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Customer Behavioral Analytics for Digital Marketing

Customer Behavioral Analytics for Digital Marketing

To make a business grow and prosper we need to know our market, needs, spending habits and budget of our buyers. To know and understand our market and buyers we require data related to them. With the advancement of the technology and heavy usage of various digital devices have made its impact on marketing and almost replaced traditional marketing in some domains. Digital marketing is more effective than traditional marketing and also has many advantages. In this data-driven world, we have various means to get the data and once we have the related data, we can analyze it to draw insight about the market and the potential buyers. These insights give us the power to take corrective measures and make our business successful.
Do customers behave always the same way? No, customer behavior and expectations change with time, which makes the task more difficult for the business. A business can utilize various sources and Application Programming Interface (APIs) to collect customer-related data, for example, weblogs, clicks, mouse hovers, page scrolling, email opens/clicks, customer call details, and approved/declined credit card transactions, etc. Acquisition, cleaning, and analysis of this customer data is the right point to start. Here, in the following sections, we cover different elements of customer behavior analytics and its applications.
Elements of Customer Behavior Analytics
In the analysis of the data from the customers, we can focus on the following different elements (also see Figure 1).

Figure 1. Elements of Customer Behavior Analytics.
Acquisition Channel: Customer acquisition is a crucial part of any business and choice of the right acquisition channel plays an important role in this. Some examples of customer acquisition channels are social media, email, search, online video, mobile video, online display, mobile display, over-the-top (OTT) TV, and connected TV (CTV), etc.
Behavior flow: It is a route that a user follows on the web page from the time of start to the time of the end in his/her visit. Behavior flow is difficult to capture, but there are some tools such as ‘Behavioral Flow Report’ to analyze the behavior of the visitors when they visit the website and adjust the content as per the requirement of the customers.
Browser and Extension: There are a number of browsers that we can choose from (see Figure 2). Important points to consider while choosing a suitable browser are support for plugins and the processing power it uses. To know its presence and effect of its marketing strategy, a business can use various browser extensions, e.g. Ghostery, Page Ruler, ColorZilla, etc.

Figure 2. Browsers and Extensions.[ https://lifehacker.com/what-browser-extensions-cant-you-live-without-1700748660]
Demographic is one of the important things that we can use in behavioral analytics (see Figure 3). Commonly used demographics are gender, age, location, languages knew, annual income, parental status, etc. Demographic helps in understanding website visitors and making important decisions related to estimating the size of the target marketing audience, and means of communication for more effective marketing.

Figure 3. Demographics for Behavioral Analytics.[ https://digitalmarketinginstitute.com/blog/social-media-demographics-guide-for-digital-marketers]
Applications of Behavioral Analytics for Digital Marketing:
Behavioral analytics is a sub-domain of data analytics, which can provide insights into the actions and behavior of potential customers. Along with various different applications of behavioral analytics, it can be very useful to achieve a specific digital marketing objective (see Figure 4). Following are the different applications of behavioral analytics.
  • E-commerce and retail: used for product recommendations and future sales.
  • Online gaming: used to predict trends in usage and preferences for future offerings (game development).
  • Application development: knowing about the usage of an app by the customers to forecast future trends.
  • Security: help to detect unusual activity and identify security issues.
  • Cohort Analysis: Uses data from a particular usage (a game or e-commerce platform) as one set and divide it into different groups. This grouping is considered a cohort with shared properties of time and size.

Figure 4. Behavior Analytics for Digital Marketing.
End Notes:
In this article, we have looked at the basics of behavioral analytics, different elements of customer behavior analytics, and applications of behavioral analytics for digital marketing. For a good implementation of the behavior analytics, we should have a good knowledge of the customers, and follow different flexible strategies. Additionally, by knowing the customer behavior about their usage of the smart mobile phones, social media, a business can design an Artificially Intelligent powered marketing strategy and use chatbots, and robots to interact with the customers and analyze their behavior. Today’s digital age is evolving very fast with technology and offering a large amount of data. Good research, knowledge of the latest trends and the right data are the powerful tools to help us to identify the right and best practices.
Written by: Dr. Parmod Kumar, Senior Associate Faculty at the Regenesys Institute of Management. You can reach me at parmodk@regenesys.in.