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Behavioural Analytics

What is Behavioral Analytics?

Behavioral analytics is an area of data analytics that focuses on providing insight into the actions of people, usually regarding online purchasing. It’s used in e-commerce, gaming, social media, and other applications to identify opportunities to optimize in order to realize specific business outcomes.

Behavioral analytics examines the “what’s” and “how’s” of customer behavioral data to inform the “why’s” of customer behavior. This can include tracking page views, email sign-ups, or other important actions like registration. These critical day-to-day insights allow us to further optimize for conversion, engagement, and retention.

For example, if a business is looking for insight into why users bounce before subscribing, they can build out an analysis that aims to isolate points of friction within a specific conversion funnel. The example Funnel above compares user flows of those who open an email and begin their journey to creating an account.

How Behavioral Analytics Works

Behavioral analytics is based on hard data. It uses the volumes of raw data people use while they're on social media, in gaming applications, marketing, retail sites, or applications. This data is collected and analyzed, and then used as the basis of making certain decisions, including how to determine future trends or business activity, including ad placement.

However, there is a lot of ambiguity about the nature of the insights that it yields. For example, online advertisers use behavioral analytics to help them tailor the right offer at the right time. This is often done utilizing the user’s demographic data, any past search or social information, and a locational market to put the user into a bigger group, sometimes called a cohort or demographic. The user is then served with ads or offers that match the ads and offers that have the highest success rate with that group.

Behavioral analytics can support a number of different hypotheses, so the process of elimination comes from experimentation and evaluation. Businesses usually are looking to increase conversions, so if the change makes it worse, that hypothesis can be thrown out in favor of a different one or no change at all.

Behavioral analytics are most often used to inform A/B testing where one variable is changed at a time. As behavioral analytics have deepened and the technology to test multiple changes in real-time evolves, companies are getting much better at targeting customers.

Build an AI chatbot to engage your always-on customers

What types of data can behavioural analytics find?

To gather, use, and generate user behavior data and, create an online strategy in parallel using this information, we must consider that there are three types of data that we can transform into valuable information:

1. Registered data

Data stored in our CRM or marketing automatic tool.

2. Observed data

How our users behave on our website or how they interact with the different elements of the platforms in which we are present. Observing their behavior gives us clues about their interest and how they react to our messaging, published. We examine their behavior, and this gives us clues about their interests and their way of responding to our messages, distributed elements, or phases of each journey.

3. The voice of the consumer

How consumers feel about our services, and how they express those feelings. They can express them either reactively through surveys, focus groups, workshops, or proactively, through social listening, where users, without asking, express their opinions, present doubts, propose improvements or simply participate in conversations about our products, services or issues related to our brand.

Benefits of behavioral data

1. Understanding the customer

The basic idea and motive behind the collection of behavioral data is to understand the customer. Once you have understood the customer you can provide them with goods and services that are more in line with what they are looking for.

2. Anticipate customers’ needs

This is one of the most important benefits of behavioral data. Once you have understood the needs of your current customer, it wouldn’t take long before you can understand the need of your future customers. At the same time, you are able to understand your customers’ needs, you can offer them things they did not even know that they wanted.

3. Innovate faster

Through all this valuable data, you can figure out trends and patterns that you never could have imagined on your own. Through that, you can propose additional offers and capture the mind and market share. This is also many times the inception of innovative and disruptive product innovations.

4. Drive your business more efficiently

Thanks to all this data, the concept of doing business through predictions and intuition are concepts of the stone age. Through the process of customization, you no longer need to post ads targeting every single person in the audience, nor do you need to shoot out a thousand emails every day. You can benefit from more efficient usage of resources such as time, money and employees.

 

Behavioural analytics workflow 

Step 1: Collection of data

There are plenty of sources through which data from the consumer is gathered such as websites, applications, CRM platforms, etc. The consumer doesn’t even realize when a lot of the data is being captured from them in the form of ‘cookies’. These delicious sounding treats are like files that are available on various platforms that store all your information. Those cookies are then swung around the web sharing that information further.

Step 2: Segmentation

This step involves breaking up of all the data into smaller clusters that represent a certain category. To take a simplified example, it could be people who have been active on Netflix in the past month vs people who haven’t. The segmentation criteria would completely depend on the kind of business you run and there may even be multiple segments, since the same dataset can provide you with various information.

Step 3: Implementation

Now comes the part where all the data is put into play and targeted ads are presented to specific segments of the audience. In the case of Netflix, it could be the advertisement for a new show or movie in a way that will catch your eye. They customize the whole page according to your tastes, based on your list of viewed shows. This also applies to the highlighted show on top of your account. It could be the last Netflix Original series or an anime or the last season of your favorite show.  

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Behavioural Analytics

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is Behavioral Analytics?

Behavioral analytics is an area of data analytics that focuses on providing insight into the actions of people, usually regarding online purchasing. It’s used in e-commerce, gaming, social media, and other applications to identify opportunities to optimize in order to realize specific business outcomes.

Behavioral analytics examines the “what’s” and “how’s” of customer behavioral data to inform the “why’s” of customer behavior. This can include tracking page views, email sign-ups, or other important actions like registration. These critical day-to-day insights allow us to further optimize for conversion, engagement, and retention.

For example, if a business is looking for insight into why users bounce before subscribing, they can build out an analysis that aims to isolate points of friction within a specific conversion funnel. The example Funnel above compares user flows of those who open an email and begin their journey to creating an account.

How Behavioral Analytics Works

Behavioral analytics is based on hard data. It uses the volumes of raw data people use while they're on social media, in gaming applications, marketing, retail sites, or applications. This data is collected and analyzed, and then used as the basis of making certain decisions, including how to determine future trends or business activity, including ad placement.

However, there is a lot of ambiguity about the nature of the insights that it yields. For example, online advertisers use behavioral analytics to help them tailor the right offer at the right time. This is often done utilizing the user’s demographic data, any past search or social information, and a locational market to put the user into a bigger group, sometimes called a cohort or demographic. The user is then served with ads or offers that match the ads and offers that have the highest success rate with that group.

Behavioral analytics can support a number of different hypotheses, so the process of elimination comes from experimentation and evaluation. Businesses usually are looking to increase conversions, so if the change makes it worse, that hypothesis can be thrown out in favor of a different one or no change at all.

Behavioral analytics are most often used to inform A/B testing where one variable is changed at a time. As behavioral analytics have deepened and the technology to test multiple changes in real-time evolves, companies are getting much better at targeting customers.

Build an AI chatbot to engage your always-on customers

What types of data can behavioural analytics find?

To gather, use, and generate user behavior data and, create an online strategy in parallel using this information, we must consider that there are three types of data that we can transform into valuable information:

1. Registered data

Data stored in our CRM or marketing automatic tool.

2. Observed data

How our users behave on our website or how they interact with the different elements of the platforms in which we are present. Observing their behavior gives us clues about their interest and how they react to our messaging, published. We examine their behavior, and this gives us clues about their interests and their way of responding to our messages, distributed elements, or phases of each journey.

3. The voice of the consumer

How consumers feel about our services, and how they express those feelings. They can express them either reactively through surveys, focus groups, workshops, or proactively, through social listening, where users, without asking, express their opinions, present doubts, propose improvements or simply participate in conversations about our products, services or issues related to our brand.

Benefits of behavioral data

1. Understanding the customer

The basic idea and motive behind the collection of behavioral data is to understand the customer. Once you have understood the customer you can provide them with goods and services that are more in line with what they are looking for.

2. Anticipate customers’ needs

This is one of the most important benefits of behavioral data. Once you have understood the needs of your current customer, it wouldn’t take long before you can understand the need of your future customers. At the same time, you are able to understand your customers’ needs, you can offer them things they did not even know that they wanted.

3. Innovate faster

Through all this valuable data, you can figure out trends and patterns that you never could have imagined on your own. Through that, you can propose additional offers and capture the mind and market share. This is also many times the inception of innovative and disruptive product innovations.

4. Drive your business more efficiently

Thanks to all this data, the concept of doing business through predictions and intuition are concepts of the stone age. Through the process of customization, you no longer need to post ads targeting every single person in the audience, nor do you need to shoot out a thousand emails every day. You can benefit from more efficient usage of resources such as time, money and employees.

 

Behavioural analytics workflow 

Step 1: Collection of data

There are plenty of sources through which data from the consumer is gathered such as websites, applications, CRM platforms, etc. The consumer doesn’t even realize when a lot of the data is being captured from them in the form of ‘cookies’. These delicious sounding treats are like files that are available on various platforms that store all your information. Those cookies are then swung around the web sharing that information further.

Step 2: Segmentation

This step involves breaking up of all the data into smaller clusters that represent a certain category. To take a simplified example, it could be people who have been active on Netflix in the past month vs people who haven’t. The segmentation criteria would completely depend on the kind of business you run and there may even be multiple segments, since the same dataset can provide you with various information.

Step 3: Implementation

Now comes the part where all the data is put into play and targeted ads are presented to specific segments of the audience. In the case of Netflix, it could be the advertisement for a new show or movie in a way that will catch your eye. They customize the whole page according to your tastes, based on your list of viewed shows. This also applies to the highlighted show on top of your account. It could be the last Netflix Original series or an anime or the last season of your favorite show.  

Let's build your first AI Chatbot today!


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