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Originally published at Code-free Visual Path Analysis: Watch Now.

Marketers need to visually analyze customer paths. IT professionals should be able to visually analyze server logs. Healthcare professionals want to visually analyze treatment paths.


There is no reason any of these tasks should require advanced coding skills.


Check out these demo videos we recently put together for the Teradata Path Analysis Guided Analytics Interface. You’ll see how easy it is to visually explore paths without writing any code. You can export lists of customers (or servers, or patients) who have completed paths or are on specific paths. And you can investigate text associated with events on these paths. All you need to be able to do is specify a few parameters in the interface and click a few buttons.


Predictive Paths

Video Link : 1111

In this demo, we use the predictive paths capabilities of the Path Analysis Interface to identify two sets of customers. One set of customers is at risk of churn. The other group is prospects we may be able to push across the line to conversion.


Cart Abandonment

Video Link : 1112

In this video, we look at “cart abandonment” scenarios with an online banking data set and an eCommerce data set. Also, we showcase the “Add Drops” feature that makes it visually apparent where prospects and customers drop off paths within the Path Analysis Interface.


Leveraging Text

Video Link : 1113

The text analytics capabilities of the Path Analysis Interface are very unique and also very powerful. In this demo, we use text to provide context around complaints within a multi-channel banking data set.


Healthcare Billing

Video Link : 1114

Here, we are looking at healthcare billing data. We want to make it apparent that path analysis use cases are about much more than marketing. Healthcare professionals may also want to look at paths to certain procedures, paths around treatment and recoveries, or paths to specific diagnoses.


If you’re interested in visually exploring paths and patterns, please contact your Teradata account executive or send me a note at ryan.garrett@thinkbiganalytics.com. We can have you up and running with the Teradata Path Analysis Guided Analytics Interface on Teradata, Aster, or the Teradata Analytics Platform in no time!


Using a popular analytic technique to understand behaviors and patterns, data scientists reveal a subtle but critical network of influence and competition, giving this gaming company the ability to attract and retain gamers in this $109B industry.

Insight that can only be found when you combine multiple sources of data with analytics.  With Teradata Aster® Analytics, users apply cFilter.  A function tailor-made for understanding behaviors and opinions.

Looking into the data, amazing patterns emerge.

Understanding the relationships that drive user behavior can help developers create better games to attract users, prevent churn, and determine how gamers influence each other.

Art of Analytics: The Sword - Sri Raghavan  

Video Link : 1106

Understanding relationships and influence with a collaborative filter helps multiple industries. 




They can understand their customers behaviors.  Then influence the customers who, in turn, influence their network.

Related Links:


Customer Churn


Art of Analytics

Predictive Analysis

Teradata Aster® Analytics


Originally published at /community/bso/blog/2017/11/16/what-happens-next-using-paths-to-predict-prevent-and-accelerate-behav....

Do you know which customers are likely to churn? Which prospects are likely to convert?


Historical path analysis is a critical factor in such predictions. The problem is path analysis is hard. And even when companies have such capabilities, they often reside in the hands of a few specialists – or vendor consultants.


The business analysts, marketers and customer support professionals who could ultimately act on these predictive insights to improve customers’ and prospects’ journeys are effectively left out in the cold. Even the specialists are ultimately confined to the limits of their tools.


Ask anyone who has used a traditional business intelligence tool to understand customer paths. It requires significant time and patience to shoehorn this type of analysis into a tool that was not designed for it. To begin with, just manipulating the data to build an event table for a BI tool is a significantly high hurdle. And even at the end of such a project, organizations end up with a static, inflexible report on historical data that does little to help businesses prevent future churn or accelerate future conversions. (This is hardly a criticism of BI tools, as their benefits and value are well documented. I’m only pointing out that path analysis historically is not one of their strong suits.)


Other advanced approaches leverage statistical tools like R and programming languages like Python. They may incorporate sophisticated analysis techniques like Naïve Bayes text classification and Support Vector Machine (SVM) modeling. But, at the end of the day, these are not tools or techniques for businesspeople.


And at the end of the day, what matters is providing your business teams the opportunity to influence the customer experience in a manner that is positive for your business.


The solution is to bring path analysis – including predictive path analysis – to the business. For such a solution to succeed, it must be:

  • Visual. For marketers and business professionals, the ability to visually explore analytics results is critical. Tree diagrams are instantly understandable, as opposed to results tables that require the user to read through thousands of rows.
  • Intuitive. Most analysts and marketers are comfortable using business intelligence tools to understand their data. We use point-and-click interfaces to interact with information every day. But marketers are not comfortable directly manipulating data with SQL or applying advanced statistical models to that data for predictive results. Even predictive results must be returned with a few clicks.
  • Code-free. Your marketers are expert marketers. They shouldn’t need to be expert programmers to understand which customers are on negative paths and which prospects they can help push over the edge to convert.

The new Predictive Paths capability in the Teradata Path Analysis Guided Analytics Interface makes this interface a solution to consider.

Using the interface, marketers and analysts use a simple form to specify an event of interest – a churn event or conversion event, for example – and whether they want to see paths to or from that event. The interface returns results in the forms of several visualizations, including tree, sigma, Sankey and sunburst diagrams, as well as a traditional bar chart.

Within the tree diagram, users can select partial paths to their event of interest and create a list of users who have completed that partial path but not yet completed the final event. For example, if you are looking at an online banking data set and see that a path of “fee complaint, to fee reversal, to funds transfer” precedes a large number of churn events, in three clicks you can generate a list of customers who have completed the path “fee complaint, to fee reversal, to funds transfer” but not yet churned. Thus, you have just used Predictive Paths to identify potential churners without writing a line of code.

Video Link : 1105
This video demo shows how marketers and business analysts can predict next steps for customers with the Path Analysis Guided Analytics Interface.


Watch this short video to see how Predictive Paths works within the Path Analysis interface. If you’re interested in bringing these capabilities to your business teams, please contact your Teradata account executive today.


Combining the collaborative expertise of data scientists, geophysicists and data visualization an integrated oil company developed new understandings of complex reservoir management with data and analytics. This business case easily transcends multiple industries focused on asset utilization and optimization.

The Sailor - Duncan Irving 

Video Link : 1091

Art of Analytics: The Sailor - YouTube 


Related Links

Data Visualization


Fusing business acumen, data science, and creative visualization, the Burning Leaf of Spending enabled a major bank to detect anomalies in customer spending patterns that indicate major life events, and provided artful insights into the personalized service required to enhance the customer experience, improving lifetime value.

Burning Leaf of Spending - Christopher Hillman 

Video Link : 1090


Related Link:

Detecting Anomalies


Advanced analytic techniques, combined with innovative data visualization, identifies suspected claims fraud, enabling a property and casualty insurer to automate new business rules to improve the fraud investigation process and mitigate risk.

Text Mining

Graph Analysis

Cluster Analysis

Predictive Analytics

All techniques behind fraud invaders. Giving business value in risk mitigation to any industry to identify exposure across the enterprise.  Giving companies the ability to detect suspicious fraud at higher rates with new information sources and increased automation. Avoiding costs and liability.

Art of Analytics: Fraud Invaders - Christopher Hillman 

Video Link : 1089

Other Content by Christopher Hillman 


Related Links:

Text Mining 

Graph Analysis

Cluster Analysis

Predictive Analysis

Detecting Fraud


Using analytic techniques that normally follow the "Customer Journey," Teradata Think Big consultants and data scientists use data and analytics to visualize & identify ‘The Human Journey,” allowing Buttle UK to identify and fulfill needs for at risk.

Video Link : 1088

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