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 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.
In this blog we’ll delve into an overview of Behavioural Analytics and how it works, bestowing special focus on its role in Marketing.
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 Apple. 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. 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. As per a study carried out by McKinsey, 35% of Amazon.com’s revenue is generated by its recommendation engine.
(Speaking of Recommendation engine, you can also spare a glance at our blog on 6 Dynamic Challenges in Formulating the Imperative Recommendation System)
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 usage 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.
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 :
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.
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.
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.
Similarly in 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.
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.
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.
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.
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.
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. They 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 :
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.
In the absence of behavioral analytics, teams are stuck using insufficiently detailed demographic data and so-called vanity metrics. 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. For more blogs on analytics, do read Analytics Steps! Share this blog on Facebook, Twitter, and LinkedIn.
Mallika is an eager and enthusiastic intern at Analytics Steps. Mallika believes that words hold the power to clarify and illuminate technicalities of various subjects and help readers in gaining understanding and knowledge. Her love for exploring and absorbing new technologies helps her keep pace with the ever changing digital world.
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