Understanding Mobile User Behavior Is the Driver for Mobile Success

The rise of ad blocking is threatening a major source of digital and mobile revenue, but it's also creating an opportunity to make marketing better on mobile devices. Does anyone really believe that banner or video ads--which were first repurposed from TV to the web and are now being repurposed from the web to mobile--are the optimal form of mobile advertising? The truth is that ad blocking has been around since the 1990s, yet only in the last few years have people started to really use ad blockers. The combination of ad intrusiveness, data costs, privacy concerns and latency issues have created the perfect storm, which finally made ad blocking take off. But I believe that it will bring about a search for new, better and more native-to-mobile forms of marketing that will better serve the entire mobile ad ecosystem–marketers, users, publishers and app developers. For years, digital marketers have promised the ability to serve the right ad to the right user at the right time. Unfortunately, that rarely happens. One could even argue that if it were happening, users wouldn't be downloading ad blockers. But that's exactly the problem that mobile marketers should be looking to solve: to find the right time and channel to serve a relevant marketing message to their users. With millions of vertically focused applications installed on millions of mobile phones the world over, the industry has access to a tremendous treasure trove of data. And with users providing billions of clues based on downloading, engaging, swiping and scrolling, we need to start doing a better job of uncovering the signals from all of the noise, and then understanding how to translate these signals into actions that result in offering the right user the right application at the right time. For example, the success of the FitBit and other health- and fitness-focused devices and applications has highlighted the importance of being physically fit today. So if it's important to be physically fit, when are people more open to dieting: When they're eating or when they're exercising? From our research, we've seen that people are far more open to recommending a diet app when they're exercising than when they're eating. From this data point, we can glean a number of behavioral attributes. First, guilt is less of a catalyst for exercising. Second, exercisers are connected and engaging while they're exercising. And third, different achievements through different exercises are correlated with the willingness to share, which helps optimize sharing across and the network. Today, algorithms don't need to write down and think about their results–they learn over time automatically when and how best to serve ads. But as the humans who are coding these algorithms, we do need to stop and look at the behavioral attributes that we're learning from the behaviors that our algorithms are uncovering in their work. Another example that our algorithm was able to uncover in a popular recipe app was the strong effect of two seemingly unrelated models when combined together. The first model was measuring where users spent their time inside the app, and the second measured whether users were walking, standing or the smartphone was completely still. When investigating the reasoning behind those dramatic results, we found that the combination of those two models accurately described three popular behavioral patterns of how people used this app. The three patterns were: walking in the supermarket buying ingredients for a recipe, preparing a recipe when the phone is typically laid on kitchen table and when users are either sitting or just browsing for ideas. There is so much that we can learn from all of the data touch points that our algorithms are uncovering in our data centers, but we need the time to look for those needles in the haystack–the data points that are indicative of user behaviors–and let these behavioral crumbs lead us to better, more relevant marketing touchpoints. For too many years, we've been promising consumers relevant advertising. Now, by understanding user behaviors, we have an opportunity to finally deliver on that promise. Isn't it time we actually did? Nimrod Elias is the founder and CEO of mobile app growth provider TapReason. Image courtesy of Shutterstock.

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Understanding Mobile User Behavior Is the Driver for Mobile Success

Does anyone really believe that banner or video ads are the optimal form of mobile advertising?

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