As a marketer, understanding consumers’ behavior has always been of paramount importance to me. Who are they, what are their passions and interests, which brands do they prefer… Location data came to marketers as the holy grail, promising an unprecedented opportunity to understand audiences, help effectively connect with them, and ultimately measure the impact of marketing efforts. However, the marketplace is filled with so many data providers and products that it has become a challenge for us marketers to identify the best data sources. Which brings me to the elephant in the room, what should marketers be aware of when looking for the right data to help them bridge consumers’ online and offline worlds? What matters and what should be disregarded?
According to the US Census Bureau, 9 out of 10 consumer purchases happen in the offline world, this statistic is disconcerting when you think of the limitations that we marketers have been facing when trying to understand consumers’ offline behaviors, trends, and measure offline impact of our online marketing efforts. When location data came about, marketers rejoiced as it finally seemed we had a powerful tool to understand offline behaviors and bridge the gap between the online and offline worlds. What wasn’t necessarily promoted was how most of this data was either taunted by inaccuracy or limited scale, hindering its value and effectiveness, due to the way the data itself was collected. To add insult to injury, the industry lacked transparency on the quality limitations of the data, making it hard for marketers to identify the value of the solutions available. As a result, marketers have been relying on products leveraging either bid stream data from ad exchanges, which lacks accuracy and provides a limited view of user behavior, or panel-based data, which has limited scale and bias issues. Low quality became the accepted industry standard… marketers had a big problem and unfortunately, due to lack of education in the marketplace, many of us didn’t even realize it.
What does this really mean?
According to an MMA report* less than 33% of location data collected through ad exchanges is accurate within 100 meters. As a marketer, this is worrisome if you think of all the uses for the location data: audience segments for campaign targeting, measuring footfall attribution, getting insights on offline consumer behavior. If you are not sure which locations users actually visited, how can you understand their offline behavior? Was the anonymous user visiting Starbucks or was he 300ft away at a car dealership?
In addition to this limitation, bid stream data only captures location at one point in time vs. profiling anonymous user patterns over time. This means that we have no way of knowing if the anonymous user was, say, at Starbucks or just walking by. No wonder that in my days on the Publisher side I heard time and time again from many agencies - and stressed out Account Executives - how audience targeting didn’t work and actually decreased the performance of their campaigns. No kidding!
On the other side we have location data from panels. Despite overcoming the accuracy challenges that ad exchange data faces, this data has equally concerning limitations: scale and bias. Why does it matter? If you ever had the chance of taking Statistics 101 back in college you might remember how one of the first things you were taught was that for survey results to be relevant, you need a representative sample and you want to steer away from incentivizing people surveyed because that will skew your results. There you go, now you have high accuracy but the results are based on a sample that is too small and that in may cases was incentivized to participate.
So how can marketers bridge the gap between the online and offline world? You need both, scale and accuracy. You need to understand which locations users visited, how often and how much time they spent there, and you need to have a user base large enough so that your results are representative. As a marketer I find this opportunity fascinating, and this is what drew me to Cuebiq in the first place: the opportunity to be part of a movement to change the status quo, disrupting how the industry collects, views, and uses location data to help other marketers better serve consumers.
How do we make all of this happen here at Cuebiq? Over a year ago we took a different approach to data collection methodology which allowed us to achieve both accuracy and scale while eliminating the limitations of bid stream and panel-based data. By teaming up with over 180 apps where persistent background location is core to their value proposition, our SDK leverages proximity signals to collect offline behaviors of anonymous users. This data is then enriched with the analysis of frequency and time spent at the various locations which allows us to create the industry’s biggest and most accurate geo-behavioral location data and intelligence available today. What’s even better: because we know that the quality of our data is unmatched and we are firm believers in educating the marketplace on the importance of high quality location data, we offer transparent solutions. This applies to both data collection and its activations. Our collection methodology strictly follows privacy guidelines not only making users aware that location data is being collected but also making it easy for them to opt-out. On the activation side, our solutions provide transparency on the how the data is analyzed and the results. This is a praise we hear from clients day in and day out, when compared to competitors’ products.
In our CEO’s words: “Today, marketers face a unique challenge because the arsenal of data choices is extremely large and still growing. Our goal is to bridge the online and offline worlds, by leveraging accurate data at scale to provide full visibility of offline behaviors.”
*Demystifying Location Data Accuracy, MMA (2015)