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Behavioral Analytics

Did you know that from the moment you click on websites or apps, your every activity is monitored?

The number of clicks you made, the amount of time you spent on each item, the type of items you spend your time on, etc. is stored. This massive volume of data is captured and analyzed by companies. You must have noticed, when you spend time on apps like Instagram, if you started to look at a lot of cute dog pictures on Instagram, your feed will automatically start having more and more dog content. This is because Instagram has been capturing your behavior and analyzing it to provide more personalized content for you. This process of analyzing user behavior is called behavioral analytics. Incorporating behavioral analytics into your operations can be a little intimidating, both in terms of implementation and expense. However, according to a report by McKinsey, organizations that use customer data to produce behavioral insights outperform their peers by 85 percent in sales growth and more than 25 percent in gross margin.

We usually see behavioral analytics being used by companies regularly (but often don’t realize it):

  1. Netflix: Netflix provides its recommendations through a complex algorithm that takes into consideration the preferences of the customer who is watching and also the shows which were watched by people with similar preferences:

Analyzing user behavior like this has helped Netflix increase the lifetime of their customers and helped in making their content more personalized for them. Netflix executives estimated that this analysis saves the company $1 Billion a year.

  1. Amazon: Amazon gives product recommendations through a complex machine learning algorithm that combines behavioral data such as: ● A user’s purchase history ● Items in their cart ● Items they’ve liked and rated ● What other customers have viewed and purchased This algorithm is estimated to be responsible for around 35% of Amazon’s total revenue.

What are Behavioral Analysis Tools?

There are three main behavioral analysis tools involved in building a picture of your customer journey: segmentation analysis, funnel analysis, and cohort analysis.

SEGMENTATION ANALYSIS

The study of customers divided into smaller groups to understand specific characteristics such as their behavior, age, income, and personality is known as segmentation analysis. When a company is marketing a smaller segment of consumers, it is easier for them to advertise since each advertisement can be highly tailored and precise to the features of each group.

FUNNEL ANALYSIS

Funnel analysis is a method of evaluating the steps taken to achieve a certain outcome on a website, as well as the number of users who complete each step. Funnel analysis helps you spot where users are leaving your website, so you can optimize the problem area and increase conversion rates. To analyze a funnel, you have to find: ● User Conversion rates ● User Drop-off rates

COHORT ANALYSIS

Cohort analysis breaks the data into a data set into related groups before analysis. These groups, or cohorts, usually share some common characteristics. Cohort analysis allows a company to find patterns throughout the life-cycle of a customer. By seeing these patterns, a company can adapt its service to those specific cohorts.

The 2 most common cohort types are:

● Acquisition Cohorts: divides users by when they signed up first for your product. For app users, you might break down cohorts by the day, the week, or the month they launched an app, and track daily weekly, or monthly cohorts.

● Behavioral Cohorts: divides users by their behavior in your app within a given period. These could be any number of actions that a user can perform – App Install, App Uninstall, Transaction, or any combination of these events.

Benefits of Behavioral Analytics

Behavioral analytics is critical for increasing conversion, commitment, and retention at a company. Every member of a team should be able to obtain the actionable knowledge they need to answer their questions and exploit data in ways that didn’t seem possible before with the right behavioral analytics tool.

According to the results, a large percentage of users use a particular e-commerce platform after searching “Thai food” on Google. Most of the users spent time on the homepage and went to the “Asian Food” tab and end up buying nothing. Examining each of these incidents as a single data point fails to shows what is going in consumers mind and not able to analyze why consumer isn’t buying the product

Both web traffic and page views are viewed as a timeline of related events that did not result in orders in behavioral analytics. Since the majority of users left after seeing the “Asian Food” page, there might be a discrepancy between what they’re looking for on Google and what the “Asian Food” page reveals. Knowing this, a glance at the “Asian Food” page shows that Thai food is not prominently displayed, leading people to assume it is not available, even though it is.

Let’s look at a few examples of how behavioral analytics may be used: A travel company decides to use its website to monitor consumer events to improve its marketing efforts. A consumer might have looked at visiting a particular destination with a specific airline but abandoned the process before making an order. The business sends an email to the prospect in response to this material. To inspire a booking, the email may contain a travel discount or bid, as well as any valuable information on the destination of interest.

Another scenario may be that a car dealership sends out an email campaign with a PDF attachment providing information about a variety of vehicles. They can detect user events using behavioral analytics. They will see who opened the attachment, how long they left the PDF open, how much they got into it, and where they spent the most time. The data is then sent to the sales team, which is now in a stronger position to initiate talks with prospects. Prospects become more involved in discussions, and sales representatives may provide more accurate information and tailored deals.

Here are a couple of the benefits of behavioral data analytics: ● Customized advertising and marketing campaigns ● Heightened customer interaction and subsequent fulfillment ● Better customer relations, and ● Eventually more sales

To conclude, a company should use behavioral analytics to help understand its target audience’s expectations and preferences. In today’s world, not understanding it means resorting to a scattershot campaign, which does not work.

Criticism of Behavioral Analytics

Behavioral analytics raises substantial privacy questions because it necessitates the processing and aggregation of vast volumes of personal data, including extremely confidential data (such as sexual identity or sexual interests, health conditions, and location), which is then exchanged between hundreds of parties interested in targeted ads. Starting in 2015, Amazon joined Google and other tech giants in launching in-home voice devices that are expected to become a gold mine of behavioral insights for off-line life, just as the activities on their pages are a repository of data for your online life. Some people think this is invasive and unnecessarily informative to data providers and the government, but when they buy something, they are de facto subscribing to the terms.

Various Products and Websites

Amplitude, Indicative, and Mixpanel are common behavioral analytics providers, and each has its own set of advantages and disadvantages.

If a company is looking for the right behavioral analytics supplier, it’s critical to take your time to thoroughly study each tool. Some companies have a free trial period, which will help you get a clearer understanding of how the tools function and whether the product is right for your market and research needs. It’s important to check out behavioral intelligence resources that can help you to

● Optimize behavior through multiple paths and isolate the efficient ones ● Diagnose and remove unwanted steps for customers ● Focus on key behaviors that result in higher total customer value ● Use targeted customer segments (cohorts) to inform and launch campaigns ● Isolate and aim users at risk of churn ahead of time ● Develop at-a-glance dashboards that can be shared with teams and executives

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