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 and how this helps generate pleasant customer experiences are well-known among today's organizations. The first stage in customer journey analytics is creating a map or diagram that depicts all the steps customers will take when interacting with a company. These touchpoints encompass a customer's interaction with a brand from start to finish.

Why is Customer Journey Analytics Necessary for the Organizations?

Customers today demand experiences that are flawless, secure, omnichannel, on-demand, and customized. Organizations need to handle organizational, technological, and cultural challenges to meet these rising expectations. 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 and retention rates, and drive revenue growth. Therefore, customer journey analytics is crucial for companies.

According to McKinsey, research shows that organizations that 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 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 want and actionable insights that can inform decision-making by merging data about customer activity with marketing analytics.

Furthermore, customer journey analytics enables businesses to estimate better 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 entertaining a particular 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 narrows 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 1) observable in operations and 2) more predictive of genuine consumer requirements and wants than socioeconomic segmentation, 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 personalization 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 personalization gap and predictive analytics to improve sales conversions.

The Box Predictive Insights for Customer Journey Analytics

B2Metric provides an automated machine learning (Auto-ML) based solution, a unifying platform layer 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 the best prediction accuracy for journey models. 

B2Metric's AI-Based customer journey predictive analytics platform solution has been run for eight 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 modeling, you can connect and standardize customer data from any source, online and offline, with the 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 users’ behavior, interrogates journeys, and investigates ‘hot spots’ along the way with its 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 faster and better loops. 

To manage journeys effectively, you must start by aggregating customer data across channels and time. The result is customer journey data, time-series data that captures customer interactions indexed by time. It powers real-time modeling and analysis and orchestrates 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, capturing and using customer insights to shape products, solutions, and the buying experience 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 identify issues that negatively impact 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 uses 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 significant 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 crucial in 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

• Simple preprocessing steps allow you to quickly ingest your data in CSV, XLSX, and TXT formats to the platform in minutes.

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 explain the model's decision judgment by converting the results of the data distribution learned by modeling into simple rule sets. Thus, following the rule sets, you can find out which situations are for or against your business model.

• Sunburst: A Sunburst Chart, Ring Chart, Multi-level Pie Chart, Belt Chart, and Radial Treemap. This type of visualization shows hierarchy through a series of rings sliced for each category node. Each round corresponds to a level in order, 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 piece is either divided equally under its parent node or can be proportional to a value. Color 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, aslso 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 desired confidence of a rule is defined as the product of the support values of the ruling body and the rule head divided by the support of the ruling body. The confidence value is defined as the ratio of the support of the joined rule body and rule head divided by the help of the ruling body.