Perform better, smarter experiments by creating user segments.

Customer Journey Analytics
9 Minute Read
Machine Learning
Automated Machine Learning
B2metric Machine Learning Studio
Customer Journey
Customer Journey Analytics
B2Metric IQ Analytics

Table of Content

  1. What is Customer Journey Analytics? 
  2. Why is Customer Journey Analytics Necessary for the Organizations?
  3. Customer Journey Analytics' Advantages
  4. Customer Journey Analytics in Today
  5. Out of the Box Predictive Insights for Customer Journey Analytics
  6. How does B2Metric AutoML work for Customer Journey Predictive Analytics?
  7. B2Metric ML Studio 


What is Customer Journey Analytics? 

Customer journey analytics and its importance in revealing problems along the customer journey, as well as how this helps to generate pleasant customer experiences, are well-known among today's organizations. The first stage in customer journey analytics is to create a map or diagram that depicts all of the steps customers will take when interacting with a company. From start to finish, these touchpoints encompass the entire arc of a customer's interaction with a brand.


Why is Customer Journey Analytics Necessary for the Organizations?

Customers today demand experiences that are flawless, secure, omni-channel, on-demand and customized. To meet these rising expectations, organizations need to handle organizational, technological and cultural challenges. Journey Analytics is the solution that helps you understand and transform your journeys, at scale. Customer Journey Analytics is the operation of analyzing the customer experience across every touchpoint in the customer journey. 

In an increasingly customer-centered world, the ability to catch and use customer insights to shape products, solutions, and the buying experience as a whole is critically important. 

Customer journey analytics is one of the most effective ways to increase customer lifetime value, improve customer loyalty, improve customer retention rates, and drive revenue growth. Therefore, customer journey analytics is very important for companies.

According to McKinsey researches show that organizations which leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.

Customer journey analytics encompasses advanced analytics ways equivalent to predictive analytics, real time analytics, customer segmentation, and more, so as to supply corporations with unjust insight which will directly impact their bottom line.


Customer Journey Analytics' Advantages 

Companies acquire a deeper understanding of client needs and wants, as well as actionable insights that can inform decision-making, by merging data about customer activity with marketing analytics.

Furthermore, customer journey analytics enables businesses to better estimate and predict consumer behavior based on data gathered from previous encounters and comparable messages delivered across several touchpoints.

Customer journey analytics enables businesses to answer questions like: 

  • When is the optimum time to engage with a specific customer?

  • What are the ideal channels for engaging with a certain client category – or even a single customer?

  • Which clients (personas) are most likely to follow a specific buying path?

  • Customers are more likely to make a purchase through which channels or touchpoints?

  • Before churning, which channels or touchpoints do customers use the most?

Companies can make data-driven decisions to directly influence outcomes, such as engaging with focused customer care efforts when a client is on the verge of churn, armed with answers to these and other crucial questions.

Customer Journey Analytics in Today

By delivering behavioral data that is both 1) observable in operations and 2) more predictive of genuine consumer requirements and wants than socioeconomic segmentations, using big data to understand customer journeys builds a bridge between the two disciplines.

Customer journey analytics is now elevating the experience to new heights. Companies may achieve increased personalisation and better business outcomes by gathering and analyzing more customer data from more sources—and acting on it at the correct time in the customer experience. This involves using artificial intelligence and machine learning to close the personalisation gap and using predictive analytics to improve sales conversions.


Out of the Box Predictive Insights for Customer Journey Analytics 

B2Metric provides an automated machine learning (Auto-ML) based solution which is a unifying layer of a platform that learns continuously and orchestrates customer journeys across all interactions in digital applications. It can fetch data from multiple cloud based and on-premises based silos, data storage solutions, event analytics products and crawling via robots unstructured data aggregation to build up best prediction accurate for journey models. 

B2Metric's AI-Based customer journey predictive analytics platform solution has been run for 8 industries into production. Insurance, Telecom, Retail, Banking and Finance, Digital Apps (Mobile, TV, Web), Media, Automotive and Energy. 

With B2Metric AutoML based predictive analytics customer journey solution, you can manage all your customer data from a single platform. For fast analysis and modelling, you can connect and standardize customer data from any source, online and offline with B2Metric IQ platform. Analyze a customer's journey across multiple channels, visualize and report data with the B2Metric AutoML platform solution. Improve your marketing strategy with artificial intelligence-based insights provided by the platform and improve your data-driven decision-making ability in real time.


How does B2Metric AutoML work for Customer Journey Predictive Analytics?

Organizations are at different data-maturity levels. But regardless of how far along a company is, virtually every organization has valuable customer data assets that could be put to better and more active use. B2Metric understands end user’s behavior, interrogates journeys and investigates ‘hot spots’ along the way with it’s impactful visualizations and AI-powered analytics offerings. Personalize customer interactions through deploying automated machine learning algorithms. This next-best-action engine drives better outcomes from customer journeys. Digitized data points are now speeding up feedback cycles. By using advanced algorithms and automated  machine learning that improves with the analysis of every new input, organizations can run loops that are faster and better. 

To manage journeys effectively, you need to start by aggregating customer data across channels and time. The result is customer journey data, which is time-series data that captures customer interactions indexed by time. It powers real-time modeling and analysis, as well as orchestrating actions to optimize journeys. Unified customer journey data lays the foundation for your entire enterprise. It’s the first step towards aligning cross-functional teams around journeys and breaking down traditional data and organizational silos. B2Metric AI based journey orchestration goes beyond traditional personalization techniques. It leverages customer journey data from every channel, source or system. This way, each interaction reflects the customer’s entire experience with the organization—not just the current interaction. As a result, every moment of engagement is highly personalized for each individual, because the interactions a company takes are based on each customer’s prior experience.

In an increasingly customer-centric world, the ability to capture and use customer insights to shape products, solutions, and the buying experience as a whole is critically important.

Similarly, journey analytics enables you to pinpoint sources of friction that prevent customers from reaching their goals. Continuously tracking omnichannel behavior helps you identify issues that negatively impact both CX and business outcomes. By leveraging B2Metric’s customer journey analytics:

  • Diagnose new problems in real-time 

  • Determine the best way to solve these issues

  • Prioritize improvements based on the potential impact on CX and business objectives

  • Improve retention & decrease churn rate

  • Increase repeat purchases

  • Increase cross-sell & up-sell

  • Decrease cost of service

  • Improve customer satisfaction and loyalty

  • Increase lifetime value

B2Metric ML Studio 

B2Metric ML Studio is an augmented analytics solution that refers to the use of low-code/no-code tools, often leveraging machine learning (ML), to automate various tasks required during the analytics process. It enables you to automatically discover, visualize and narrate important findings (such as correlations, exceptions, clusters, drivers and predictions) in datasets, without requiring people to build data visualizations, create models or write algorithms.  Augmented capabilities are differentiating features in platforms across D&A. They’re also a key factor accelerating the convergence between analytics and data science. 


B2Metric AutoML - Workspace

  • You can manage your work through different workspaces.

  • For each workspace, you can filter your data in that workspace.




B2Metric AutoML - Data Ingestion

  • You can easily ingest your data in CSV, XLSX, TXT formats to the platform in minutes with simple preprocessing steps.


B2Metric AutoML - Experiment

  • You can run Python-based ML models and B2Metric AutoML engine without the need for coding knowledge and data scientists. 

  • You can examine the Interpretable model result reports in detail.


B2Metric AutoML - Interpretable Pipeline Reports

  • Desicion Tree: Decision trees provide an explanation of the model's decision judgment by converting the results of the data distribution learned by modeling into simple rule sets.Thus, you can find out which situations are for or against your business model by following the rule sets.


  • Sunburst: As known as a Sunburst Chart, Ring Chart, Multi-level Pie Chart, Belt Chart, Radial Treemap. This type of visualisation shows hierarchy through a series of rings, that are sliced for each category node. Each ring corresponds to a level in the hierarchy, with the central circle representing the root node and the hierarchy moving outwards from it. Rings are sliced up and divided based on their hierarchical relationship to the parent slice. The angle of each slice is either divided equally under its parent node or can be made proportional to a value. Colour can be used to highlight hierarchal groupings or specific categories.



  • Feature Importance: The feature importance list is the list of variables that affect the results of this model, which best describes the underlying meaning of the data. By looking at the variables in this list, we can classify, prioritize and interpret the factors that affect our business cycle.




  • Feature Dependency: Feature dependency, as known as shapley importance shows importance of features that affect the model prediction result.




  • Lift Analysis: The Lift value of an association rule is the ratio of the confidence of the rule and the expected confidence of the rule. The expected confidence of a rule is defined as the product of the support values of the rule body and the rule head divided by the support of the rule body. The confidence value is defined as the ratio of the support of the joined rule body and rule head divided by the support of the rule body.






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