This resource provides clear definitions and explanations for key marketing metrics used in evaluating campaign performance. It covers essential terms, offering insights into what each metric means and how it can be applied to optimize your marketing efforts. Use this guide to better understand the data behind your campaigns and make informed decisions.

Along the top of your reporting dashboard is the key metrics that summarizes the performance of your campaign.
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Impressions refer to the total number of times an ad is displayed on a user’s device, regardless of whether the user interacts with it. This metric is fundamental in understanding the reach of your campaign and forms the basis for other key performance indicators (KPIs).

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Clicks measure the number of times users engage with your ad by clicking on it. This metric reflects initial user interest and can be a precursor to further engagement or conversion.

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Visits represent the number of users who physically visited a store or location during the campaign period after being exposed to the ad. This metric is crucial for connecting online or DOOH ad exposure with offline actions.

Tracking visits helps measure the real-world impact of your campaigns. If visits are lower than expected, analyze factors such as ad placement, timing, and creative effectiveness to identify areas for improvement.

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Unique reach indicates the number of distinct devices that were exposed to the ad during the campaign. Unlike impressions, which count multiple exposures, unique reach focuses on the actual audience size.
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Unique reach provides a clearer picture of how many individual users your campaign reached. It’s an essential metric for understanding the breadth of your audience and can help in determining whether your campaign is saturating the market or reaching new users.

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Total Exposed Visits is the number of visits recorded by users who were exposed to the ad. If a user visits the store multiple times, each visit is counted separately, offering insights into repeat footfall.

Monitoring total exposed visits helps assess the campaign’s ability to drive store footfall.

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Average Frequency measures how many times was an ad shown to each unique device/user on average. This metric helps assess optimal frequency for showing ads

Higher frequency may lead to ad fatigue. Use this metric to find the optimal balance between sufficient exposure and overexposure.

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Spend tracks the amount of money invested in each channel and its effectiveness in driving visits to the store.

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Visit Index quantifies the number of store visits generated for every 1,000 ad impressions served during the campaign. It provides insight into the efficiency of your campaign in driving physical footfall.

Formula: Visit Index = (Total Exposed Visits / Impressions) * 1000

A higher visit index suggests a more efficient campaign in converting impressions into visits. If the index is low, consider improving the targeting relevance and appeal of your ad creative.

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Exposure Index shows the percentage of total visitors (including both organic and those exposed to ads) who were exposed to the ads. This metric helps distinguish between organic and influenced footfall.

Formula: Exposure Index = Total Visits / Total Organic Visits

*TOTAL VISITS ARE THE AD ATTRIBUTED/EXPOSED VISITS

A high exposure index indicates that your ads are effectively reaching and influencing your target audience. Use this data to refine targeting strategies and maximize the impact of your ad spend.

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Conversion Index represents the percentage of users who, after being exposed to an ad, visited the store. This metric focuses on unique device reach rather than total impressions, providing a clearer view of conversion efficiency.

Formula: Conversion Index = (Exposed Visits / Unique Reach) * 100

The conversion index is key to understanding how effectively your campaign converts exposure into action. A low index may indicate the need for more compelling ad content or better-targeted audiences.

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Cost per Exposed Visit calculates the average cost incurred to drive each store visit after ad exposure. It’s a critical metric for evaluating the efficiency of your campaign.

Formula: Cost per Exposed Visit = Total Budget / Number of Visits

This metric helps you assess whether your campaign is delivering value for money. If costs are high, consider optimizing your bidding strategy or refining audience segments to improve ROI.

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CTR measures the percentage of ad impressions that result in clicks. It’s a primary indicator of how engaging your ad is to viewers.

Formula: CTR = (Clicks / Impressions) * 100

A higher CTR indicates that your ad is successfully capturing attention. If CTR is low, experiment with different headlines, visuals, or calls-to-action to boost engagement.

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Conversion Rate represents the percentage of impressions that lead to store visits after ad exposure. It provides a direct measure of the campaign's effectiveness in driving footfall.

Formula: Conversion Rate = (Visits / Impressions) * 100

This metric is crucial for understanding how well your ad is performing in terms of driving real-world actions. A low conversion rate may signal issues with ad relevance or targeting.

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Visit Lift Index measures the percentage increase or decrease in visits among users exposed to the ad compared to a control group (not exposed to ads). It’s a key metric for assessing the incremental impact of your campaign.

Formula: Visit Lift Index = (Normalized Exposed Visits – Normalized Control Visits) * 100 / Normalized Control Visits

Visit Lift provides insights into the direct impact of your ad campaign. A positive lift indicates effective targeting and messaging, while a negative lift suggests a need for campaign adjustments.

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Partner Breakdown provides insights into the performance of your campaign across various ad channels and DSPs (Demand Side Platforms), such as DV 360, Facebook, TTD, and Yahoo. This data helps in evaluating the effectiveness of different channels.

By analyzing the performance of each partner, you can allocate your budget more efficiently, focusing on channels that deliver the best results.

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Partner Index measures the impact of each advertising channel within a campaign. It’s used to identify the effectiveness of various channels in driving visits or conversions.

A high partner index for a particular channel indicates that it’s driving a significant portion of your campaign’s success. Use this data to optimize channel selection and budget distribution.

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Platform Breakdown categorizes performance metrics based on the platforms where ads were served, such as Mobile App, Mobile Web, or Desktop. This helps in optimizing your platform-specific strategies.

Understanding which platforms perform best allows you to tailor your campaign for each environment, ensuring maximum effectiveness and user engagement.

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Creative Breakdown offers insights into the performance of different ad creatives used in the campaign. By analyzing which creatives drive the best results, you can refine your creative strategy.

Identifying high-performing creatives allows you to focus on what works, improving overall campaign effectiveness. Rotate out low-performing creatives to maintain audience interest.

Publisher Name provides a breakdown of performance by the publisher, offering insights into which publishers are driving the best results for your campaign.

Publisher performance analysis helps in optimizing ad placements, ensuring your ads appear on sites that deliver the best audience engagement and conversions.

Device Type categorizes performance metrics based on the type of device targeted, such as phones, tablets, or PCs. Understanding device-specific performance helps in tailoring campaigns to the most effective devices.

By focusing on the devices that yield the highest engagement, you can enhance user experience and increase the effectiveness of your campaign.

Custom Value allows you to segment performance based on specific criteria such as ad groups, strategies, creative themes, or any other custom criteria. This enables deeper insights into campaign performance.

Using custom values, you can identify which segments of your campaign are most effective, allowing for more precise optimization efforts.

Let's use a real-world example involving a clothing retailer running an online ad campaign to illustrate Custom Value (CV) with multiple custom values.

Imagine Brand MNO , a clothing retailer, is running an online advertising campaign to promote a new line of summer clothes. They have different ad sizes and styles they want to track separately:

Brand MNO wants to understand which ad size and style combination performs the best. To do this, they use Custom Value tags in their tracking URLs.

1. Ad Size Custom Value:

- For ads of size 300x250 pixels, they set the custom value cv=300x250.

- For ads of size 728x90 pixels, they set the custom value cv=728x90.

- For ads of size 160x600 pixels, they set the custom value cv=160x600.

2. Ad Style Custom Value:

- For banner ads, they set the custom value style=banner.

- For video ads, they set the custom value style=video.

3. Tracking URL Setup:

Brand MNO sets up their tracking URLs with both custom values. For example, here’s how the URLs might look for each combination:

- 300x250 Banner Ad:

https://pixel.ad.lifesight.io/pixel/event/xxxxxx?event=RENDER&ios_idfa=[ios_idfa]&android_gaid=[android_gaid]&cid=[campaign_id]&app=[app_name]&channel=customdsp&cv=300x250&style=banner&dnt=[do_not_track]&cb=[cachebuster]&ts=[currentTime]

- 728x90 Video Ad:

https://pixel.ad.lifesight.io/pixel/event/xxxxxx?event=RENDER&ios_idfa=[ios_idfa]&android_gaid=[android_gaid]&cid=[campaign_id]&app=[app_name]&channel=customdsp&cv=728x90&style=video&dnt=[do_not_track]&cb=[cachebuster]&ts=[currentTime]

- 160x600 Banner Ad: https://pixel.ad.lifesight.io/pixel/event/xxxxxx?event=RENDER&ios_idfa=[ios_idfa]&android_gaid=[android_gaid]&cid=[campaign_id]&app=[app_name]&channel=customdsp&cv=160x600&style=banner&dnt=[do_not_track]&cb=[cachebuster]&ts=[currentTime]

Analyzing Performance:

After collecting data from these tracking URLs, Brand MNO can analyze how each combination performs:

Example Findings:

1. 300x250 Banner Ads might show a high click-through rate but low conversion rate.

2. 728x90 Video Ads might have a moderate click-through rate but a high conversion rate.

3. 160x600 Banner Ads might show low performance in both metrics.

Optimization:

With these insights, Brand MNO can make data-driven decisions:

By using Custom Values, Brand MNO can fine-tune their advertising strategy to maximize their campaign’s effectiveness.

You have three filtering options to choose from in order to customize a report for your business based on partners, custom values and impressions.

Identifies what percentage of Impressions/Clicks got converted as exposed visits at the attributed stores.

It collects data on different lead actions and their partners. It identifies how each partner have fractionally contributed to the total consolidated visits of the campaign.

The total visits by day of the week reveals when the store is most visited during a typical week. You can now see in the graph with the number of incremental visits each day throughout the week.

The total visits by hour reveals when the store is most visited during a typical day. You can now see in the graph with the number of incremental visits throughout the day.

This metric displays the specific days and times when users view or engage with an ad.

The distribution of exposed visit against the frequency of engagement.

Gender analysis shows the distribution of visitors by gender, providing insights into which gender segments are most responsive to the campaign.

Understanding gender-specific responses can help tailor future campaigns, ensuring your messaging resonates with the intended audience.

Age Group analysis reveals the distribution of visitors by age, offering insights into which age segments are most engaged with your campaign.

This data helps you refine your targeting, focusing on the age groups that are most likely to convert.

Device analysis dives into the operating systems and devices used by visitors, helping you focus your testing and optimization efforts on the devices your audience actually uses.

Optimizing for the most popular devices ensures a seamless user experience, potentially increasing engagement and conversion rates.

Carrier analysis provides a breakdown of delivery and performance by carrier network. This data helps in optimizing campaigns for the most effective carriers.

If certain carriers consistently underperform, you may choose to exclude them from future campaigns or investigate potential issues related to network performance.

The Conversion Pattern metric shows the specific times and days when users see an advertisement and then go on to visit a physical store. By analyzing this data, you can determine the most effective times to display ads in order to increase the likelihood of driving in-store footfall.

In other words, this metric tracks the correlation between when an ad is shown and when a customer makes a visit to a store. Understanding these patterns helps businesses optimize their advertising schedules to maximize the chances of converting ad viewers into store visitors.

Examples here showing how the conversion pattern can help you optimize your ads

Let's break it down using Brand ABC as an example:

Brand ABC wants to increase footfall to its retail stores by optimizing its advertising strategy. To do this, they use the Conversion Pattern metric to analyze when their ads lead to store visits.

1. Ad Exposure Data: Brand ABC runs ads across various channels like social media, TV, and online platforms. They track when these ads are shown to users.

2. Store Visit Data: They also collect data on when users who saw the ads actually visit Brand ABC stores.

Example in Action

1. Data Collection: Suppose Brand A’s ads are displayed at different times—morning, afternoon, and evening—over several days of the week. Brand ABC records the exact times these ads are shown and matches them with data on when people visit their stores.

2. Identifying Patterns:

8-9AM : Brand ABC finds that ads shown in between 8-9AM lead to store visits predominantly on weekends, specifically on Saturdays.
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3-4PM : Ads shown in between 3-4PM lead to store visits more frequently on weekdays, particularly on Tuesdays and Thursdays.

7-8PM: Ads delivered between 7-8PM drive store footfall mainly on Fridays and weekends.

From this analysis, Brand ABC discovers that:

8-9AM Ads are most effective for weekend store visits, suggesting that users are more likely to visit stores on Saturdays if they see ads in the morning.

3-4PM Ads drive visits on weekdays, so running ads in the afternoon could be beneficial for increasing store footfall during the workweek.

&-8PM Ads are best suited for Fridays and weekends, aligning with the time people are more likely to shop after work or on their days off.

Based on these insights, Brand ABC adjusts its advertising schedule:

- Increase between 8 - 9 AM : Ads on weekends to boost foot footfall on Saturdays.

- Target afternoon Ads more heavily on Tuesdays and Thursdays to capture weekday shoppers.

- Focus evening Ads on Fridays and weekends to attract customers who shop after work or during their days off.

By using the Conversion Pattern metric, Brand ABC can tailor their ad placements to match the times when their ads are most likely to lead to in-store visits, thus optimizing their advertising spend and increasing footfall to their stores.
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Visitation Pattern tracks the time and day when exposed users actually visit the store location. This data helps you understand customer offline behavior and optimize campaign timing.

By analyzing visitation patterns, you can optimize your store's operations, aligning staffing and resources with peak customer traffic to maximize efficiency and enhance the shopping experience.

App Categories and Names show the top categories and specific apps used by exposed visitors during the attribution flight. This data offers a deeper understanding of digital behaviors and preferences.

1. Analyzing app categories and names provides insights into our visitors' interests, enabling more precise audience segmentation and targeted marketing strategies.

2.Understanding the apps your audience uses allows you to optimize in-app advertising strategies and improve targeting accuracy.

This chart shows the top apps used by the exposed visitors during the attribution flight. The installed most popular app is indexed to 100 and the popularity of other apps are compared against it.

(Based on google's app name taxonomy)

Interests and Intents provide insights into the online content consumed and shopping intentions of exposed visitors. This data allows for more personalized marketing efforts.

Leverage this data to create highly targeted campaigns that resonate with users based on their specific interests and purchase intentions.

Event segmentation tracks user interest in specific global events and lifestyle choices, helping you target users with high relevance based on current trends.

Targeting based on event interests can increase campaign relevance and engagement, particularly during major events or holidays.

Online Brands analysis reveals user interest in global brands based on their online behavior, helping you understand brand affinity and target users more effectively.

Use this data to align your brand messaging with the interests of your audience, enhancing brand relevance and campaign effectiveness.

Place analysis tracks the total number of visits measured at individual store locations. This metric offers insights into the geographic distribution of your campaign’s impact.

Analyzing visits by location can help identify high-performing stores and areas that may need additional marketing support.

Distance from Home and Distance from Work metrics indicate how far users travel from their home or workplace to visit your store. This data provides insights into your geographic reach and customer distribution & reach

Understanding how far customers travel can inform decisions about store locations,proximity marketing efforts, and customer engagement strategies.

Dwell Time measures the amount of time an exposed user spends in your store during their visit. This metric provides insights into customer engagement and in-store experience.

Longer dwell times typically indicate higher engagement levels. Use this data to enhance in-store experiences and improve customer satisfaction.

Brand and Category Segments show the other brands or types of locations visited by exposed users. This data helps you understand cross-brand behaviors and shopping preferences.

By analyzing cross-brand behaviors, you can identify potential partnerships or adjust your competitive strategy to better meet customer needs.

Lead Time Conversion measures the time between ad exposure and a visit to the store. This metric is crucial for evaluating how quickly your ads influence consumer behavior.

Shorter lead times suggest that your ads are highly persuasive. If lead times are longer, consider strategies to increase the urgency of your calls-to-action.

Competition Footfall Analysis compares your store visits to those of nearby competitors during the campaign period. This metric helps assess your campaign’s relative effectiveness in driving footfall.

The two criteria for competition analysis are:

Geo Behavioral Personas provide insights into the offline visitation patterns of users. This metric helps you understand movement patterns and preferences.

Leverage geo-behavioral data to enhance targeting precision and create more personalized marketing strategies that resonate with your audience.

Visit Lift quantifies the percentage increase or decrease in visits among users exposed to your ad campaign compared to a control group that was not exposed. This metric is vital for understanding the incremental impact of your ads.

Formula: Visit Lift % = (Normalized Exposed Visits - Normalized Controlled Visits) / Normalized Controlled Visits * 100

Represents the proportion of visits observed in the exposed group relative to the total number of users in that group.

- Example: If 2,000 visits were observed from 5,000 users in the exposed group:

- NE = 2000 / 5000 = 0.4 or 40%

Certainly! Let’s break down the Normalized Exposed Visit Rate (NE) using a real-world example.

Scenario:

Imagine you work for a company that runs an online advertising campaign to promote a new offline store. You have two groups of users:

- Group A (Exposed Group): Users who saw the ad.

- Group B (Control Group): Users who did not see the ad.

You want to measure how effective the ad is by comparing the visit rates between these two groups.

Example:

Group A (Exposed Group):

- Total users: 5,000

- Total visits to the offline store: 2,000

Group B (Control Group):

- Total users: 5,000

- Total visits to the offline store: 1,500

Normalized Exposed Visit Rate (NE) Calculation:

1. Determine the number of visits in Group A: 2,000 visits.

2. Determine the total number of users in Group A: 5,000 users.

3. Calculate the exposed visit rate:

Normalized Exposed Visit Rate (NE) =Number of visits in Group A/Total number of users in Group A

NE = 2000/5000 = 0.4 or 40%

Interpretation:

The Normalized Exposed Visit Rate (NE) of 40% means that 40% of the users who saw the ad visited the offline store.

Real-World Insight:

If you were comparing this to Group B, where the visit rate was 30% (1500 visits out of 5000 users), the NE shows that the ad exposure led to a higher proportion of visits. This suggests that the ad might have been effective in driving users to the offline store, since the visit rate is higher in the group exposed to the ad compared to the control group.

Represents the proportion of visits observed in the control group relative to the total number of users in that group.

- Example: If 2,500 visits were observed from 10,000 users in the control group:

- NC = 2500 /10000 = 0.25 or 25%

Brand XYZ wants to assess the impact of their new special burger ad campaign by comparing it to a control group. Here’s how they set it up:

Example:

Group B (Control Group):

Group A (Exposed Group):

Normalized Controlled Visit Rate (NC) Calculation:

1. Determine the number of visits in Group B (Control Group): 2,500 visits.

2. Determine the total number of users in Group B: 10,000 users.

3. Calculate the controlled visit rate:

Normalized Controlled Visit Rate (NC) =Number of visits in Group B/Total number of users in Group B

NC = 2500/10000 = 0.25 or 25%

Interpretation:

The Normalized Controlled Visit Rate (NC) of 25% means that 25% of users who did not see Brand XYZ ad visited the special burger offline store.

Real-World Insight:

In this case, the NC value represents the baseline visit rate for users who were not exposed to the ad. By comparing this with the exposed group (Group A), which has a visit rate of 40%, Brand XYZ can evaluate the effectiveness of their ad campaign. The difference in visit rates helps assess how much of an impact the ad had in driving footfall to the offline store.

3. Visit Lift Index Calculation: The Visit Lift Index is calculated as:

- Formula: Visit Lift Index = (NE − NC) * 100 / NC

- Example: Using the above rates:

- Visit Lift Index = (0.4 - 0.3) * 100 / 0.3 = 33.33%
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Scenario:

Brand XYZ wants to measure the impact of their new special burger ad campaign by comparing the visit rates between those who saw the ad and those who didn’t.

Group A (Exposed Group):

Group B (Control Group):

Step-by-Step Calculation:

1. Calculate the Normalized Exposed Visit Rate (NE):

Normalized Exposed Visit Rate (NE) = Number of visits in Group A/Total number of users in Group A

NE = 4000/10000 = 0.4 or 40%

2. Calculate the Normalized Controlled Visit Rate (NC):

Normalized Controlled Visit Rate (NC) =Number of visits in Group B/Total number of users in Group B

NC = 2500/10000 = 0.25 or 25%

3. Calculate the Visit Lift Index:

Visit Lift Index = [(Normalized Exposed Visits - Normalized Controlled Visits)/Normalized Controlled Visits]*100

Visit Lift Index = [(0.4 - 0.25)/0.25]*100

Visit Lift Index = (0.15/0.25)*100]

Visit Lift Index = 60%
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Interpretation:

The Visit Lift Index of 60% indicates that the ad campaign led to a 60% increase in the visit rate to the special burger store for users who saw the ad compared to those who did not see the ad.

F Real-World Insight:

This result shows that the Brand XYZ ad campaign was quite effective in driving footfall to the offline store. The significant lift (60%) suggests that the ad successfully encouraged more users to visit the offline store compared to the control group who did not see the ad.

Visit Lift is a direct measure of your campaign’s effectiveness. Positive lift indicates that your ads are successfully driving incremental footfall, while negative lift suggests the need for campaign adjustments.