What if mobile advertisers could identify the mobile user who has intent to buy? What if they could also pinpoint those users who are in a state of mind to actually make a purchase?
While audience data and targeting capabilities have been around for years, the use of custom audience creation methodologies is gaining momentum. (See our full report, released this week: Intelligent Audience Creation.)
It’s all about “big data” these days, in which publishers and app developers are gathering more and more data from their users and developing new ways to collect, store and leverage that data to target their advertisements. And this means that identifying the mobile users with an intent and with a state of mind to make a purchase is actually feasible.
Behavioral targeting has become one of the most critical components of mobile advertising. By examining a mobile user’s past actions, demonstrated interests and purchase intent, advertisers can hone in on a very specific subset of consumers that will be the most receptive to the campaign. And that can make for a higher ROI and a more effective ad spend. Because at the end of the day, now matter how captivating the ad, it falls flat if you don’t reach the right audience at the right time and in the right context.
So just how can we accurately observe and understand a users behavioral activity patterns? That’s where the practical use of the data comes into play. By capturing large amounts of data over a significant amount of time and from a series of contextual events, patterns and habits become evident.
Past behavioral patterns have proven to be particularly accurate indicators of future actions. Take for instance a user who has installed apps in the past as a result of a response to an ad. That means he is more likely to do so in the future, so advertisers are more inclined to show them a similar type of ad, and expect a similar type of response. Users who have not installed an app in the past, on the other hand, might be shown a lower paying “house” ad, since the expectations of that user to make the purchase are also lower.
The time of day and week also seem to have an influence on a users engagement with advertisements. For example, in Western Europe, user engagement over the course of a 24-hour day showed two primary engagement periods: one in the morning (6-10am), which was driven primarily by an interest in news, and one later in the day (6-9pm), where we see an increased interest in sports, weather and social networking.
Another component of behavioral targeting involves determining whether mobile users are in a “need” or a “want” state. In a “need” state, the user accesses a mobile site or app with the intent to satisfy an immediate desire or purpose, such as searching for a nearby Starbucks or locating the right tool for a home project. And in this case, the user is not receptive to advertisements. In a “want” state, however, the user is most receptive to brand advertising messages, as they appear to be browsing in a more recreational manner, and are more open to discovering new information.
The frequency in which a user accesses a site or an app is another important metric when it comes to understanding his or her behavior. Some sites, like sports, news and social sites, typically see their users early in the time cycle. For example, over the time span of a month, sports sites will more than 50% of their monthly users within the first week, with an additional 20% identified in the next 10 days. Other sites, such as style and fashion or food and drink sites, may go much longer before they acquire the same amount of users.
While frequency does reflect interest, helping mobile advertisers decide which period of time to focus on, the time spent on a particular site or app is another important consideration to make. After all, if the user is on only briefly, they must not have a vested interest. It’s also important to consider that time spent is influenced by such factors as cost of data plans, the availability and quality of high-speed networks and the mode of engagement (web vs. app).
Now, up until this point, data on user behavior has mostly been collected and analyzed through their use of a single site or mobile application. But with the advent of cross-site activity analysis, behavioral targeting just became next level. Not only does understanding cross-site activity provide a deeper understanding of a user, it allows mobile advertisers to develop a more complete view of who their audience really is, and as a result, how they should gear their advertisements.
Behavioral targeting helps mobile advertisers get more granular in their selection of audience variables, and ultimately, achieve a higher ROI. With that said, it is important to recognize that the more granular the audience segment becomes, the smaller it will be. So mobile advertisers will need to learn how to balance targeting efficiency with scale potential. After all, the idea is still to increase your audience and grow your piece of the pie.
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