Customer Behavioral Analytics - An Overview

 

Starting from the beginning of civilization, humans have struggled to understand each other. From traders predicting the needs of people in order to cater to them, politicians calculating the move with the best political outcome on the basis of mass’s opinions and generals judging their member’s capability for planning out the position of the army, the need for analyzing behavior becomes crucial.   

 

We've all got our own preferences, our own likes, and dislikes and our own set of reactions to any particular phenomenon but did you know that all our actions get recorded? In a digitally dominated era, we as a user, leave behind an online trail which becomes a weapon for concerned organizations that track them for profitable purposes. This is where the term “Behavioural Analytics” comes into play.

 

"Behavioral analytics and personalized messaging empower brands to turn normal users to highly-engaged power users. Behavioral analytics are key to this because it enables brands to sense the 'digital body language' of users who may drop off, and then intervene with relevant messages."  -Doug Roberge, Product Marketing for Kahuna.

 

In the case of a business environment, marketing analytics is the network that aids in comprehending customer preferences and demand for products and services as well as in planning strategies for sales and communications. For instance, Google is a tech giant that establishes its main business on selected publicity on a search result page. The company’s marketing strategy is based on web users’ preference rankings for web pages. (Read out a blog that emphasizes on the applications and tools addressing Google AI) 

 

In this blog, we’ll delve into an overview of Behavioural Analytics and how it works, bestowing special focus on its role in Marketing. 

 

 

What is Behavioural Analytics?

 

In essence, behavioral analytics is the data that fills you in regarding how your users act on websites or mobile applications. This analytics doesn’t just provide information on the number of monthly active users or pageviews.

 

The behavioral data extracted from the analytics facilitates core answers and provides a visual interface, allowing companies to segment its users and determine their interests and also facilitates how the user’s behavior can be leveraged to increase the engagement, retention, conversion rates as well as the lifetime value of any product and of course, ultimately the organization’s revenue. 

 

The heart of behavioral analytics lies in recording the Events involved. These events constitute the trail left by the user online i.e the actions performed by the user on any particular website or mobile application such as opening the app, creating an account, viewing a video, the duration for which they open a particular page or any other activity associated with the user such as making a purchase. These events are tracked and recorded individually for each user and together they create Behavioural Analytics. 

 

An excellent example of a firm that has made constructive use of Behavioural Analytics would be How Apple uses Big Data. Keeping the consumer at the heart of its operations, the company adopts the extensive data which it gathers at every stage of its consumer’s journey, to pinpoint what actually intrigues their audience into purchasing their product.

 

In case of its product launches, Apple determines its existing users via surveys, through its website and remaining sources. The analysis allows the platform to determine why its users familiarise themselves with Apple’s products. On the basis of the main requirements and the pain points of the customer, the needs and issues surrounding the industry were analysed. 

 

The platform measures how the gap can be bridged between what the customers require and what they presently have.  It determines the design issues which require resolutions to effectively resolve them, all the while maintaining the standard quality of the platform.

 

"Apple’s rich application of insights for planning out its products and services has been indispensable for the success of the tech giant both in revenue and customer satisfaction terms."

 

Yet another firm which has made extensive and admirable employment of Behavioural Analytics to build its success would be Amazon. By examining the behavioral data to determine the items the customer has earlier purchased or viewed, for instance, or which items they might have in their basket or wish list, the company then leverages this data to recommend relevant items to consumers and enhance their experience.

 

The more that Amazon learns about its customers, the more capable the platform gets in predicting what they wish to purchase, which in turn leads the retailer to strategise their approach of tempting the customer to purchase the product. For instance, by suggesting a selected section of the products instead of making the customer swift through the entire catalogue. 

 

As per a study carried out by McKinsey, 35% of Amazon.com’s revenue is generated by its recommendation engine. The platform’s recommendation technology is based on collaborative filtering, which implies that it determines what it perceives that the customer would prefer by constructing an image of the customer by studying their behavior and then suggesting them products which people having similar profiles have brought.

 

You can get further knowledge on this subject by going through our blog on How Amazon uses Big Data. 

 

Yet another tech giant that makes excellent use of Behavioural Analytics would be Netflix. Leveraging behavioral customer data and analytics, Netflix is able to determine the amount of user activity that an individual customer requires each month to gain sufficient value to continue subscribing.

 

Thus by designing a behavioral segment for all customers falling below the minimum product usage value threshold, Netflix is able to easily recognize at-risk customers, discover insights that can lead to low usage, and monitor this over time.

 

 

Steps to carry out an effective customer behavioral analysis

 

The behavioral analysis involves a high degree of planning and strategizing, with its outcome largely relying on how it has been implemented and how constructively the data has been recorded. 

 

Below are some crucial steps for carrying out an effective behavioral analysis :

 

1. Set the KPI

 

In order to monitor whether the user is reaching a set goal in terms of purchase or conversion rates, It's crucial to select the KPI aka Key performance indicator, to illustrate the user’s progress towards the set goal.

 

A book subscription service like The Big Book Box, for instance, can track its paid subscriber growth. An enterprise resource planning (ERP) software like Netsuite ERP that depends on annual contracts, on the other hand, can track users that complete its onboarding sequence.

 

2. Enable a personalized user journey

 

The company has to ensure that it facilitates a desirable journey for both the customer as well as the company itself. Behavioral analytics enables firms to deliver a personalized user journey.

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A journey of a Customer

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This is done by keeping track of every new or potential customer. For instance, if a customer clicks on a brand’s marketing mail, visiting the company’s website yet fails to convert the firm can retarget the user by leveraging banner ads or personalized emails demonstrating any related products or offers. (Related blog: What is Hyper-Personalization? Benefits, Framework, and Examples) 

 

Similarly, in the case of customers who’ve made a purchase or signed up for a newsletter, the behavioral data can be leveraged to intensify the lead and enhance the lifetime value of the consumer through the use of relevant offers and content.

 

3. Develop an accurate tracking plan

 

On the basis of its number of users, the company needs to determine the level of events they’ll be tracking for a particular product or service. The data being tracked needs to be specific and concise with the focus being on quality over quantity. Events can get pretty complicated with their multiple connotations.

 

For instance, the event of a movie opened on Netflix will also lead to further data regarding related movies, movies with the same directors, same artists, etc with the help of a recommendation system. 

 

For keeping these events and properties organized companies tend to develop a tracking plan using a spreadsheet. This will hence serve as a kind of directory for all the events and helps draw an outline for implementing the analytics tool.

 

• The tracking plan is a fluid document that needs to be updated and amended with changes in the product or any alterations in the set goals.
• It’s also preferable to develop the tracking plan by including all involved teams as members of all teams will be required to remain aware of how users and events are organized and situated in order to be able to comprehend the results. 

 

 

4. Assign a unique identifier

 

With digital products these days existing across multiple platforms, a user can appear to be multiple people, hence assigning them with some unique kind of identifier in order to be able to track them becomes a priority. This allows you to match data from multiple devices and sessions to one user. It's also important to ensure that the user ID is set to something that will not change.

 

5. Implement analytics and initiate event tracking

 

Upon the completion of the tracking plan, companies can adopt behavioral data analytics software, using their SDK or API to integrate it with their products. That’s when a unique identifier for users is assigned to users, setting up user and event properties as mapped out in the tracking plan.

 

The fluid quality of the tracking plan also allows for additional events to be added if required. Before making the tracking system go live, it's also imperative for the firm to verify the events and cross-check the user tracking by using test devices. Post this, the firms are free to start analyzing their users.

 

This raises another question of how the results of behavioral analytics can be applied. 

 

The results gained from Behavioural Analytics can be adopted for a variety of purposes. For instance, E-commerce apps can use this data to segment their users based on their behaviors and preferences.

 

Speaking of e-commerce, you can also sneak a peek at our blog on applications of IoT in Ecommerce. 

 

These apps can develop a segment for recent users who added items to their shopping cart without purchasing them, they can also develop a segment for more enthusiastic shoppers who access the app multiple times a day.

 

Various other industry applications for user behavior analytics are :

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Industry Applications for Behavioural Analytics

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• E-commerce sites like Amazon and Booking.com can use this data for forecasting future trends and enhancing conversion rates

• Messaging apps like Whatsapp and Telegram can use it to enhance their usage

• Insurance companies like SBI Life Insurance and PNB MetLife Insurance can use it for selling additional products

• Travel sites like MakeMyTrip can leverage it to escalate their demand and improve booking rates.

• Online gaming platforms like Playstation and Vortex can use it to attract more users

 

 

Conclusion

 

Whether a company is large or small, keeping track of the decision making behavior and buying habits of its customers will ensure that the company stays on track, in terms of generating experience which will boost its customer base and keep them in the game on a long term basis. 

 

As we steer towards a data driven era, platforms will find it difficult to sustain if they fail to keep track of the preferences of their customers. In the absence of behavioral analytics, teams are stuck using insufficiently detailed demographic data and so-called vanity metrics. This analysis will allow companies to stay informed and ensure that their customers encounter a satisfying experience. As the co-author of Streaming, Sharing, Stealing Michael D. Smith explained to The Signal, if a company wants to personalize its service to users, it needs their behavior data.

 

Hopefully, the article would have served you a brief idea regarding how Behavioural Analytics works particularly in the area of marketing.