What is Offline Attribution? Why is it important for marketers?

What is Offline Attribution?

Offline Attribution aims to measure the effect of data-driven marketing campaigns across any channel (digital-online, mobile, OOH, print, TV) in driving foot traffic to offline brick-and-mortar locations. This helps marketers measure the success of digital campaigns aiming at driving foot traffic to physical stores. There are two types of Offline Measurement metrics:

1. Visit Attribution – Measures the performance of a campaign using store visitation data. This relies on human movement data or mobile location data.

2. Spend Attribution – Measures the performance of a campaign using spend data. Though it is more granular and accurate compared to visit attribution, gathering accurate spend data is difficult and, if available, the scale is usually too low to be of use.

How does Offline Attribution work?

Marketers can measure online attribution by measuring the lift in online page visits or app downloads from an online campaign. Similarly, offline attribution is being able to measure the lift in foot traffic to physical stores due to online campaigns.  Apart from conversions, offline attribution can also help marketers understand other performance metrics such as customer acquisition cost (CAC), lifetime value (LTV), and average time spent at a store (dwell time).

Offline attribution is tricky to track as there is a spatial dimension that needs to be considered and which can complicate data collection. For example, with online attribution, it is easy to measure success with easily tracked metrics such as website visits or clicks. But when it comes to measuring success for offline campaigns, you might need to track human movement data or analyze location data to measure rise in footfall.

Offline attribution can be measured in different ways and below are three common methods:

• Attribution Windows: An attribution window is a period during which a campaign is being run and all conversions that happen during that period of time are credited to the campaign. For example, you could be running an online campaign for an upcoming music concert. All ticket sales that happen during the campaign period are credited to the campaign. On the other hand, if your goal is to drive offline traffic, the difference in the foot traffic numbers during the campaign versus before the campaign gives you an understanding of the increase (or ‘lift’) in visits.
• Geo-fencing: There have been attempts to pinpoint attribution using data from ad exchanges or a phone’s latitude and longitude. It can be difficult to obtain accurate results with geofencing when used only for attribution as native geofencing technology can deliver accuracy only upto 100-200 metres. It is more effective when used for marketing campaigns.

• Using Location Data: Mobile location data or human movement data has been a game changer for marketers. Using location data, marketers can accurately estimate attribution, especially for online to offline campaigns.

Why is Offline Attribution important for marketers?

Being able to effectively analyze and report on the effectiveness of marketing campaigns is crucial for the modern marketer. Understanding the impact of your marketing efforts (whether online or offline) on store performance can help marketers evaluate and optimize for improved customer experience and drive increased ROI. Here are a few benefits of Offline marketing:

1. Measure marketing impact on store visits: Multiple campaign metrics help advertisers understand the performance of a campaign. But with offline attribution, marketers can measure in-store ROI. If a campaign’s goal is focused on building brand awareness or goodwill, metrics such as Clicks, Impressions, and CTR are most important to measure its performance. However, if the campaign’s goal is focused on driving sales at physical stores, Offline Attribution is the most important metric to gauge its performance.

strong>2. Understand in-store consumer behavior: It is important to understand consumer behavior at physical stores as well as online activity. Offline attribution enables marketers with a more in-depth and multi-dimensional understanding of their audiences by including in-store behavior insights. Store visit intelligence generally includes demographics, brand affinity, and interest-based information.

3. Maximize advertising effectiveness: With insights gained on the audiences that visited stores and their in-store behavior, marketers can improve their audience curation for future campaigns. By using real-world data insights, marketers will be in a better position to generate higher returns on advertising spend.

Challenges with Offline Attribution

Marketers are accustomed to collecting data, measuring clicks, and setting up digital ads in online attribution – but things are not so clear in the physical world. The challenge is not only finding a correlation between online marketing and offline results but causality.

1. Connecting online and offline data: To measure offline attribution, unifying these two different data sets (online and offline) is pivotal. This exercise requires access to large repositories of varied data types and a robust technical architecture to analyze these data sets at scale.

2. Availability of accurate location data: Location data plays a key role in calculating offline attribution. However, the availability of high-volume and high-quality location data is sparse. Additionally, location pings need extensive cleansing and sophisticated data models to derive inferences.

3. Limited access to footfall data: To accurately measure lift in in-store footfall, footfall data is a necessity but due to tech limitations and privacy concerns, gathering all store footfall data is not possible. Offline attribution models, therefore, rely on using a subset of the total footfall as sample sets. This is a challenge for attribution models to ensure accuracy and measure the lift in store footfall from a limited sample set. Therefore, attribution models instead calculate the ratio of the percentage of audiences exposed to the campaign and seen at the physical store to the percentage of non-exposed audiences seen at the same stores to accurately measure the lift. This ratio is called the Attribution Lift Index.

4. Device/Platform based vs User Deduplication: Most attribution models measure attribution by identifying consumers based on device IDs. But device-based models can lead to inaccuracies as a single user could have been exposed to the ad on multiple devices they own. A user-based model is more accurate and matches multiple device IDs of a single user to that individual, avoiding duplication.

How to choose an Offline Attribution vendor?

Many vendors are offering offline attribution. Here are a few factors to consider when choosing a vendor:

1. Offline attribution uses human movement data, a type of mobile location data. It is always good to know your vendor’s primary data source, their measures to ensure consumer privacy laws are being adhered to, how this data is anonymized to keep consumer identity safe, and more.
2. To get accurate attribution, you must either have completely clean data without any duplications or you must find a vendor that can also provide ID or identity resolution. This is to ensure that the consumer identifiers used in ad delivery can be realistically linked to the identifiers on visit data.
3. The vendor must have the necessary scale of data, both online and offline, required for accurate attribution metrics.

Why Azira for Offline Attribution?

Azira offers an independent Offline Attribution dashboard that offers the following features: