Marketers may assume that the location data they purchase is fresh from the source, but the fact is that most vendors don’t use in-house data. That means that unless you have some way of checking to ensure your data’s freshness and accuracy, all you have your vendor’s word. You have to blindly trust that they are selling you what they’ve promised.
That leaves marketers in a bad spot, purchasing data without any way to understand how good it is – or if it’s good at all.
Why does clean data matter?
If you’re wondering what can go wrong, consider this: What if you were trying to target fitness enthusiasts, building an audience almost entirely on data that shows that these consumers go to a certain gym once a week? You have a whole campaign targeting this particular group of gym-goers, and there’s a lot riding on it. Now, what if there’s a fast food burger place directly across the street from that gym? What if that audience was actually going for burgers, fries and a super-sized shake every day at the time the data appeared to show they were going to the gym? That would throw off your campaign entirely; in fact your campaign would be targeting an audience that is exactly opposite of the audience you believed you were targeting, simple because the location data was off by a few meters. So are you 100 percent confident that your audience was going to the gym and not the burger joint?
Precision, freshness and cleanliness of your location data is critically important, as you can see. Even if your data was accurate, if it wasn’t timely, it could still dramatically – and negatively – impact your campaign results.
A proprietary tech stack is a key tool in ensuring data cleanliness, as is a team of data engineers & scientists that knows how to process, analyse & build algorithms to control the flow of data. To illustrate why these are necessary, Blis receives upwards of 40 billion bid requests a day. There is no way a human could check all of those bids and all that data; it simply has to be done by computers and algorithms. Apart from the sheer volume of data, humans can’t do the job nearly as well. Machines can very easily and accurately recognize the patterns and suspect behaviour over time that could point to the presence of bad data.
How is stored location data useful? Why wouldn’t you just use real-time location data?
While real-time location data has its purposes – for example, targeting people who are at the gym right now with an ad for coconut water – historical location data can be incredibly useful for building audiences and segments. It’s great to reach people who are at the gym right now, but consider the value of building an audience of people who go to the gym five days a week at 6:00 AM? Location data overlaid with behavioural data can tell us a lot about consumers and how they spend their time.
In keeping with our current example, imagine that our coconut water company partners with the NFL and Tom Brady to create a promotional video. The advertisers will need to target their fitness enthusiasts at the game, but it’s probably not a smart idea to try to get them to engage with the video while they’re at the stadium. First of all, they’re probably actually watching the game and less likely to engage anyway. Secondly, they probably don’t have access to wifi and wouldn’t want to use all their data to stream the video over a mobile network. Instead, the advertiser will rely on stored data to reach that audience later in the evening, when they’re at home. That way, when they recognize their target users are on a residential IP address, they can reach them across multiple screens, when they’re in a better setting and frame of mind to really engage with the promotional video.
Knowing where an audience has been is one thing, but knowing when and where to serve them an ad is something quite different. Historical location data can tell us so much about who the people in our audiences are; combining that information intelligently with real-time data about what’s going on around them allows you to engage the right audience in the right moment – rather than in the only moment
To better illustrate this point, think of the many marketers who attempt to retarget young mothers online. If you’re a marketer for an ice cream brand, you will probably want to double those efforts when the temperature exceeds 75 degrees. In order to drive mothers to buy ice cream for their little ones on hot days, you need to have historical data that confirms that they actually are women with children, combined with real-time data about the weather in their current location. Location may also factor into the call to action (where is the nearest store for them to purchase ice cream?) as well as the attribution after the interaction. It’s all about tailoring the moment – something we at Blis do very well.
For all this to work as it should, location data must be clean and accurate. Marketers need to educate themselves better on location data and its uses so they can ask the right questions of vendors. Do not blindly trust in the data your purchase from your partners. Ask about sources. Request a demo that includes visualisation tools, like heat mapping and location profiling.
Remember: Even a few meters of inaccuracy could have you targeting French fry enthusiasts instead of fitness enthusiasts. And that, in turn, could mean your entire campaign is off, and your entire budget – not to mention all your effort – has been wasted.
In our next post, we’ll discuss device IDs, and why they’re so important for both targeting and attribution.
