The Perils Of Inaccurate Location Data And How To Avoid Them
The potential for location data from mobile phone usage is huge, but it’s all made redundant unless tech experts can guarantee clean and accurate information. Amid concerns about where ad spend is apportioned, how can we ensure that data is accurately extracted and applied by brands at different points of the consumer journey.
There are three key data points for understanding a consumer: device ID showing latitude and longitude, point of interest (POI) showing information about the context of their current location (for example, if they are in a shop) and timestamp, showing time of day and date. By applying this insight, a consumer’s routine can become more than just a string of numbers and you can start tracking someone’s day to build a behavioural profile.
However, there is a vast amount of location data in the market and not all of it is accurate. Data testing is a process to be treated rigorously; with only 25% of data sources passing our tests, we know how much inaccurate or fraudulent data there is in the field.
The importance of accuracy
It’s really important to understand what constitutes good and bad data. Data extracted from muddled sources, bots or combined with missing information is likely to be inaccurate. If a very broad location radius is used, often with data being aggregated on a city level, it is also not fit for purpose. “Bad data” could show 340,000 unique users in a square metre. Alternatively, it may show the location as within many decimal places of the target or, sometimes, it can even get the country code wrong.
“Good data” can be accurately extracted and applied by marketers using GPS location and behavioural data collection directly from a device by embedding a small piece of code, the software developer kit (SDK), into an already installed app and extracting data across a typical smartphone user’s day.
For example, a user’s smartphone activity in a single day can provide a lot of crucial information for a campaign. From the weather activating a specific campaign first thing in the morning, to their IP address at work showing they’re an office worker, or their browsing history showing they’re a football fan. All of these are potentially valuable insights to ensure an individual is served the right ad at the right point during their day.
The consequences of scaling up campaigns based on bad data
It’s really important that media owners are transparent about the source and quality of data they are using, which requires the verification of tech, data sources and inventory. If any of the three key data points outlined earlier are wrong, then all profiles built on it will be completely incorrect. It’s also essential to ensure that all insights derived are as good as the source data.
Avoiding pitfalls in the future
Due to the potential pitfalls of bad data, it’s no wonder that location intelligence platforms are collaborating with independent validation companies to guarantee their data is 100% accurate to put brands’ minds at ease over fraud, brand safety and viewability. Accurate data is also a non-negotiable starting-point to ensure strong results. Working with certain data partners helps brands to build a clearer picture of the customer journey in order to measure campaigns more precisely and develop a deeper understanding of campaign attribution. The holy grail for advertisers will be the ability to understand the effects of their ads on store visits based across all cross-screen digital displays.
Click here to see the original article on Digital Doughnut.
Tags: Andy Beames, Digital Doughnut, Inaccurate Location Data