A Beginner’s Guide to Funnel Analytics

Massive reservoirs of data and the information that they hold calls for extensive analysis and application of science, art and business rules. Problem solving using data and applied technology and maths principles is now a rapidly growing industry. Hence, there are new algorithms and methods are being devised almost everyday. Funnel Analytics is a web-analytics method devised to aide the process of marking target users and taking a series of actions to reach the desired goal.

What is Funnel Analyses?

As the name suggests, Funnel analysis measures and visualizes the progress of a user on a web-platform through a series of steps. Let us take, for instance, the series of actions performed by a user on an e-commerce website:

1.) Visit the e-commerce website

2.) Browse through the product list and select a product

3.) Add the product to the cart

4.) Pay and complete the transaction.

There might be additional steps between these actions, but to design the funnel workflow in this scenario, the 4 steps mentioned above will form the important layers. Each layer determines the purpose of the user and helps us form a target-plan for him/ her. For example, a user could be browsing through the products and add them in the wishlist, but just to make a personal catalog of them or maybe to compare the price of the item on different sites.

Why do we require Funnel Analytics?

Funnel Analytics

On careful analysis and study of these trends could help the website owners target their users better and improve the performance of the site. Funnel analyses thus helps us define a systematic workflow and thus define a local problem statement to work upon. A funnel thus, takes a large sample space of users and drops them off as the criteria build on, thus narrowing it down to a specific group of target users with common traits.

The importance of funnel analytics lies in the element of actionability that it provides. It helps one pinpoint the exact step where on a user journey the platform is not performing as desired. Depending on the dropoff rate at each step, one can answer the ‘what’ and ‘how’ of the site-performance. To answer the ‘why’, one can easily dive deeper into the pain-point, so highlighted.

This leads to a simpler approach at testing different iterations of the platform. Hence, one can now compare the dropoff rates of different versions, compute any uplift and eventually, quantify the associated improvement.

Application of Funnel Analytics

The predominant use of Funnel analyses can be seen in the e-commerce industry to target customers and devise new plans for improved performance. They use it to see why users are not converting despite spending considerable amount of time on the website. They find application in the ‘ordered’ funnel (wherein a user can perform actions only in a definite order).

However, it is also used by media companies to increase the number of people who go from viewing an advertisement or reading an article to subscribing. Now, since users can subscribe to a site before reading an article, these firms can invest on ‘unordered’ funnel analysis. Mobile apps designers use it to boost the number of people who download the app and then become regular users. To add to the list, online game designers use it to convert casual gamers to more engaged ones.

The industries need more competitive Funnel Analytical tools

There might be a few limitations to the method as it is not always easy to identify the initial sample-set of users who should be included in the funnel analysis. It is also, not effective for analyzing platform performance where there are multiple mutually exclusive workflows. However, the benefits of the same outweigh the flaws in the ways of a heuristic approach. Hence, more firms are adopting this method by the hour.

Funnel Analytics

Weblytics offers a competitive end-to-end reporting platform that will help firms engage better with their audience. With features like, real-time reporting, user-profiling and data aggregation, integration is one primary feature of this tool. Creating user segments on the basis of behavioral and content-driven insights will help one design better campaigns and eventually improve the performance of the website.

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