Location Data can Boost Consumer Engagement…When Used Correctly
Today, consumers are inundated with dozens, if not hundreds of product or service ads on a daily basis. As a result, it’s often the case that these ads have simply become white noise to be ignored.
The traditional method for visit attribution has been to take traditional census block group (CBG) level data and generalized household demographics data that provide a sketch of a target audience, but this data often lacks the depth and context needed to meaningfully engage consumers. In other words, the data generates white noise instead of results.
Modern brands with highly successful campaigns are now turning to location data, specifically utilizing points of interest (POI) data that can help generate a path to meaningful engagement, and help significantly increase return on ad spend (ROAS). According to the Local Search Association, ROAS increases by 45% when targeting a CRM list with granular interest based targeting.
Whereas the traditional method would bombard a passerby near a retail strip with generalized and ineffective ads, or even the wrong audience entirely, enriching these other data sources with accurate points of interest takes actual visit and dwell times by consumers and creates ads that are tailored to them and targets them in a more effective way.
The goal here is to fully understand what the customer desires and to meaningfully interact with them in a way that grabs their attention and caters to their wants and needs in a discerning and sophisticated way.
Too often, visit attribution is seen as a blunt instrument based on proximity and guessing. But it doesn't have to be when built on top of accurate and robust POI data. Spurred on by the pandemic, the rapid transformation of the physical landscape means that location databases risk becoming obsolete faster than ever. In the last month, we saw at least one new store opening for 18.8% of brands across the globe. We also saw at least one store closing in 16.4% of brands across the globe in the same time period. With all these changes just in the last month, annual updates are no longer sufficient as an input into a sophisticated model.
Beyond global brands that are most commonly associated, it’s important to also acknowledge the rich tapestry of independent businesses as a segment. In fact, nearly 75% of US retail locations are independent or non-branded, colloquially referred to as “mom and pop” stores.
The challenge that some location databases experience not only lies within timely updates, but also the ease in pulling data for mom and pop stores over large, well known brands. Overlooking such a vast segment is detrimental to a comprehensive consumer profile.
Today's consumer is discerning and sophisticated. They yearn for meaningful interactions, not generic advertising spam. A passerby near a retail strip shouldn't be bombarded with ads for every store they glanced over or likely ignored completely. Instead, genuine intent is showcased through actual visits and dwell times.
Accurate modeling of consumer behavior requires more than just building footprints. While these are relevant for standalone businesses, multi-tenant spaces necessitate intricate polygon representations. Simple proximity isn't enough; we need to comprehend the depth of a consumer's interaction with a place.
Furthermore, while advancements like 5G are set to enhance some types of mobile data accuracy, refining the algorithms remains paramount. It’s not enough to cluster pings to a general area. Meaningful insight comes from understanding the broader retail context of the location. Any newly opened stores? Any recently changed store hours? Do some of the surrounding stores operate at distinctly different times of day? This can help actually determine who visited which store with accuracy. And by factoring in variables like time of day and visit frequency, you can delineate between potential employees and customers, refining our understanding further.
Data that doesn't always capture the changes of the physical world is costly. Advertisers must prioritize data quality to validate the accuracy of visit attribution methods.
According to an IBM independent study, bad data costs the US economy approximately 3.1 trillion dollars per year. This impact is felt by 88% of companies, costing them about 12% of their revenue.
A robust framework is essential, encompassing precision to ensure the accuracy of each POI in reflecting real-world status. Evaluating recall provides insights into dataset coverage for particular brands or geographies, benchmarking against industry leaders like Google, while column precision focuses on the accuracy of specific data columns like open hours, address, closed, duplicates, and more.
Regular data updates are also crucial for maintaining accuracy and relevance in a dynamic environment. Unlike the traditional practice of annual updates, frequent refreshes ensure that data sets remain current and reflective of real-world changes. Having data updated more often than yearly allows businesses to adapt swiftly to evolving trends, consumer behaviors, and market dynamics.
When evaluating data, this level of transparency is a game-changer for advertisers, facilitating a deeper understanding of overall data quality and empowering them to elevate their campaigns.
In essence, the goal is to fully understand what the consumer desires. And to achieve that, accurate location data is indispensable. By refining our algorithms and acknowledging the richness of the physical landscape, we can transform the advertising landscape. It's about engaging the consumer meaningfully, echoing their sophistication in our approach, and genuinely meeting them halfway.
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