Definitive CX Guide
Customer Experience Analytics for Product & Marketing Teams
Read now
EXPLORE SCUBA

Everything You Need to Know About

360° Behavioral Analytics

and How Leveraging It Can Benefit Your Enterprise

There’s no doubt that knowing your customers is important - things like their age, location, gender, income, and other demographic information. But understanding who they are is only half the battle. You also have to learn about what they do

 

Two prospects can appear identical in terms of demographic makeup, yet the ‘how’ and ‘why’ behind their purchases are drastically different. Not only that, but the buyer’s journey is becoming ever more complex - with the potential to occur over multiple devices, platforms, and interactions with your brand over a period of days, weeks, or months. 

 

This is why more enterprises are turning to behavioral analytics to gain a clearer and more dynamic picture of their customers and the buyer journey overall. Thanks to advanced analytics tools that gather continuous data across these scattered touchpoints, enterprises can now observe and optimize every aspect of the customer experience. 

 

From the very first interaction with your brand to clicking the buy button, and everything in between, you can connect the dots and see what’s working versus what needs improvement. If you want to optimize for engagement, conversion, and retention, behavioral analytics is one of the most powerful tools in your arsenal. 

 

What is behavioral data?

 

Behavioral data refers to information that’s generated each time a customer interacts with your brand. Behavioral data may include small actions or “events” such as:

 

what-is-behavioral-analytics 

 

In addition, events have “properties” that further qualify or describe the events, such as device type or time of day. Whether your customers are business entities or individuals, behavioral data can always be tied back to a single end-user - which is crucial for personalization & segmentation within target demographics.

 

Observing customer behavior is not new, of course. Marketers, researchers, and product developers have been using behavioral data for years via call centers, help desks, surveys, and other qualitative research techniques. In the digital age, however, we leave behind a great deal more data that can be tracked and learned from by using behavioral analytics software.

What is behavioral analytics?

 

Behavioral analytics, also known as user behavior analytics or consumer behavioral analytics, focuses on tracking, sorting, analyzing, and measuring user behaviors over time in order to gain insights. Tracking behavioral data empowers marketers, product managers, data analysts, and others to better understand not only what their customers do, but also how, why, and when they do it. Behavioral analytics seeks to answer questions about customer behavior such as:

 

  • How frequently customers shop
  • Number of touchpoints before they purchase
  • Where are friction points along the buyer journey
  • Which products and features they prefer
  • How they perceive your marketing, sales, and customer service offers

 

With information about how people interact with your product and/or digital properties, you can develop the most effective strategies for: 

 

  • Improving onboarding 
  • Increasing conversions
  • Deepening engagement
  • Maximizing retention

 

Research from McKinsey & Company says organizations that leverage customer behavioral analytics “outperform peers by 85% in sales growth and more than 25% in gross margins.” It’s exactly how companies like Facebook, Google, Amazon, and Netflix became the digital giants that they are. For these companies, behavioral analytics in action might look like: 

 

  • Search suggestions popping up in the Google search bar as you type
  • Amazon’s “customers who bought this item also bought” feature
  • The personalized recommendations you receive from Netflix based on your viewing activity


Facebook’s, ‘Aha moment’ came from tracking behavioral analytics, and understanding the key to user loyalty boiled down to new users earning 7 friends in their first ten days.

Are you obsessed with providing the best

CX

at your organization?

Book a personalized demo

What is behavioral segmentation

Market segmentation has always been a key piece in the development of an effective marketing strategy. Dividing customers into groups based on traits or other data about them allows for better targeting and service delivery, as well as higher sales. Common types of market segmentation include:

 

  • Demographic - Age, education, income, family size, nationality, etc.
  • Geographic - Sometimes considered a subset of demographics, or can stand alone
  • Psychographic - Needs, priorities, values, opinions, interests
  • Technographic - Platforms, devices, and systems used by a company or individual

Behavioral segmentation is another major type of market segmentation that categorizes users based on behaviors, actions, and decision-making patterns. For example, you could segment your customers based on:

 

  • Purchasing behaviors - How, when, where, and how often they purchase and what barriers occur along the path to purchase
  • Popular feature usage - How they use features, how often, how much time they spend on it, which features are most popular 
  • Stage in the buyer’s journey - Including how long they stay in each stage and what propels them forward
  • Product usage - Such as casual, core, and super users

 

Often used in combination with other types of market segmentation, behavioral segmentation provides the greatest opportunities for marketing personalization, product optimization, and improving the customer experience.

How to conduct a behavioral analysis

A customer behavioral analysis involves collecting and evaluating behavioral data surrounding your customers and how they interact with your company. Conducting a behavioral analysis of your customers is a multi-step process:

  1. Identify your primary audience segments
  2. Determine business priorities for each segment 
  3. Obtain qualitative and quantitative data using behavioral analytics tools, etc. 
  4. Analyze your data 
  5. Apply to a campaign 
  6. Measure and iterate

This process can be repeated over time to provide insight into the different variables that influence an audience at any given point in the customer journey.

 

customer-satisfaction-graphic

Who should track behavioral analytics?

Behavioral analytics can benefit industries, roles, and organizations across the board. Enterprises that have a full picture of their customers can make decisions based on data, rather than guesses. 

 

To understand behavioral analytics in practice, here are a few examples of how different roles can use behavioral data to their advantage: 

Product Managers

One way product managers can use findings from behavioral analytics is to accelerate the sales cycle. For example, observing product usage behavior to pinpoint the moment a user approaches the threshold of a free service. When someone has used 98% of their allotted free storage space, the product manager could trigger a notification to the sales team to reach out for a conversation or promotion surrounding an upgrade. 

Marketers

Marketers are perhaps the best known for tracking customer behavior. For example they use A/B testing to find ways to increase the revenue of ad campaigns in addition to tracking user engagement metrics like open rate, click through rates, bounce rates, and conversions. Marketers may use heat map analysis to gain better views of on-page behavior. The goal for all marketing efforts is to improve customer lifetime value, reduce churn and optimize messaging to deliver to the right person at the right time.

Operations & Human Resources

“People analytics” is developing fast in the HR world. Human resource professionals have started tracking email, social media, intranet, and digital documents for insight into employee performance and engagement. Since high engagement with company platforms usually signals low absenteeism (and vice versa), behavioral data can indicate who may be at-risk for leaving the company or who has the potential for leadership.

We can also look at some examples of behavioral analytics by industry

ecommerce-icon
Ecommerce

Naturally, ecommerce brands want to understand shopper behaviors surrounding cart additions and online checkout. If brands see a high bounce rate at a particular step in the process, it signals a friction point where the experience needs to be tweaked. Additionally, they want to know how often a user views the same item before buying it (or if they buy it), as well as insight into products with high purchase correlation or high return rates.

travel-icon
Travel

In the hospitality and travel industry, companies like Hotel Tonight, Expedia, and Hipmunk want to identify listings, vacation packages, and social posts that lead to high levels of bookings. To do so, they look for commonalities among the highest-performing offers and use those insights to create guidelines and standards for offer creation. Additionally, they can analyze conversions in order to pinpoint the time of highest engagement and move people more efficiently through the buying process.

saas-icon
SaaS

The tech space is known for continuous improvement via new features, bug fixes, and other ongoing updates. In order to provide these real-time improvements to the customer experience, SaaS companies (like Asana, SurveyMonkey, GitHub, Slack, etc.) must be able to monitor site conversion flow, feature usage, website vs app engagement, and other measures of product usage and satisfaction. 

 

By quickly adding requested features and removing glitches or friction points, SaaS companies can keep customers happy and reduce churn, while pushing for new signups.

 

behavioral-analytics-graphic

Types of behavioral analytics tools on the market

One of the biggest obstacles in behavioral analysis is getting a complete view of customer data points under one roof. Ideally, enterprises would have access to a single source of truth for their data, rather than pulling data from multiple sources and running analyses on multiple tools. 

 

One complete system allows for faster decision-making and the ability to make data-driven decisions with confidence. Too often however, enterprises are piecing together fragmented data from multiple sources, leaving them with an incomplete picture of these data points. 

There are a number of behavioral analytics tools on the market. The one that’s right for you depends on your enterprise’s: 

  • Goals and objectives
  • Preferences for features, functionality, and reporting
  • Technical understanding vs need for ease of use
  • Data sources and data volume
  • Budget

Product Analytics

When it comes to product analytics, Amplitude and Mixpanel are popular solutions which allow you to analyze product interaction across your sites and apps. If you need deep user insights or operational analytics however, you’ll need another tool. 

 

Both of these platforms also require some SQL knowledge, which tends to severely limit adoption within the organization, since using the data will likely require help from data scientists. Finally, if you’re pulling data from multiple silos, you’ll lose precious time blending events from multiple data sources.

Data Visualization

Tableau takes your databases and spreadsheets and transforms them into beautiful visual reports. Tableau is strong on data visualization, but lacks the functionality of a true analytics tool. You can’t create new queries on the fly with Tableau - you have to import all your data manually (as opposed to source event data) which means it requires ETL work (extract, transform, load) before you can use the tool to gain insights. 

 

For these reasons, Tableau and other visualization tools are usually complementary, rather than standalone tools for complete and ongoing behavioral analysis.

Operational Analytics

There are many tools on the market that help organizations handle large amounts of operational data. However, they are usually data warehouses or data lakes, each of which have shortcomings.

 

Data lakes (like Databricks) are filled with raw data, which can make querying against them difficult. Data warehouses (tools like Snowflake and Redshift) deal with processed data, but they aren’t flexible because the data is processed for a single, specific purpose. Modifications to your table structure require lots of ETL work, which can be tedious and increase time-to-insight.

Conclusion

Scuba is an enterprise-class behavioral analytics tool that allows for greater complexity of analysis, speed, and scale than competitors, with costs that scale to your organization. Scuba is a mix of both a data warehouse and a data lake, which means you can store anything in the database, and don’t have to leave anything out. And, querying the Scuba "data lake" is very easy, and provides the flexibility to analyze new behavioral patterns on-the-fly.

 

You might have very specific needs with your behavioral analytics tools, but why not have one that offers flexibility and insight for the entire company? Scuba is the only product on the market that can perform product analytics, data visualizations, and operational analytics in a single tool, like Scuba. And it doesn’t require code, which means anyone in your enterprise can use it.

 

Talk to a

sales professional

to learn more!

Connect with an expert