Over 90 percent of the population in Italy has a mobile phone; 71 percent of people have a smartphone. Of 60 million Italian residents, 36.7 million will likely have smartphones by 2021. That’s more than half the population. With so many people carrying mobile devices, there is a lot of location data to be found in Italy – which means there’s a lot of insight available into consumer behaviour.
In addition, OpenSignal reported improved LTE reach in Italy last year, with the majority of operators passing their milestone metric of 65 percent of 4G availability. The combination of strong smartphone penetration and high-speed connectivity is good news for marketers who are aiming to use mobile as part of their strategy. Reaching consumers while they’re on-the-go is important for marketers but relying on mobile data to deliver real-world .intelligence about consumer behaviour is vital.
The data we collect from mobile phones can deliver a lot of insight into the behaviours of the consumers with whom we want to engage. That’s because, logically, where a person goes throughout the day says a lot about who they are. Whether a person goes to the same office every day, the gym three times a week, or the same café every morning, we get an important glimpse into how they live and what matters most to them.
However, all data is not created equally, and marketers need to be cautious about the data they use – not only because it must be permission-based, but also accurate. When it comes to location data, a few metres can make all the difference in the world.
So, if you’re a marketer aiming to reach and engage consumers, how can you be sure you’re getting high quality data? How do you know it isn’t fraudulent, inaccurate, or collected illegally?
Visualization is undoubtedly the best way to tell if the data you’re receiving is good. Location data is generated by unpredictable humans, the data will appear irregular if you plot it on a map. Perhaps there will be larger clusters of activity around densely populated urban areas, but no true patterns should emerge. However, with bad data, you are likely to notice straight lines and other repetitive patterns that look more like they were generated by a machine.
At Blis, we’ve become experts at identifying bad data. As Amy Fox, Product Director at Blis noted, “We can easily spot one single app passing a load of lat/long data that is being advertised as high-quality GPS data.” In fact, only about 20 percent of the data that Blis receives passes our stringent quality standards. The majority of data fails for one of the following reasons:
The mobile data was generated by centroids: While it is adequate for campaigns requiring less precise location data, centroid data does not meet Blis’ standards for precision and accuracy. “Centroid” is really a catchall term for any central location that’s been used to identify where a user is at that moment, but it generally refers to latitude and longitude data that originates from a central IP in a metropolitan area. While accurate, it’s not at all precise: A centroid may be in Rome, but the users could be in Trastevere or corso Trieste. If you’re looking for shoppers of a particular store on the Via Del Corso, centroid data won’t help.
The data isn’t precise: Centroid data may be accurate, but it’s not precise – and precision matters. With location data, precision is typically measured by the number of decimal places in the latitude and longitude (aka “lat/long”) provided in the bid request – in fact, the number of decimal places is a direct correlation to the level of precision. A lat/long that’s been rounded up to one decimal can represent a country or region accurately; two could be a county or district. Three can identify to the street level, but five can precisely define the location of a single tree, and eight can identify a single paperclip on your desk. Blis requires that a lat/long have at least five decimal points to be included in our data sets. We consider anything less too imprecise for campaign use.
Uniques aren’t quite unique enough: In location, when we talk about “uniques”, we’re not thinking of visitors or device IDs, we’re actually referring to unique lat/longs. Since we require a minimum of five decimal places – from that “single tree” level to spaces that are literally two square millimeters – we would rarely see more than one or two devices in a location at a time. As a result, when we see unusually large volumes of data originating from a single lat/long, we know something isn’t right.
These filters prevent nearly 80 percent of bad data from reaching our stores, and there are still additional filters we apply to ensure our clients receive only the highest quality, cleanest data available. Smart Pin, our proprietary data cleansing technology, was purpose-built to validate the accuracy and confidence of all the data we receive.
We’re committed to bringing to market the highest quality location data available, data that is also highly accurate and precise. We understand how that quality, precision and accuracy can impact your campaigns, and we developed Smart Pin to ensure that your campaigns are fueled by data you can feel confident using.
To learn more about Smart Pin and Blis’ real-world intelligence, contact our team today.