Defining Behavioral Analytics 2.0

Here are the basics: typical analytics focuses on the who, what and where. Behavioral analytics is about the why and how. For example, typical analytics solutions will give one a general picture of who is on their site or app, where they come from, and what pages they are looking at, while a complete behavioral analytics solution is able to also answer why and how certain users behave the way they do. This post provides an overview of behavioral analytics tools and analyses.

KPIs, or Key Performance Indicators, are used to measure trends and determine their progress and success. KPIs can be financial, or related to retention, customer satisfaction, market share, lead generation, etc. Companies use KPI’s to track and figure out how many users perform a certain action, or complete a given task. Analyzing data and relating it directly to a company’s KPIs, especially with a visual widget or interface, allows them to better understand their users and, in-turn, meet their KPIs.

Path analysis gives a company the ability to optimize and improve a process by illustrating where users go, or drop off, between events on the company’s platform or site. An event can be just about any action, like logging in, adding an item to the cart, completing a purchase, or playing a certain number of levels in a game. Any number of events can be tracked in a single path that illustrates the progression of users and breaks it down as time goes by.

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Cohort analysis is used to break users into specific groups or cohorts in order to see how they behave. A cohort can be made up of users segmented geographically, or by gender and age, but also according to behavioral attributes, like people who have played a certain number of game levels, performed some other action, or a group that generates the same two consecutive events during a set period of time. Placing users into cohorts like these makes the data drawn about them more relevant, as it pertains only to the examined cohort. This analysis is often used for retention because it measures trends that will enable your company to get on the right track for conversion rates, churn and user experience. img

Funnel analysis helps identify the problematic steps in a platform’s flow – whether in a game, a mobile app or an eCommerce website – to find where users are churning and see the effects of ongoing improvements.

Predictive analytics anticipates what users are going to do in the future, moving beyond customer behavior and actions taking place right now, or in the past. Predictive analytics is a broad term which covers a variety of statistical and analytical models. These predictive models generate a score for relevant variables: the higher the score, the higher the probability a given behavior will happen. These scores are then used to build predictive models that answer questions such as: which variables have the greatest effect? What’s going to happen to a certain user who logged into a game and then left? Predictive analytics enables one to focus on the future instead of the past by utilizing historical data, combined with analytical insights.

If you are interested in learning more about predictive analytics download CoolaData’s dedicated white paper. Also make sure to check out our dedicated blog posts on the different types of analytics discussed above, like cohort analysis, funnel analysis, path analysis and predictive analytics.

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