Collecting behavioral user data, such as affinities, interests, demographics, past purchase data and any other available user-centric information may have a significant, positive impact on user experience and business growth. It is a dynamically evolving area, which involves complex machine-learning algorithms and data mining capabilities.
Utilization of behavioral data for customized ads and/or content requires deep data mining abilities.
Data mining is a term used to explain the process of analyzing big data for the purpose of finding interesting patterns, correlations and insights. It is an analogy of the method of extracting rock-solid data from a massive mountain. In the online world, data mining is used to find behavioral patterns of users by analyzing their past online behavior.
While one might think of it as an out-of-reach technology, the fact of the matter is that behavioral analysis and targeting tools have become much more accessible today and most valuable – now more than ever. In fact, behavioral targeting is all around us. It is commonly used by online advertisers to deliver customized ads and increase the relevancy and effectiveness of their off-site campaigns. One classic example is the retargeting of ads to segments of visitors who had previously visited the website based on specific behavioral criteria. Another great example would be Facebook’s Lookalike Audiences capabilities. The Lookalike Audiences feature allows advertisers to automatically target people on Facebook who are very similar to the initial seed of audience, which was selected by the advertiser as a highly relevant target audience to its business.
Behavioral analysis and predictive abilities are gaining in popularity.
Many websites today understand that in order to increase user experience, engagement and conversion rate, they need to engage users in more personalized ways. Up to 94% of in-house marketers agree that website personalization is critical to current and future success (source). Predicting future behavioral patterns of the target audience may have an impressive impact on the bottom line, and that’s exactly were website personalization engines come in handy.
Concluding valuable insights from a thorough, automated data mining process and, then, dynamically packaging it for personalized recommendation modules is one of the most typical and effective ways of leveraging behavioral data. As an example, e-commerce sites have been using this technique for offering personalized product recommendations to their visitors.
With the explosion of big data, before making any big decisions, marketers should know how to effectively gain valuable insight and avoid any bias.
One of the key findings according to “The State of Always-On Marketing Study” conducted by Razorfish in collaboration with Adobe is that 76% of marketers are failing to use behavioral data in segmentation analysis and targeting execution. In addition, very few are capable of delivering real-time analytics and experiences in terms of technology, creative execution and integration of data. Choosing the right behavioral targeting tools, as well as a mature marketing strategic plan, is key to achieving success.
About the Author
Yaniv Navot is the Marketing and Growth Manager at Dynamic Yield, a SaaS-based solution for real-time personalization and automated conversion optimization. He’s a conversion optimization enthusiast and a web analytics expert.