Along the top of your reporting dashboard is the key metrics that summarizes the performance of your campaign. The key metrics has three sections as follows:

Impressions: Total number of Ad views delivered in the campaign. Whether the Ad is clicked or not is not taken into account. Each time an Ad is delivered for the campaign, it is counted as one impression.

Clicks: Click on the Ad in any channel (Subset of Impressions). Every time someone clicks on an ad this is recorded and then displayed as the total number of clicks.

Total Exposed Visits: This is the number of visits that happened after seeing the Ad. If the same person visits twice to the store, then it is counted as two visits.

Campaign Reach: Total Number of Unique Devices that were reached by the campaign.

Frequency: Frequency is the average number of impressions delivered per unique device.

Visit Index: This is to represent what percentage of the impressions ended up in visits.
Example: If 1000 impressions leads to 1 visit, visit index is 1

​Exposure Index: [Total Number of Exposed Visits / Total Number of Visits]. This gives an indication about the effectiveness of the Ad campaign. Exposure index of 1% suggests that the campaign has managed to reach 1% of your total organic visitors.

Conversion Index: Exposed visits represented as a percentage of reach.

Cost per exposed Visit: This gives the cost incurred to get one Exposed Individual to the store.

​​CTR: The ratio of users who click on an ad in comparison to the total number that
were exposed.

Visit lift index shows the % of lift, positive/negative, by comparing normalised visit rate of exposed group with the normalised visit rate of the control group. (Control group is derived by Factori's lookalike algorithm). Visit Lift Index = (Normalized Exposed Visits – Normalized Control Visits) * 100 /Normalized Control Visits.

*Normalised controlled and exposed numbers refer to the scaling up or down of values as per the scale of population observed as part of the campaign and the population of visits observed by Factori as compared to the actual visits observed at the respective stores.

It is a measurement of the impact of an advertising campaign and is primarily used to identify the effectiveness of various channels in bringing more visits or conversions. It indicates the effectiveness of a particular channel vis-a-vis the whole campaign.

As an advertiser you want to know the amount spent on each channel and its effectiveness in bringing visits to the outlets.
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Compares the visit index of all respective line items to the overall campaign's
visit index.

Provides a breakdown of delivery and performance by creative

Provides a breakdown of campaign delivery and performance by publisher.

Provides a breakdown of delivery and performance by device type.

Provides a breakdown of delivery and performance by custom values. Custom
values can be added to differentiate between different creatives and strategies.
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Filter your Report Data

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.

The Cohort analysis chart below provides additional conversion analysis by cohort. The cohorts are subsets of the total exposed audience grouped together based on exposure to the campaign on a given day, providing a visual representation of the conversion pattern by cohort groups for the remainder of the attribution window.

This Heat Map below shows the visit rate by time of day/day of the week. The last impression / click received prior to the visit is attributed to the actual visit. The darker the colour, the more visits were generated by impressions delivered during those times.

The distribution of exposed visit against the frequency of engagement.

The Gender graph below shows the percentage of visitors by gender (Male Vs Female) out of the total incremental visits.

Exposure Index: If the total incremental visit is 100, then according to the graph below, exposure index of female and male are 11.8 and 13.5 respectively. It means out of every female visited your store, 11.8% of them were exposed to the ad. Similarly out of every male who visited the store, 13.5% of them are exposed to the ad.

Visit Index: It shows the number of female visitors converted for every 1000 female audience reached.

The Age graph below shows the percentage of visitors by Age group from the total incremental visits.

Diving into what operating system and devices your website is accessed from can help you concentrate your testing on the devices that your customers are actually using. Use this analysis to understand your customers. Focus on optimizing the user experience on the most popular devices.

Provides breakdown of delivery and performance by carrier network and also the  top carriers of the exposed visits. 

Provides a breakdown of what time and day of week a user will click on the ad
and convert into store/location.

The heat map below visualizes the days and times when consumers (exposed visitor) visited the locations (POI visit time). The darker the color, the more visits occurred during those times.

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

(Based on google's app category taxonomy)

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)

This chart shows the top interests (online content) consumed by the exposed visitors during the attribution flight. The installed base of most popular interest is indexed to 100 and the popularity of others are compared against it.

Factori receives data from many partners to deliver high- quality pieces of information about users’ shopping intent. We collect data from sources connected to the e-commerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones or cars. Recency of data can range from 7 to 14 days depending on the product category.

This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviours. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time.

This category was created based on the high interest of users in terms of content related to specific global brands based on visits to their websites or brand names being searched for online. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time.

The total number of visits measured per individual store/ location.

Shows the distribution of distance traveled to the attributed store from the inferred home locations of the exposed devices.

Shows the distribution of distance traveled to the attributed store from the inferred work location of the exposed devices.

Dwell time indicates the time spent by a device at the store location.

Brand Segments: This shows the top brands visited by your customers apart from your brand. By knowing other brands that your customers are visiting will help you to understand them better.

Category Segments: This shows the top places categories visited by your customer (i.e. Fast Food, Cafe & Lounge, Hypermarket, Car Service, etc.)

A lead time to conversion is the latency between the time of seeing the Ad and visiting the store. For example, the lead time between the viewing of an Ad and visiting the outlet may be anywhere from few hours to few weeks. In the following figure, almost 50% of the visits happened within eight days after seeing the Ad impression.

Compares per store average footfall  of attributed brand against a max of 3 competitors.

Methodology : 

This shows the top offline movement patterns based on geo-behavioral segments that the exposed devices are part of.

This shows the % of lift, positive/negative, by comparing normalised visit rate of exposed group with the normalised visit rate of the control group. (Control group is derived by Factori's lookalike algorithm). Visit Lift Index = (Normalized Exposed Visits – Normalized Control Visits) * 100 /Normalized Control Visits.

*Normalised controlled and exposed numbers refer to the scaling up or down of values as per the scale of population observed as part of the campaign and the population of visits observed by Factori as compared to the actual visits observed at the respective stores.