By Stephanie Ventura

Today, customer intelligence isn’t just about collecting and analyzing CRM data. It is about truly understanding customer goals, as well as the journeys they take to achieve those goals, and using that data to optimize every aspect of their experience. To improve their organization’s customer intelligence capabilities, leading business customer experience (CX), analytics, customer care and marketing professionals are now employing journey analytics to manage customer journey data, monitor and measure journeys, and improve experiences and business outcomes.

In this post, we’ll cover seven powerful ways you can improve your customer intelligence capability by using customer journey analytics to look at your customer data within the context of millions of unique customer journeys.

What is Customer Intelligence?

Customer intelligence is the information extracted from customer data with the purpose of understanding customer behavior and producing insights in order to drive future business growth.

Customer data can include behavioral data, demographic data, web and mobile browsing activities, customer sentiment, support team interactions, survey data, social media actions, transactions, customer preferences and sales team interactions.

Why is Customer Intelligence Important for Your Organization?

Customer intelligence is important for our organization because it provides you with the insights necessary to improve key business metrics such as revenue, retention, acquisition, churn, repeat purchase and many others.

Customer intelligence is used by both customer experience, analytics and marketing teams, enabling each to understand not just who, what, when and where, but also why. Customer journey analytics can be used to turbocharge customer intelligence by enabling you to analyze customer data within the context of end-to-end customer journeys.

What’s the Difference between Customer Data, Customer Intelligence and Customer Insights?

Customer Data

Customer data is simply information about customers in raw form. It comes in various formats like an answer to a survey question or a call center transcript or the record of a single purchase.

Customer data is the starting point for analysis, but it is largely useless on its own and without context. Customer data cannot inform you about how your customers are behaving nor can it predict how your customers may behave in the future.

For instance, the fact that I use my credit card three times a month, by itself, uninteresting to my bank. The fact that this number has decreased from six times a month is more interesting. Finally, if you add my demographic details and my preferred branch, this data becomes even more interesting.

Customer Intelligence

Customer intelligence is an outcome of customer data analysis, where the data is put into context and the result is a meaningful understanding of each customer or group of customers.

While customer data could just be a single record or source, customer intelligence results from an analysis of customer data that comes from multiple sources. It enables businesses to meet their customers’ needs and grow profitably.

Customer Insights

Customer insights are the actionable information or output that comes from further analyzing and synthesizing customer intelligence. It is the ‘why’ behind customer behavior which reveals customer motivations, preferences and sentiments. Customer insights are what ultimately drives business action.

Essential customer journey roadmap ebook cover

7 Powerful Ways to Improve Customer Intelligence with Journey Analytics

1. Unify All Your Customer Data

Customer intelligence entails placing data into context in order to make real-world business decisions. Journey analytics helps you improve customer intelligence by connecting all your customer data from different sources in one place to form a single customer view.

Customer data can include:

  • Demographics
  • Web and mobile browsing activities
  • Customer preferences
  • Sentiments
  • Customer support team interactions and transcripts
  • Firmographics
  • Sales team interactions
  • Social media
  • Transactions

This data will typically reside in data warehouses, point-of-sale systems, marketing automation systems, call center data management systems, etc.

The traditional method of data integration through ETL involves first cleaning, standardizing and then loading data into the right tables. This is a massive effort requiring effort, time, high costs and specialized resources. Moreover, it is inadequate to deliver timely results with the exponential growth in the volume and velocity of data.

The most sophisticated customer journey management solutions, which support journey analytics and journey orchestration, provide the ability to integrate data easily without first requiring customer identity matching, schema setup or fixed field mappings for different event types. These advanced customer journey analytics platforms may have built-in ETL capabilities that allow you to extract data from your system in the format that is easiest for you to use.

Customer journey analytics platforms excel at omnichannel integration across all channels. The most impressive part of this data integration is the blazing fast speed with which it takes place. A typical integration that begins with 2-3 data sources can be completed in under a week. Compare that with the weeks and months it takes using traditional processes.

2. Analyze and View Your Customer Data Using a Journey-Based Approach

The best way to arrange isolated pieces of customer data, so that they start to make sense, is to see them from your customers’ perspective—i.e. as part of an end-to-end customer journey.

Customer Journey Path

By thinking in terms of a customer journey, you can improve customer intelligence by bringing together data that spans multiple channels over time.

For example, journey analytics enables you to easily view a macro-journey and drill down into the many micro-journeys that stem from it. It uncovers behavior before, after and within journeys, as well as the number of customers who took certain paths or even friction customers encounter.

Moreover, customer journey analytics software reveals in-journey signals, or behaviors customers exhibit that indicate journey success or failure. How you define success is related to both the journey itself and your business goals. For instance, success could be as big as retaining customers or as small as a high CSAT score at the end of the onboarding journey.

Journey analytics gives you the power to understand the drivers of customer behavior across channels and over time. This enables you to understand customer behavior and interactions within a wider journey context and develop actionable customer insights.

3. Monitor Your Customer Experience in Real Time

Most customer intelligence approaches offer a historical view, which is useful for analyzing trends and performances over time. But customer experience and customer operations teams today want to know what’s happening with customers in real time and adjust the processes accordingly. The historical analysis is thus rendered useless—even before it is implemented.

Journey analytics helps you improve customer intelligence by monitoring customer experience in real time across multiple channels and millions of data points.

4. Orchestrate Relevant and Consistent Experiences

Customers want every interaction with your business to reflect their unique context, encompassing their overall experience with your enterprise. To make this happen, CX teams must understand where a customer is in their journey and what has led to that point, which requires connected data and thorough analysis. Without those capabilities, you’re merely triggering messages within siloed channels.

Improve customer intelligence to personalize offers

For instance, a health insurance provider wants its members to follow a specific medication regimen to maintain their health. One member is prescribed a drug, but never picks it up from the pharmacy. 

Ten days after the prescription is issued, your member receives an email reminding them that it’s time to schedule a follow up appointment. This message is entirely irrelevant to them, as their medicine is still waiting at the pharmacy. 

Rather than have scheduled communications, deliver messages or offers based on in-journey signals. Here, the signal is inactivity. The message should change based on the member’s lack of action instead of prompting them to move onto the next stage in their journey.

5. Enhance Customer Segmentation Using Behavioral Segmentation

Customer intelligence typically analyzes data to segment customers based on demographic attributes such as gender, age, income and firmographic attributes like company size or industry.

To improve customer intelligence, go a step further and segment customers based on not just who they are, but what they do. Behavioral segmentation is a form of customer segmentation that is based on customer behavior and allows you to group customers according to their knowledge of, attitude towards, use of, or response to a product, service or brand.

The objective is to enhance customer intelligence, so that you can get insights to understand how to address the particular needs of a segment of customers, discover opportunities to optimize their customer journeys and quantify their potential value to your business.

6. Monitor CX Metrics and Quantify ROI

Journey analytics enhances customer intelligence by enabling teams to understand which behaviors or in-journey signals impact CX metrics. It provides a quantitative link between customer data and organizational KPIs. 

Sophisticated customer journey analytics solutions offer customizable dashboards to monitor performance, so CX teams can identify pain or friction points before they snowball into larger problems that negatively impact your bottom line.

A few examples of the kind of metrics that you can measure and monitor are:

  • Customer Retention
  • Upsell/Cross-sell
  • Customer Lifetime Value
  • Churn Rate
  • First Call Resolution
  • Profitability
  • Revenue
  • Cost-to-Serve

Quantitative link between customer data and business KPIs

7. Uncover Business Opportunities You Didn’t Realize You Should Look For

Using traditional methods and tools to generate customer intelligence by analyzing customer data is a laborious and slow process, which tends to confine its usage to a set of pre-defined problems.

AI-enabled customer journey analytics can sift through a much larger and more complex data space than any human and thereby uncover many more business opportunities—even opportunities you didn’t realize you should look for. Consequently, you can spend your time prioritizing these insights instead of manipulating the underlying data.

using AI to improve customer intelligence

This is because artificial intelligence imbibes journey analytics with the power to find every single relationship in the customer data that exists (without expressly being told to look for it). It can predict the likelihood of future behavior with high accuracy while finding the drivers and inhibitors of customer performance.

This is because artificial intelligence imbibes journey analytics with the power to find every single relationship in the customer data that exists (without expressly being told to look for it). It can predict the likelihood of future behavior with high accuracy while finding the drivers and inhibitors of customer performance.

Pick a Business Case to Quickly Prove the Value of Improving Customer Intelligence with Journey Analytics

It is important to identify a use case that addresses the most significant pain points being faced by your customers. Here are two examples of enterprises using journey analytics to increase customer intelligence and use that insight to improve CX and impact their business:

1. Identify Customers at Risk of Soft Churn with Customer Journey Analytics

A leading retail bank uses customer journey analytics to learn that usability issues with their mobile app are indicators of soft churn, i.e. when an account remains open, but activity severely drops.

Using Pointillist’s predictive analytics and machine learning algorithms, the team is able to visualize high-impact journeys that lead to decreases in savings account deposits, reduction in app activity and stoppages in auto payments.

By analyzing mobile app usage, the CX team discovers that in-app check deposits and bill payment problems are the main contributors to soft churn. The bank uses the data to justify a redesign of their app experience to improve usability, increase account engagement and ultimately, reduce churn risk.

identify risk for soft churn

2. Use NPS to Prioritize Initiatives to Improve Customer Experience

The customer care team at a regional provider of cable and internet services knows that customers who miss a payment often ask for a late payment fee waiver.

So, they want to evaluate and compare the customer experience for three different channels their customers can use to resolve a late payment fee: the mobile app, web chat and the call center.

By conducting a simple analysis using customer journey analytics, they find that not only did the highest volume of customers make an inbound call to request a fee waiver, but those that did had a lower NPS score than those that resolved the payment fee through the mobile app or web chat. In contrast, the mobile app journey had the highest average NPS score.

Journey-based VoC Measurement

As a result, the customer care team chooses to launch an initiative to guide more customers who missed a payment to the app, so they can increase overall customer satisfaction while simultaneously reducing cost to serve.

To Summarize

The deluge of new customer data sources provides more information to fuel customer intelligence. But to make that information actionable, CX teams rely on customer journey analytics. Journey analytics makes analysis of customer behavior across channels and time possible, and democratizes insights across teams to enable every member of your organization to improve CX and achieve critical business objectives.