by: Soumya Subramanian
In this age of increased customer touch-points, predictive mobile analytics has become a key component in helping companies target their marketing initiatives.
With the dramatic growth of mobile usage worldwide, smartphones have become one of the most effective conduits for understanding consumers and uncovering usage patterns. With customer queries, feedback, purchase intentions, trends and sentiments churning out tremendous volumes of transactional data, companies have the potential to target their marketing and advertising initiatives.
Predictive mobile analytics enables organizations to predict consumer behavior by analyzing mobile and app usage data. Behavioral targeting is a widely used technique that’s deployed by advertisers to predict potential purchases based on a consumer’s online activity. Advertising networks observe the apps installed, Websites visited, duration of the visit, time spent on sites, pages viewed and pages followed in order to identify potential customers.
Despite all the browsing information gathered, the user remains anonymous. Personally identifiable information (PII)—such as name, address, email and phone number—is not collected, in order to avoid violating consumers’ privacy.
During the past few years, applying predictive mobile analytics has resulted in improvements in tagging, tracking and aggregating consumer behavior. While the vast majority of the data is collected anonymously, digital tags enable the creation of a digital dossier in which browsing history can be tagged to a particular unidentified individual.
This data holds great promise for companies, since customer classification is the starting point in strategic decision making and in designing products and services. Behavioral metrics, which help in targeted marketing, can be derived from browsing data, app usage and Internet buying behavior. Statisticians can mine the data collected to find patterns in subscriber usage.
Predictive mobile analytics and behavior-targeting methods have evolved significantly in recent years. These methods, coupled with advances in natural-language processing and machine learning, enable companies to classify and predict shopping patterns and consumer preferences with greater accuracy.
Benefits of Predictive Mobile Analytics
Some of the main benefits of predictive mobile analytics include the following:
· Real-time engagements and promotions based on preferences and past buying behavior. This information gives retailers leverage in targeting promotions and advertising.
· Ability to identify what customers are saying about a company and its products by understanding the textual inputs at various touch-points by applying natural-language processing techniques, which help derive insights from human or natural- language input.
· Ability to identify price and product ranges for the products being searched, by determining what is most likely to be purchased next, based on browsing behavior and application usage on the mobile device.
· Mapping of potential customers based on the similarity of services used in the past, and how likely they are to buy a company’s products.
Privacy issues continue to be a major obstacle to the full-scale utilization of mobile and app analytics. However, with improvements in predictive analytics techniques and upcoming analytics techniques such as machine learning, it is possible to make accurate and individualized predictions.
Predictive mobile analytics can help marketers generate behavioral data to improve their marketing efficiencies, purchase rates and consumer responsiveness to advertising.
About the Author
Soumya Subramanian is the head of solution analytics at Blueocean Market Intelligence, a global analytics and insights company that helps corporations attain a 360-degree view of their customers through data integration and a multidisciplinary approach.
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