An attribution report is used to understand the impact of your campaigns on offline location visits. There are several key metrics that are measured to enable marketers to understand what elements of the campaigns work to drive in-store visits.
Viewing your report
Let's look into the basic view of an Attribution Report.
A. Key Metrics
A. Key Metrics
Along the top of your reporting dashboard is the key metrics that summarises the performance of your campaign. The key metrics has three sections as follows:
Impressions: An impression is the total number of Ad delivered for the campaign. Whether the Ad is clicked 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.
Campaign Reach: Total Number of Unique Devices that got the impressions.
Frequency: Frequency is the average number of impressions delivered per unique device (exposed member of the audience).
Total Exposed Visits: This is the number of visits that happened after seeing the Ad. If a same person visits twice to the store, then it is counted as two visits.
Exposure Index: [Total Number of Exposed Visits / Total Number of Unexposed 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.
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
Confidence: This attribution report is statistically estimated with a confidence of 95%.
Cost per exposed Visit:
This gives the cost to get one Exposed Individual to the store.
B. Filter your Report Data
You have some filtering options to choose from in order to customise a report for your business. So let's go over each option you have and your choices:
1. Date Range:
Choose a date range. Here you'll choose what date range you'd like to focus on in this report and in what time range.
You can filter report by custom values that you have given when you have created the tracker. For example: You have uploaded three creatives and you have named them as banner A, banner B and banner C respectively. Now you want to know the performance of these creatives. Filtering the attribution report based on creatives that have been viewed on your website to figure out which creatives attracted the audience well. This will help to understand which creatives/pages should be promoted, which creatives/pages should be optimised, and which creatives/pages are helping to push people down the funnel quicker than others.
3. Engagement Filter (Impressions Or Clicks):
You can filter the output based on either Impressions(Views) or Clicks. By default, the report is shown based on Impressions (Views)
Our report measures two different types of conversions: Click-to Visit and View-to Visit
Click-to Visit is counted when a user clicks an ad and then converted as a customer by visiting your outlet.
View-to Visit counts customers who were shown an ad and did not click on it, but converted later.
C. Four sections of the Report
For the sake of convenience, the attribution report is divided into four major sections (four tabs).
- Tab 1: Attribution
- Tab 2: Engagement
- Tab 3: Places
- Tab 4: Conversion
You can navigate easily to other sections of the report by clicking on the tabs.(Refer figure below)
Visits is the total number of tracked footfalls at the POI. Conversion Rate identifies what percentage of Impressions/Clicks got converted as incremental visits at the POI.
If 1000 impressions leads to 1 (incremental) visit, then conversion rate = 0.1%
If 100 clicks leads to 1 (incremental) visit, conversion rate = 1%."
In the above graph (figure 2), you can view the conversion rate in four different views:
1. Custom Range:
This shows the conversion rate for each day during the specified date range. From this you can understand how many incremental visits you received during that date range.
2. Campaign Period:
This shows the conversion rate for each day for the whole campaign period.
3. Average of the Week:
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.
Use this graph to identify the peak day of the week when you receive more traffic. If the peak is during a weekend, then consider adding a special offer during the weekend eve to create a last minute rush of sales. If the peak is around weekday, then make sure you promote your brand with advertising heavily during weekday, to draw in the biggest number of visitors.
4. Average of the Day:
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.
Use this graph to identify the peak times of the day when the website receives traffic. If the peak is just before midnight, then consider adding a special offer on the website that ends at midnight to create a last minute rush of sales. If the peak is around lunchtime, then make sure you promote the website with advertising heavily around 11am, just before lunchtime, to draw in the biggest number of visitors.
The Gender graph below shows the percentage of visitors by gender (Male Vs Female) out of the total incremental visits.
Ex. If the total incremental visit is 100, then according to the graph below (figure x) 67 of them are female visitors and 33 are male visitors.
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 optimising the user experience on the most popular devices.
Example: If the total incremental visit is 100, then according to the graph below (figure x) 18 visitors are using Apple iPhones.
Location data can be useful for targeting your marketing and advertising and understanding who you are reaching. By knowing the top location where you got more visitors, you can focus on those locations to increase your sales. Similarly by knowing the least performing locations, you can optimise the ad campaigns in order to increase the performance in those locations. What level of detail is of interest to you depends on your organisation: maybe you care about the audiences across all the outlets, or maybe in specific outlets where you do business.
Top Brands: 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.
Top Places: This shows the top places (POIs) visited by your customers.
Top Categories: This shows the top places categories visited by your customer (i.e. Fast Food, Cafe & Lounge, Hypermarket, Car Service, etc.)
Lead Time to Conversion
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 60% of the visits happened within two days after seeing the Ad impression.
Impressions / Clicks Analysis
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.
Heat map visualises the days and times when consumers (exposed visitor) visited the locations (POI visit time). The darker the colour, the more visits occurred during those times.
To "Export" the attribution report, click on the EXPORT ATTRIBUTION REPORT button on the top-right corner of the report page. You can export the report in two formats:
1. Export as CSV (raw data)
2. Export as PDF (coming soon)
Understanding the terms
When making a decision here on filtering / exporting your report, it's important to know what these terms means and exactly what choices you should be making when customising your report. To view the complete set of definition of these terms, visit the Glossary of Terms.