6 min read

“All the world’s a stage and the men and women are merely players.”

Shakespeare may have considered men and women as mere players, but as large number of users are connected with smart devices and the online world, these men, and womenyour customersbecome your most important assets. Therefore, knowing your customer and envisaging their sentiments using Behavioral Analytics has become paramount.

Behavioral analytics: Tracking user events

Say, you order a pizza through an app on your phone. After customizing and choosing the crust size, type and ingredients, you land in the payment section. Suppose, instead of paying, you abandon the order altogether. Immediately you get an SMS and an email, alerting you that you are just a step away from buying your choice of pizza. So how does this happen? Behavior analytics runs in the background here. By tracking user navigation, it prompts the user to complete an order, or offer a suggestion.

The rise of smart devices has enabled almost everything to transmit data. Most of this data is captured between sessions of user activity and is in the raw form. By user activity we mean social media interactions, amount of time spent on a site, user navigation path, click activity of a user, their responses to change in the market, purchasing history and much more.

Some form of understanding is therefore required to make sense of this raw and scrambled data and generate definite patterns. Here’s where behavior analytics steps in. It goes through a user’s entire e-commerce journey and focuses on understanding the what and how of their activities. Based on this, it predicts their future moves. This, in turn, helps to generate opportunities for businesses to become more customer-centric.

Why Behavioral analytics over traditional analytics

The previous analytical tools lacked a single architecture and simple workflow. Although they assisted with tracking clicks and page loads, they required a separate data warehouse and visualization tools. Thus, creating an unstructured workflow.

Behavioral Analytics go a step beyond standard analytics by combining rule-based models with deep machine learning. Where the former tells what the users do, the latter reveals the how and why of their actions. Thus, they keep track of where customers click, which pages are viewed, how many continue down the process, who eliminates a website at what step, among other things. Unlike traditional analytics, behavioral analytics is an aggregator of data from diverse sources (websites, mobile apps, CRM, email marketing campaigns etc.) collected across various sessions. Cloud-based behavioral analytic platforms can intelligently integrate and unify all sources of digital communication into a complete picture. Thus, offering a seamless and structured view of the entire customer journey. Such behavioral analytic platforms typically capture real-time data which is in raw format. They then automatically filter and aggregate this data into a structured dataset. It also provides visualization tools to see and observe this data, all the while predicting trends. The aggregation of data is done in such a way that it allows querying this data in an unlimited number of ways for the business to utilize. So, they are helpful in analyzing retention and churn trends, trace abnormalities, perform multidimensional funnel analysis and much more.

Let’s look at some specific use cases across industries where behavioral analytics is highly used.

Analysing customer behavior in E-commerce

E-commerce platforms are on the top of the ladder in the list of sectors, which can largely benefit by mapping their digital customer journey. Analytic strategies can track if a customer spends more time on a product page X over product page Y by displaying views and data pointers of customer activity in a structured format. This enables industries to resolve issues, which may hinder a page’s popularity, including slow loading pages, expensive products etc.

By tracking user session, right from when they entered a platform to the point a sale is made, behavior analytics predicts future customer behavior and business trends. Some of the parameters considered include number of customers viewing reviews and ratings before adding an item to their cart, what similar products the customer sees, how often the items in the cart are deleted or added etc. Behavioral analytics can also identify top-performing products and help in building powerful recommendation engines. By analyzing changes in customer behavior over different demographical conditions or on the basis of regional differences.This helps achieve customer-to-customer personalization.

KISSmetrics is a powerful analytics tool that provides detailed customer behavior information report for businesses to slice through and find meaningful insights. RetentionGrid provides color-coded visualizations and also provides multiple strategies tailormade for customers, based on customer segmentation and demographics.  

How can online gaming benefit from behavioral analysis

Online gaming is a surging community with millions of daily active users. Marketers are always looking for ways to acquire customers and retain users. Monetization is another important focal point. This means not only getting more users to play but also to pay.

Behavioral analytics keeps track of a user’s gaming session such as skill levels, amount of time spent at different stages, favorite features and activities within game-play, and drop-off points from the game. At an overall level, it tracks the active users, game logs, demographic data and social interaction between players over various community channels. On the basis of this data, a visualization graph is generated which can be used to drive market strategies such as identifying features that work, how to add additional players, or how to keep existing players engaged. Thus helping increase player retention and assisting game developers and marketers implement new versions based on player’s reaction. behavior analytics can also identify common characteristics of users. It helps in understanding what gets a user to play longer and in identifying the group of users most likely to pay based on common characteristics. All these help gaming companies implement right advertising and placement of content to the users.

Mr Green’s casino launched a Green Gaming tool to predict a person’s playing behavior and on the basis of a gamer’s risk-taking behavior, they help generate personalized insights regarding their gaming. Nektan PLC has partnered with ‘machine learning’ customer insights firm Newlette. Newlette models analyze player behavior based on individual playing styles. They help in increasing player engagement and reduce bonus costs by providing the players with optimum offers and bonuses.

The applications of behavioral analytics are not just limited to e-commerce or gaming alone. The security and surveillance domain uses behavioral analytics for conducting risk assessment of organizational resources and alerting against individual entities that are a potential threat. They do so by sifting through large amounts of company data and identifying patterns that portray irregularity or change. End-to-end monitoring of customer also helps app developers track customer adoption to new-feature development. It could also provide reports on the exact point where customers drop off and help in avoiding expensive technical issues.

All these benefits highlight how customer tracking and knowing user behavior is an essential tool to drive a business forward. As Leo Burnett, the founder of a prominent advertising agency says

“What helps people, helps business.”

Content Marketing Editor at Packt Hub. I blog about new and upcoming tech trends ranging from Data science, Web development, Programming, Cloud & Networking, IoT, Security and Game development.

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