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Is Data the Holy Grail for Mobile Advertising?

Written on
Sep 1, 2014 
Author
Ryan Kirk  |

Every second of the day mobile devices create copious amounts of actionable data for marketers. These data include call detail records, Short Message Service (SMS) data, and geo-location data. The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

When analyzed effectively, this data can provide business insight on user sentiment, behavior and even physical movement patterns. Due to the sheer number of mobile devices in use, Big Data practitioners can tap mobile Big Data analytics to better understand trends across vast populations and sub-segments of users. This understanding helps business to improve engagement tactics and to optimize the delivery of services.

Mobile device data becomes particularly useful for analytics purposes when combined with extended data from outside sources. For example, weather and economic allow practitioners to correlate macro-level trends to a targeted set of users. These consumer segments have traditionally grouped users together based upon similarities. However, industry is increasingly focusing upon targeting towards individuals based upon their interests or upon their past behaviors.

Below you will find a number of ways you can apply real-time data analytics to your mobile marketing and advertising campaigns.

  • Real-time data analytics across the complete mobile lifecycle: In the past, conventional database solutions could be relied upon to effectively manage and analyze massively large data sets. But they did so at a snail-like pace, taking days and even weeks to perform tasks that often yielded “stale” results. By contrast, the big data analytics platforms of today can perform sophisticated processes at lightening-fast speeds, allowing for real-time analysis and insights. Shorter time to insight allows marketers to make real-time decisions and take immediate action based on fresh, reliable and relevant information.
  • More personalized and targeted ads: Big data allows brands to better target users with more personalized interactions that drive engagement. We increasingly see ads featuring products and services we might actually want to use to make our lives better. These more personalized, more targeted ads are all based on massive amounts of personal data we constantly provide. Everywhere we go nowadays we leave data crumbs. Following these trails reveals information about what we were doing, saying, liking, or sharing. Thanks to our mobile devices, this data trail now also hints at where we’re going.
  • Hyper-localized advertising: The proliferation of mobile devices, primarily smartphones, has created a major opportunity for marketers to deliver contextual advertisements. These mobile-specific ads target the right people at the right time. For instance, through the combination of social data and location data, stores that shoppers are near and might be interested in can send out ads offering percentage discounts or other incentives. Delivered by shops to their shoppers in real time, these ads get consumers to walk through their doors. Hyper-localized advertising has been shown to increase customer engagement and conversion rates.
  • Leveraging Attribution to Achieve Mobile Engagement: Leveraging Big Data about user behavior helps brands more accurately and completely quantify the effectiveness of their mobile marketing initiatives. Big data helps marketers determine whether their campaigns are creating the desired results. The ways users respond to branding assets can be used to literally create “rules of engagement” for each user or for each type of user. Marketers optimize their results through understanding varying levels of consumer engagement and through understanding the contributions of different campaigns across the path-to-purchase.
  • Flip traditional consumer profiling upside-down: In the context of ubiquitous, real-time consumer data brands can now ask the data who their customers are. Contrast this to the erudite consumer profiling where consumers are targeted towards based upon their goodness of fit into an expected consumer picture. Rather than relying upon arcane consumer characteristics, instead we can now quantitatively choose targeting characteristics based upon the congruence of these characteristics with the desired call-to-action.

All in one mobile engagement platform

Hipcricket provides an end-to-end marketing and advertising platform to execute mobile campaigns that build brands and drive customer acquisition and retention. Brands can make data-driven decisions to optimize their mobile marketing efforts by providing analytic insights as part of their overall marketing strategy.





Author Photo

Ryan Kirk is a Senior Data Scientist at Hipcricket where he drives the company’s thought-leadership and corporate strategy for Hipcricket’s big data solutions utilizing advanced research methods, statistical analysis, and machine learning skills. Prior to Hipcricket, Ryan worked as a business intelligence analyst at Amazon where he identified business insights using data gathering, synthesis and modeling and problem solving. Through his work at Amazon he was able to reduce automated fraud by 90 percent. In addition, Ryan has spent time as a machine learning scientist and a consultant developing a fraud prevention system. Ryan is a PH.D candidate in Human-Computer Interaction at Iowa State University and holds a BS in Psychology, Marketing and Business Administration from Drake University.

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