A Comprehensive Guide to Store Visit Intelligence

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When retail brand owners invest in mobile marketing initiatives, they often expect results in terms of shop walk-ins. Digital data is frequently insufficient to calculate these indicators. This is when footfall attribution is beneficial.

Attribution studies are carried out to monitor the targeted brands' locations physically and analyze the outcomes in connection to their mobile advertising campaign.

This implies that the study compares the effect of mobile ad exposure on consumers accessing the shop (exposed group) to the others (control group). These metrics are typically known as online-to-offline measurement or store footfall attribution. 

Why is offline measurement important?

Digital measurement indicators are typically unreliable in assessing a store's marketing effectiveness. When retailers depend entirely on online metrics to evaluate campaign effectiveness, they miss real-world business results.

Needless to say, when marketing decisions and tactics are based on incorrect outcomes, organizations suffer. Marketers can obtain a complete picture of how their efforts are doing by analyzing their offline influence. 

Brands must recognize that today's multichannel consumers desire a combination of online and offline experiences. Consider the statistics below.

  • According to 51% of consumers, the main disadvantage of internet purchasing is not being able to touch and feel an item. (Source)
  • Approximately 75% of retail buyers prefer to visit an actual store before making an online purchase. (Source)

These figures demonstrate the importance of store footfall attribution in measuring the effectiveness and success (ROI) of digital marketing in generating visitors to brick-and-mortar companies.

In-store analytics - three levels of benefits - Insider Trends

Source: https://www.insider-trends.com/in-store-analytics-three-levels-of-benefits/

The audience knowledge obtained from store visits will aid in tailored audience selection for future promotions. This is possible with fine-grained actionable insights. A particular campaign, for example, may attract more millennials to the brand's stores. By comparing both credited and control audience insights, these insights assist marketers in discovering what works and does not work.

Why store visit intelligence is hard to measure

Measuring online performance is relatively straightforward, with tools such as Google Analytics allowing marketers to see direct correlations between campaigns and consumer behavior. However, calculating an accurate store footfall attribution comes with many challenges.

  1. To develop an online-to-offline attribution metric, combining online and offline data sets require access to vast repositories of various data formats and a solid technological infrastructure capable of analyzing these datasets at scale. Few companies have skills such as connecting online and offline customer IDs and location definition tools.
  2. There is a scarcity of high-volume, high-quality location data. Furthermore, substantial cleansing and complex data models are required to gain insight from location pings. This is why Unacast has such a diligent focus on the quality and methodology of our location data.
  3. Because collecting 100% of footfall in stores is impossible, offline attribution models must depend on subsets of total footfall as sample data sets.
  4. Because a customer utilizes many devices in the real world, attribution of shop visits necessitates matching all devices of a single user to that individual. While this is a complex process, it is necessary to deduplicate devices and obtain correct data.
  5. A rising proportion of customers utilize a combination of online and offline touchpoints in their shopping journeys, whether viewing or trying on a product in-store before purchasing online or checking online reviews and price points before buying in-store. The pandemic has confused and modified consumer behavior. All of this complicates the work of attribution from online to offline.

Suppose you ask marketers about the online/offline measurement dilemma. They tend to treat it complacently, admitting that full attribution across the digital divide is something they will never truly have. They have learned to accept this blind spot as something they must work around. 

However, marketers fail to realize that without store footfall attribution, they could be missing out on vital data to fuel their future digital campaigns. 

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Best practices for store footfall attribution

There are several best practices for managing online-to-offline attribution.

Handling uncertainty

The offline consumer journey is more complicated than the digital one, and several real-world data sources exist. At various points in the sales funnel, it is analyzed by different people, and there is no standard paradigm to use as a guide. 

The secret to measuring accuracy is building models that can connect data from different sources across different stages of the customer experience and take into account actual uncertainties. Different types of data sets and varied kinds of location traffic have different sensitivities for detecting the campaign signal. It's crucial to fine-tune this periodically.

Choosing the attribution window

As more consumers who were impacted by advertising are included in a broader attribution window, the sample size is improved. An attribution window for offline attribution campaigns is a set time frame during which a publisher may assert that a click or impression caused a customer to visit a business. When it comes to assisting marketers and publishers in understanding when a conversion occurs, attribution windows are a crucial tool.

What is the baseline?

The first-party data has to be baselined before a company attempts to use it for campaign measurement. The brand has to understand how each of these baseline locations performs when there isn't a campaign in place so that there is a clear benchmark to compare the campaign efficacy against when it is implemented. This baseline has to be continuously updated and confirmed.

What does success look like?

Every campaign has its own criteria for success; for example, a 10% increase may be significant for one campaign while being a bad result for another. Marketers must specify what constitutes campaign success and the relative importance of good and negative effects on each brand.

Online-to-offline measurement with Unacast

Unacast can help companies with online-to-offline campaign measurement by providing accurate and precise location data and foot traffic. Our data can help you understand visitor behavior changes and overall campaign effectiveness, analyzing the marketing influence on promoting in-store visits.

The average time spent at a location, brand affinity, cross-shopping behavior, age, gender, home location, and more can all be provided by Unacast. Marketers can also learn about foot traffic trends over the whole attribution window to both their locations and their competitors'. Marketers can use analytics to determine how much the campaign increased in-store traffic and adjust future efforts accordingly.

Unacast data is ground truth verified (r=.93) and shown to be the most precise in the industry. Our solutions are flexible and customizable for how your business wants to see the data, and we partner with you to get you exactly what you need.

Get started with online-to-offline intelligence today

Book a meeting with Unacast to see how we can deliver innovative and dynamic demographic, location, and online-to-offline attribution data. Work with a team of data scientists to get the perfect view of your data. Ready to get started? Speak to Unacast today and ask about a free data sample. 


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