How to Use Behavioral Analytics to Improve Customer Engagement

The biggest challenge for marketers is to achieve sustainable business growth despite high customer churn rates.

While businesses are always interested in increasing the number of potential customers and converting them into paying customers for business growth, they have to strengthen their relationships with present customers to stay ahead of the competition.  

The main objective of any business professional is to acquire and retain customers. For this, he has to keep an eye on customer churn because as more customers churn out of the marketing funnel, the slower the company’s growth will be.

In other words, a company that cannot retain its’ customers cannot hope to expand. Hence, business professionals need to understand and measure their churn rates.

Generally, Companies use traditional data analytics tools to measure profit and loss.

These tools provide insights on the pain points of customers, such as problems using your product or the lack of customer engagement, but they cannot identify the signs that a customer is likely to leave your funnel until they have already churned. That is where Behavioral analytics can step in for predictive retention.

What do you mean by Behavioral analytics?

Behavioral analytics or behavioral data analytics is the method of measuring and analyzing consumer behavior to enhance your products and processes.

Moreover, it is predominantly used in product management to make sure that a company’s offerings meet the expectations of users.

The goal behind using analytics is to measure the user experience so that you can frequently make changes to the product to satisfy users. 

Why is behavioral analytics important for UX?

SaaS-based companies use behavioral analytics tools to focus on the latest trends and find out what features customers are interested in so that they can work around them. 

It also involves the use of cookies to track customers’ journeys as they browse multiple websites.

Behavior analytics in the retail industry has infinite possibilities. It involves the identification of potential challenges experienced by the users and the removal of guesswork, which results in better business decision-making.  

The goal behind this is to create a good user experience and build a strong base of satisfied customers.

Understanding customer behavior analytics is vital for capturing the sentiment of UX and helping to find issues with the user journey to achieve higher conversion rates and drive more sales. 

Let’s discuss how to further use behavioral analytics to improve the customer experience:

Determine clear goals

It is vital to set a goal if you intend to fulfill it. Later, you can prepare a strategy that will yield the desired results. 

To improve your UX, you need to have the following questions in mind:

1. Why are our landing pages not generating more conversions?
2. What do customers say about our product or brand?
3. What types of challenges do our users face when they start using our product?
4. Why is there a high churn rate for our product? How can I reduce it?

Once you’ve established your end goals, you can devise a powerful strategy to achieve them.

Monitor various steps of the user journey

The customer journey is not a single-step process, so you shouldn’t miss out on all steps of the whole process. This way, you can take the user journey to the next level. 

Use different plans and approaches

Use different tactics, such as A/B testing, heatmaps, session recordings, and customer feedback, to understand user behavior from various angles. 

This will help you get valuable insights, and with such info, you can create better products for users. 

Track the effectiveness of your actions

After making changes, and improvements, go to the Analytics tool to measure its effectiveness.

If you find the user experience is improving as expected, then you can look at other problematic areas and improve them. 

Repeat the entire process

Improving the user experience is not a one-time job; it’s a continuous process that involves finding various methods to build, and nurture a group of loyal customers through your product.

So, aim for creating a good UX for customers who want to use the product.

Why should companies use behavioral analytics?

Companies use customer behavioral analytics to understand what customers want from their products, and how they interact with them.

They use behavioral analytics to monitor different actions of users, including:

1. websites they visit
2. time spent on websites
3. clicks on a particular product or website

Simultaneously, it helps in monitoring the actions performed by the user with your product, such as how often a user purchases a product, or how often a user shares content on social media.

With the help of behavioral data, companies can develop marketing strategies and plans for better results. They can then use this information to dig deeper into customer buying patterns, interests, and purchases to understand customers’ needs. 

Behavior data can help in creating the following business goals for B2B, B2C, or SaaS companies:

1. new customer acquisition
2. user retention
3. minimizing churns

Behavior data highlights when the customer drops off and provides valuable insights to retain them, and achieve expected results with the product. 

The customer behavioral analytics tools offered by Express Analytics are compatible with many systems, provide easy results, and allow marketers to integrate internal services like CRM.

Our tool does in-depth analysis of customer data and creates automated pictorial representations to detect outliers and trends.

The next step will be measuring the predictions and making recommendations for enhancing the user experience.

Different Stages of Behavioral Analytics

The behavioral analytics process has three important stages. Let’s discuss them in brief:

Data collection: You can find plenty of data from different sources, but it’s impossible to list them all.

Whenever any client browses the internet, the data will be saved in the form of cookie files and may be analyzed after recording.  

Segmentation: This step involves grouping the data into smaller chunks, which are then placed into a few categories associated with already-defined criteria.

Segmentation may have multiple outputs as one set of data can be processed in multiple ways. 

Implementation: This step involves adjusting the content of the advertisement as per the consumer’s needs, interests, and views using data.

The goal behind this is to target the client individually and send the right information to him specifically. 

What are examples of behavioral data?

Different examples of behavioral data involve newsletter sign-ups, social media likes or comments, new account creation on your website, filling out a contact form, app downloads, adding an item to a cart, as well as website views.  

Behavioral data provides the best opportunity to understand your clients and boost your business.

Combining data from websites, social media, CRM systems, devices, and apps with enterprise data can reshape your marketing plans and also provide personalized suggestions.

This will help you use this information to customize users’ and their experiences.  

Behavioral data not only involves positive actions about a brand, but it also includes negative actions like order cancellations, email unsubscribes, removing items from the cart, and negative comments on social media.

It goes behind the demographics of customers, such as location, age, job title, and gender, to find out their needs.

What are the different types of behavioral data?  

Listed below are a few types of behavioral data: 

First-party data: Collected through various sources, including your apps, website, social media, etc.

The problem with this is, if the customer leaves the platform, it’s impossible to track their data. 

Second-party data: collected by another organization i.e., trusted partners who share their audience insights with you. 

Third-party data: Data obtained from a third-party source that sells customer information.

What are user behavior analytics tools?

User behavior analytics tools are intended for tracking, and recording the behavior of users on apps, websites, or any digital platforms to help user segmentation with data such as cursor tracking, app analytics data, search data, website analytics, user feedback, scrolling, user navigation, clicks, and user experience.  

 The following are the best customer behavior analytics tools:

1. A/B testing tools
2. Voice of the Customer (VoC) and Feedback Tools
3. Heatmap and session replay tools

How do you choose the right user behavioral analytics vendors?

Behavioral analytics solutions are compatible with different systems and provide rapid outcomes.

They allow the integration of products from desktop and mobile apps to internal services, and help teams rapidly find answers through friendly interfaces. 

Swapping out customer behavior analytics platforms can be time-consuming, so teams should do deep research and invest in a platform that adds value to them. 

 Always choose the behavioral analytics tools, which can have special features:

1. Automatically capture customer and event data points.
2. Access real-time data and query it in multiple ways.
3. Pre-defined reports including funnels, retention, and cohorts

How do I track user activity on a website?

The website provides multiple options to analyze the activities of users, such as how they navigate, where they click and scroll, and when they leave.

Monitor how different design elements look and cross-verify to ensure that you’re providing a seamless experience across different devices. 

Don’t forget to compare the differences between the behavior of returning and first-time visitors.

This observation is important for evaluating visitor engagement, and retention rates.

For SaaS as well as eCommerce websites, conversion rate optimization plays a key role.

How to track user activity on an app?

You have to monitor how users interact with mobile apps and check for misclicks. You can fix this by adjusting the size, and placement of CTA buttons. 

Here, it is possible to track which features users like the most and decide which features should be removed, but keep in mind that your app should load faster. 

How do you track product user behavior?

Product analytics helps you track feature usage, and analyze which designs are getting better engagement. As a result, you can ask the product team to prepare a marketing plan that solves users’ queries. 

This analysis improves retention rates, user experience, and churn rates.

What are the benefits of user behavior analytics?

1. Trends prediction
2. Rapid innovation and development
3. Smart business decision-making and effective marketing campaigns
4. Better utilization of company resources
5. increase in customer loyalty, satisfaction, and lifetime value
6. increase in conversion rates
7. Enhanced fraud detection

Disadvantages of behavioral analytics

1. Every single step online is monitored and tracked.
2. Don’t expect privacy online.
3. Customers must forcibly agree with the policies of data collection.
4. Data collection is a harmful task.

Business applications of Behavioral analytics

Organizations that provide financial services use this concept to find suspicious behavioral patterns to strengthen anti-fraud facilities.

Simultaneously, they link both traffic patterns, and demographic data to customer profiles to identify where to find ATMs and branches.

Retail industries use it to track customer journeys across channels to identify which channels customers use for different transactions; their response to television ads, mobile couponing, and email campaigns.

Health insurance service providers have started using this technique to understand and increase customer satisfaction scores.

Also, hospitals and healthcare providers use it to minimize negative feedback, improve patient engagement, and protect patient data.

To remain competitive and achieve higher retention rates, the telecommunications industries must work on improving the customer experience.

For that, they are using behavioral analytics tools to offer relevant discounts, craft personalized data packages, and run targeted advertising campaigns aimed at retaining customers.

Conclusion:

With the advancement of machine learning algorithms, behavioral analytics has become more useful. It is a never-ending process as businesses move forward; they have to continue maintaining a grasp on frequently changing consumer demands, market trends, and how consumers’ actions and beliefs get distracted by external variables.