How Store Visit Intelligence Can Increase Brand Foot Traffic

How can brands measure Online-to-Offline conversions?

The success of online campaigns in the offline world is measured with respect to the ‘lift in-store visits’. At Azira, the lift in-store visits is calculated as a ratio of the percentage of exposed audience seen at the relevant physical stores to the percentage of non-exposed audience seen at the same stores. This ratio is called the ALI (Attribution Lift Index). An ALI of more than 1 signifies an effective campaign. To calculate the ALI, brands usually consider an attribution window which is the time period from the launch of a campaign to the time a conversion is made. Usually, attribution windows range from 7 days to 14 days depending on the nature of the campaign.

Why do brands need Store Visit Intelligence and Measurement?

Store visit intelligence and measurement help brands understand how marketing campaigns are changing consumer behavior and learn how to decrease the cost per incremental visit to increase ROAS. There are several ways store visit intelligence can be of great value to marketers:

1. Measure marketing impact in the offline world

There are multiple campaign metrics that help advertisers understand the performance of an online campaign, but offline measurement remains a challenge. With an independent and platform-agnostic online-to-offline attribution technology, marketers can measure in-store footfalls linked to each campaign. 

2. Gather store visit intelligence on audience

Store visit intelligence can provide granular audience insights both on the attributed and the control audience. This real-world intelligence includes demographics, brand affinity, and interest-based information. Such actionable insights can enable marketers to have an in-depth and multi-dimensional understanding of their audience.  

3. Maximize the effectiveness of advertising

Store visit intelligence gathered on the audience will facilitate personalized audience curation for future campaigns. This can be achieved through fine-grained actionable insights. For example, a certain campaign can drive more millennials into the brand’s stores – that is a good way of understanding what works or does not work for different segments of the audience by comparing both the attributed and the control audience insights.

What factors should be kept in mind before Implementing Store Visit Attribution?

As per our recent survey, while a whopping 89% of marketers have an online attribution technology in place, only 33% of marketers measure success in the offline world. This difference makes offline attribution a potential key to remain ahead of the competition. Though measuring the consumer journey is now a mature science in the online world, calculating an unbiased and accurate store footfall attribution still poses several challenges for brands and agencies at large:

• Connecting online and offline data sets at scale

While data currency for online data sets includes cookie IDs and device IDs, offline data sets use latitude and longitude specifics. In order to create an online to offline attribution, unifying online and offline data sets is important and this requires access to large repositories of varied data types and a robust technical architecture to analyze these datasets at scale. Not many companies in the data space are equipped with capabilities such as linking online and offline consumer identifiers, location definition mechanisms, baseline definition for attribution lift calculation, and befitting reporting tools. 

• Availability of accurate real-world data

The availability of high volume and high-quality location data is sparse. Moreover, location pings need extensive cleansing and sophisticated data models to derive intelligence. For example, if a location is seen only as a pin and not as a polygon, the results might be inaccurate. 

• Limited access to footfall data

Gathering complete and accurate information on the entire footfall in stores is not possible and offline attribution models have to rely on subsets of the total footfall as sample data sets. 

• Device/platform based vs user deduplication

Attribution models use device-based models to measure online to offline conversion.  But since a consumer uses multiple devices in the real world, store visit attribution requires matching all devices of a single user to that individual. While this is a challenging task, it is crucial in order to deduplicate devices and get accurate results.

• Consumer behavior is a complex subject

According to eMarketer, about 43% of millennials research online and buy in-store, while 13% research in-store and buy online- and then there are other categories- consumers who strictly stick to either online or offline channels. Consumers are not completely moving to single touchpoints-not anytime soon- and that makes the task of online to offline attribution even more complicated.

Everything you need to learn about Store Visit Intelligence and measurement is here