for all users)` - **Average target/test metric value for test group / target cohort** – The average value of the target/test metric for users who performed the test event (or belong to the target/test cohort). - **Average target/test metric value for all users** – The average value of the target/test metric across all users. **Example** If users who trigger the **Chatbot Interaction** event have an **Average Order Value** that is **1.2x** higher, it suggests that the chatbot boosts spending. Consider *100* total users. The **Average Order Value** across all *100* users is *$50*. For example, 30 users had a **Chatbot Interaction**. For these *30* users, their **Average Order Value** is **$60**. The Metric Impact is calculated as: `($60 Average Order Value for chatbot users) / ($50 Average Order Value for all users) = 1.2x. ` This means that chatbot users spend **20%** more on average. ### Correlation This statistic measures whether two metrics tend to move together, producing a value between **-1** and **+1**. It identifies potential relationships within your user experience, helping you spot trends and areas for further investigation, but does not imply that one metric causes the other. It applies to the Metric(s) → Metric test and target type. **Calculation** – Pearson correlation coefficient (r). - Values range from -1 to +1 - **+1** – Strong positive correlation (metrics increase/decrease together). - **-1** – Strong negative correlation (as one metric increases, the other decreases). - **0** – No linear correlation. **Example** A strong positive correlation (for example, r = 0.9) between two metrics means the two metrics tend to increase together. For example, users who spend 60 seconds on a product page might have a 5% purchase rate, while users who spend 120 seconds might have a 10% purchase rate. However, correlation does not imply causation; other factors may explain the relationship. For example, highly motivated buyers may naturally spend more time on a page before converting. Use this as a signal to form a hypothesis and validate it through controlled experimentation before drawing conclusions. The following example is a **Metrics → Metric** analysis testing three metrics — **Average Titles Viewed per User**, **Average Ad Revenue per User**, and **Total Purchase Revenue** — against the target metric **Total Time Viewed (TTV)** for **All Users** over a **30-day** period. The bar chart displays the **Pearson Correlation (r)** value for each test metric against the target, revealing the following: - **Average Ad Revenue per User** (r = 0.94) has the strongest correlation with Total Time Viewed, suggesting that users who generate more ad revenue tend to watch significantly more content. - **Average Titles Viewed per User** (r = 0.87) also shows a strong positive correlation, indicating that users who watch more titles also tend to accumulate more total viewing time. - **Total Purchase Revenue** (r = 0.82) has the weakest correlation of the three, though it still represents a strong positive relationship, suggesting that purchasing behavior is also associated with higher total viewing time. All three metrics show strong positive correlations with **Total Time Viewed**, indicating that engagement and revenue metrics tend to move together. However, it is important to note that these correlations do not imply causation — they signal relationships worth investigating further through controlled experimentation. ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/8mtyT3sGsiCZiGBSutH_J_correlation-example.png)