Mobile was cited by US marketers as the number one area of opportunity in programmatic advertising according to research from eMarketer. Brands who embrace mobile, and recognize its strength for speed and targeting accuracy will be able to harness the power of programmatic to its full extent. To accomplish this however, data must be introduced at campaign inception. With the introduction of a data programmatic platform, buying systems can access millions of data points across fragmented, yet valuable and relevant inventory to meet campaign KPIs. In this article we’ll take a closer look at what types of data are available and how this can be used to reach the right people at the right time and place.
One of mobile’s most promising data sources comes from the fact that we carry our smartphones everywhere we go and thereby open up a unique set of possibilities, unparalleled by anything we’ve seen in marketing before. Unsurprisingly, BIA/Kelsey predicted in a recent study that location targeted ad revenues will soar to $18.2 billion in the US in 2019, up from $4.3 billion in 2014. Location data can be used in a variety of flavours, depending on the goal of the campaign and what technology is used:
The most straightforward use of location data is by sending a message (read: displaying an ad) that is relevant to the location of the consumer right at that time. For example, McDonald’s can show offers from a restaurant nearby to seduce people to make an impulse decision to come and eat there. Another variation is geo conquesting: targeting consumers that are in the vicinity of a competitor’s location to lure them away at the very last minute. Geomarketing is all about leveraging the here and now and is especially potent in combination with impulse purchases such as bargains/deals, (bad) habits, food, and single dollar items.
Audience location profiles
By aggregating data from a person’s geographical movements and combining this with mapped point of interest (POI) data (retailers, airports, stadiums, concert venues, etc.) a profile of this person’s preferences and behavior can be derived. The benefit from this over geomarketing is that it usually entails a much larger dataset which makes it possible to make better conclusions about what ad would resonate with this person, and perhaps more importantly, what ad wouldn’t.
Micro location entails tracking exactly where consumers are inside stores and other venues and how they move through them. Micro location marketing provides a way to take a store’s design into account and send messages that are relevant to the store section a person currently finds herself at. For example, imagine getting a push notification with a discount on cereal when you’ve just passed the cereal aisle in a supermarket. In addition, by aggregating movement patterns heatmaps can be created that indicate in what section customers spend the most time. Although micro location marketing – especially using beacon technology – has been hyped and later ignored en masse, we now see some signs that marketers are starting to adopt it. It won’t take long before most major programmatic platforms will offer in-store targeting as this technology matures. It’s safe to say that this is the final frontier in location marketing as it has reached an accuracy of mere inches.
Historically, marketers have always relied heavily on describing their target customer in terms of demographics. On desktop, the most common way of obtaining demographic data was by analyzing a user’s online behavior and deriving a demographic profile from this. Today on mobile, 1st party data is used more often which provides a vastly more accurate picture. Apps that let users sign up for an account ask for your gender and date of birth and feed this data into ad networks (unless they are as big as Facebook or Google which keep this to themselves and create walled gardens).
However, this 1st party data is not always obtainable because people simply don’t want to sign up for an account and give their personal information to every app they use. This is where mobile DMPs come in. They collect, analyze, and feed back enormous amounts of data about mobile users which allows advertisers to target audiences based on demographics, as well as other parameters.
One of those other parameters is interest. Knowing what interests mobile app users have is a vital complement to their demographics in order to deliver the right content to the right person. Interest data can be especially potent in combination with content marketing. Since content marketing aims to – at some point – convince people to become customers by providing relevant, interesting, and valuable information, it is vital to know if a person’s interests overlap with what you are providing.
Behavioral data and intent
Behavioral data is a bit harder to define because it can entail many different types of data, including location. Audience profiles (in terms of demographics and interests) are often derived from behavioral data coming from different sources. Most notable sources are web-browsing behavior, what apps they use, what physical locations they visit, what ads they click, what products they buy, and much much more.
An absolutely vital part of behavioral data is being able to identify individual users across devices. By failing to do so, inferred profiles are based on incomplete information which significantly decreases ad effectiveness.
Another reason for identifying individuals across devices is to being able to track the path to purchase for consumers. Many people use a variety of different channels to find information about products or services they want to buy and they access these channels on different devices. By understanding how this process occurs it is possible to predict purchase intent which is key to delivering a relevant message at the right time.
In essence, behavioral targeting is based on the complete picture of available data. Who is the customer? What do they like? Where are they? What do they need? Do they need it now? Those questions can only be answered by a comprehensive strategy that takes full advantage of all the possibilities data programmatic platforms offer. In addition, designing an advertising campaign will need to take into account the set of characteristics that come with the product or service you’re promoting in order to take advantage of the unique strengths of the individual data types described above.