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Footfall Attribution Measurement in TV & Cross-Media Advertising Campaigns

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Accurate in-store visitation uplift data is key for advertisers to measure campaign effectiveness in footfall attribution in retail

Data accuracy is essential for precise in-store footfall uplift attribution, and single-source, person-level cross-media campaign data will lead the way for pioneering retailers and QSR brands.

Each week many consumers visit supermarkets and other high street stores for routine reasons as well as DTS (drive-to-store) prompts, so getting an accurate reading on measuring a campaign’s effectiveness in increasing footfall in retail has historically been a challenge for advertisers who increasingly need to attribute outcomes to their marketing spend to be competitive.

What is footfall measurement?

Footfall measurement is mainstream for retailers. It’s the counting of the numbers of people entering the store, known as “store traffic”, and there are long-existing methodologies for doing that. People-counting software is the norm, and brands and agencies use video analytics to examine video streams from CCTV cameras. Usually, this works with a counting unit that performs real-time analysis of the video to detect and count people in-store. The footfall is thus logged and readily available in real-time to the retailer to monitor traffic volumes on an ongoing basis to track the basic health of the business.

What is attribution in advertising?

In marketing, attribution goes beyond pure measurement of what is happening in terms of counted numbers and pushes deeper into understanding effectiveness, understanding what drove the outcomes. 

Measuring marketing effectiveness involves using data analytics to make data-informed insights into what drove those measured outcomes, and analyzing which specific activity or activities can be identified as contributing to driving marketing effectiveness. Understanding attribution is actionable, the insights get applied in marketing planning and tracked accordingly.

Attribution in TV and media is most seen in brand lift measurement studies.

For brand advertising, the primary desired outcomes will be to spot increases in various key brand metrics like awareness, consideration, and purchase intent. Measuring and tracking these metrics is understood to be necessary by advertisers as custodians of the brand’s equity. 

It’s understood that it’s not sufficient to just monitor the movement in the numbers for the various brand health metrics. Why the numbers responded as they did to a campaign has to be understood and it is here that we see advertisers increasingly looking for deeper methods to attribute which elements of a campaign, both media and creative, had the most impact.

Retail footfall attribution can be approached in the same way to the deep benefit of the retailer advertiser or QSR brand.

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How to measure TV footfall attribution in retail

There are different solutions for people-counting, but how to best track campaign effectiveness in driving store visitation is a real issue for retailers and QSR brands amidst this abundance of ‘footfall’ solutions. Counting store traffic numbers, whilst very important, is not actionable without more insight into what’s behind the numbers, and the best way to get that insight is through accurate data.

“To summarize, measurement of footfall is not the measurement of footfall attribution”.

In 2022, attribution analytics in the form of accurate cross-media footfall attribution measurement is the much-discussed lodestar for brand advertisers. Putting reliable first-party data at the front of the quest must apply as the default option where it is available. Single-source and reliable first-party data are viable methodologies for that, here and now, that don’t need statistical workings and related assumptions.

In the context of an increasingly fragmented TV and media landscape, where it is essential to understand how new connected devices are increasingly dominating the media consumption agenda of most users, TV footfall attribution (with all its variants) stands as a specific attribution metric defined as the measurement of impact on footfall uplift that results from someone being exposed to an ad campaign on the TV. Making it a fundamental KPI for any retailer or QSR brand, that wants to measure the effectiveness of their TV, CTV, BVOD, and AVOD ad campaign, and determine how it is driving offline outcomes with the selected target audience.

Accordingly, TV ad footfall attribution is not only critical to optimizing media campaign ROI but demands the most accurate single source and first-party data to track whether ad exposure was followed by in-store visitation or not.

Having a clear understanding of offline outcomes makes the planning process actionable for advertisers who can then effectively attribute behavior using 1-2-1 data to match TV ad exposure with store visitation.

Good data addresses with some certainty whether a campaign contributes to results. In the case of data-driven TV footfall attribution, the desired outcomes are two-fold. Footfall uplift and adding the store’s own first-party transaction data, whether increased sales resulted from the uplift in visits.

Cross-media footfall attribution data can also enable an advertiser to not only measure the effective uplift, but it can also enable the advertiser to optimize campaigns in-flight.

It’s possible to see how a campaign is performing across channels, even at an individual ad level in terms of driving visits. This enables optimization by rotating creatives and in planning terms, having insight on the optimal budget allocation by channel to be most effective in driving uplift.

The complexity of footfall attribution measurement

Single-source data is essential for capturing accurate retailer out-of-home footfall uplift in the context of advertising effectiveness measurement.

That requires a methodology that seamlessly provides first-party data for both the key elements, cross-media campaign measurement of TV and radio (reach and frequency) and location, from the same source.

This immediately removes the need to begin the attribution analysis with fused data sets with all the inbuilt assumptions and algorithms that underpin that methodology. The output of bringing various data points together, in effect, creates an integrated third-party data set.

“Why use a data source that isn’t deterministically sound and risk the introduction of data imprecision into the complexity of building full-funnel attribution analytics?”

Therefore, having a first-party data set is especially important for the retailer and QSR advertisers who want to, as accurately as possible, work multi-touch attribution down the full marketing funnel from brand awareness at the top to conversion at the bottom. 

Understanding consumer in-store visitation uplift and mid-funnel footfall attribution

As already stated, consumers go out to shop and visit a particular store retailer for various reasons (brand reputation, range, price, convenience, weekly routine, habit), and seeing an ad campaign can prompt a visit if a new product is being listed or there are time-limited offers to be had. Thus it would be accurate to say, that ad campaigns do drive some consumers to visit a store in the same way some ad campaigns sometimes affect their top-of-the-funnel brand perceptions more than others. 

It has been proven that single-source footfall attribution data can pinpoint whether a person has been exposed, or not, to the campaign when visiting the store and determine if they are, therefore, attributable to visitation uplift analytics.

In this context, a campaign’s measured effectiveness in driving store visitation, and particularly visitation uplift, can be designated as mid-funnel attribution because this insight does not allow the retailer to attribute an “uplift visit” to conversion further down the to the bottom of the funnel.

Towards understanding fuller funnel TV footfall attribution

Integrating single-source mid-funnel attribution data with a store’s own first-party loyalty card data will open the route to fuller funnel attribution, based on exposure or non-exposure to a campaign. Understanding the effectiveness of their campaigns and letting brand advertisers gauge which channel mix of ads was most efficient in driving in-store visitation uplift, across TV and radio, across genres (brand, product, and offer ads), or individual creatives. 

First-party data integrations will enable the possibility of understanding the optimum average of cross-media frequency.

First-party data integrations will also enable the possibility of understanding what the optimum average of cross-media frequency is, thus ultimately setting the first stepping-stone to taking footfall attribution through to conversion. Which retailer wouldn’t want to know the percentage of consumers who bought a specific product or shopped a specific offer who had been exposed to elements of an ad campaign and then leverage that insight?

Passive ACR technology holds the key to fully understand TV footfall attribution

Footfall retail attribution data can only be reached with first-party data from a single source.

It’s exciting to see how brand advertisers, turning to alternative cross-media measurement technologies, are enticing the adtech industry to push for the said ground-breaking solutions to come to the fore.

The current status quo portrays a scenario where existing audience measurement currencies are challenged by KPIs like cross-platform footfall advertising attribution (based on content matching with highly accurate geolocation software collected from the same source). This phenomenon has paved the way for globally innovative media measurement solutions based on new software breakthroughs, such as for instance, passive ACR (Automatic Content Recognition) technology, designed for the single purpose of ad cross-media campaign measurement. 

The truth is that today, in 2022, cross-media full-funnel solutions from a single source and based on ACR and geofencing technology (up to 5 meters) do exist. Allowing brands to pinpoint when a consumer enters a designated store, their in-store dwell time, as well as whether the in-store visit can be attributed to a consumer who has already been exposed to the ad campaign (the passive R&F data previously collected by the ACR technology).

Join the future of cross-media audience measurement and footfall attribution analytics with confidence and single-source transparency.

Picture of By Keith Johnson
By Keith Johnson

Beatgrid GM UK

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