Location data is a hugely valuable asset to any marketer wanting to understand user movement and target accordingly. But are marketers really making full use of this rich source of user behaviour? Stephen Upstone (pictured below), CEO and founder, LoopMe, tells ExchangeWire that location data shouldn’t just be useful to target users in their current location, but also provide a rich and rounded profile of a user’s daily movements.
Phone, keys, wallet. It’s the mantra most of us repeat whenever we leave our house or office – and with the advent of Apple and Android Pay it might soon become just ‘Phone and Keys’. Our smartphones are with us wherever we go, and the odd occasion we do leave them behind leaves us feeling strangely bereft.
Not only are our phones always with us but they are always tracking our location. A quick check of my phone’s settings reveals 35 apps that have permission to use my location data; 14 of which are able to access this information all the time.
Using this GPS data to facilitate location targeting is nothing new, brands have been making the most of our smartphone’s tracking abilities for a long time. But when location targeting is discussed or described it’s typically outlined as making use of a user’s current location. Target users with a voucher for coffee when they’re near a Starbucks, send them details of cheap train fares when they get to a train station or ads for minicab companies when they land at a new airport.
All these methods are effective ways of using location data, but they fail to see the big picture. In reality, our smartphones have created a map of the places we visit the most often, outlining our habits and behaviour in a uniquely precise manner. By accessing this data, we are able to build up a much more in-depth picture of who a user is and, by extension, with which brands they are the most likely to want to engage.
For example, a user travels, midweek, to European business centres around three times per month. When they return to their home location, they live in a nice area in the suburbs of London, travelling to an office located in the city. In the evenings they frequently visit restaurant locations before going to a train station and arriving home. From this information it’s possible to ascertain the user is a successful business worker, most likely affluent and probably a decision maker at work. Consequently, this person would be of high value to brands like Cartier or BMW.
Take another user, this time working from an office block. Each lunchtime Monday-Friday their profile shows them visiting high street shops, while in the evenings they typically head to bar locations. At weekends they stay in the city, visiting different retail and entertainment venues. This person is likely younger, fashion conscious with a reasonable level of disposable income. They’re probably not a candidate for the likes of Cartier, but for a brand like American Apparel, absolutely perfect.
Add to this location data all the other pieces of information we can gather about each user, their browsing history, gender, age, demographic, device and the picture becomes even more compelling. By building up a thorough understanding of each user, marketers can determine who is the most relevant to their brand and by implementing artificial intelligence, target only the users who are of the highest value.
Once the advert has been served, location data becomes invaluable as a means of determining attribution, particularly for the retail sector. In the past, measuring the impact of a digital advertising campaign on driving users to store or purchase has been difficult to track, particularly if a product is viewed online and bought in store. Location data turns this situation on its head, providing marketers with the means to determine the ROI of a campaign. By analysing footfall in shops using Geo-Fencing, and linking this directly back to users who have viewed the advertising campaign, it is possible to reveal its efficiency. If the brand is able to share purchase data from stores showing a rise in footfall, then it is easy to determine the link between ads seen and units sold, completing the cycle from digital ad to purchase and proving the value of digital.
This practise should not only be limited to mobile campaigns, but the learnings should be implemented across all media buying. Once it is possible to efficiently track all users across all devices (connected TVs, Smartphones, Tablets, Desktops) location data can have a huge impact on who brands target for their campaigns. Once the campaign is underway, location data can be used to show the value delivered by a campaign, moving beyond brand metrics, proving the ROI on offer from digital advertising.
Whenever location data is promoted as a marketing tool, questions around user privacy arise. It’s important to note that while location data can help outline a user’s preferences, behaviour or audience group, no personally identifiable information is stored. At no point would a user be identified by their name or address; and, should a user have strong opinions on location tracking, it’s easy to adjust settings in all smartphones to prevent location data being shared. As an industry, we should aim to improve the advertising experience for users; location data allows us to deliver better targeted, relevant ads, but, if at any point a user feels uncomfortable, they should, and do, have the opportunity to opt out.
Location data is an incredibly important tool, both for targeting users with advertising that is interesting to them and delivering marketers proof of the ROI of their digital ad campaigns. As an industry, it is our responsibility to use data to the best of our ability, to deliver better-tailored campaigns, and ensure users are receiving an optimum online experience. Without a doubt, location data has a huge part to play in realising these goals.