Teradata Guided Analytics

Teradata Guided Analytics
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For some, path analysis falls squarely in the realm of web analytics. It's easy to understand why. Website logs give us the perfect opportunity to understand how our customers proceed along paths, and we can often directly and almost immediately associate the things we do to improve those paths with an improvement in our bottom line.


But path analysis can be much more broadly applied. With IoT data, we can identify common paths to machine failure. Airlines can use path analytics to find patterns in how and where bags are lost. And in the medical arena, path analytics can reveal the most common events and diagnoses leading to a specific procedure and, in some cases, help us identify candidates for potentially more effective and efficient treatments.


To that end, my colleague @rratshin put together an excellent video demonstrating how powerful path analysis can be in healthcare.



New Demo Videos!!!

Move over, Avengers. The first big hits of the summer blockbuster season are here.

From the YouTube sensations who brought you such fan favorites as Path Analysis Guided Analytics Interface Overview and Path Analysis Guided Analytics Interface Demo: Predictive Paths comes a massive 1-2 combo!



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

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

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

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

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!


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.


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.

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The text that your customers share when they chat online, post on social media, or call your call centers provides important context about their experiences as they traverse their so-called customer journeys.


Historically though, path (or journey) analysis and text analysis have been distinct domains. Valuable, yes, but difficult or impractical to do in concert. There has been a dearth of tools that could enable you to investigate text in the context of the journey or path from which it came.


That changes today. Yes, you can analyze text and paths together. It’s not only practical and powerful – it’s extremely easy!


The screenshots and copy below walk you through the new text functionality built into our Path Analysis Guided Analytics Interface.

Highlighting a path to view text records

First, you click to “Highlight a Path” and then “View Text Records.”

Viewing the text records of an event.

In the Text tab, events or nodes that contain text are filled in orange in the path at the top of the page. The page also contains a table that displays customer ID’s, time stamps, and the text of the record.

Filter the results by ID or term.

You can filter the results by ID or term, and move between events that contain text.

Highlighted terms

If you filter by term, the term is highlighted in the resulting records, which should help you get through your analysis a bit quicker.

Top tokens for a path

You may want to check out the “Top Tokens” tab to identify some keywords for filtering.


Hopefully the images and workflow above show how easy and powerful this contextual text analysis can be. As a user, you will feel like these text capabilities are seamlessly integrated into your path analytics with the guided analytics interface.


If you’d like to learn more or see a demo, please feel free to send a note to ryan.garrett@thinkbiganalytics.com.


Amazon plans to acquire Whole Foods. Retailers and grocers have only had a couple weeks to process the news, and they still have many questions as they plan their competitive positions and strategies.


That news was still fresh (no pun intended) in my mind as I read the eMarketer Retail headline “Online Grocery Shopping Is No Longer Just a Millennial Story.” Twenty-three percent of households have purchased groceries online in the last three months, according to a survey by TrendSource cited by the article. That statistic may not surprise you. But the fact that 21 percent of baby boomers claim to have done so really opened my eyes and made me think of how rapidly the grocery space is evolving.


There is no denying it — if you as a grocer are not making personalized product recommendations, your days are numbered.


Amazon, along with other web-native powerhouses like Google and Facebook, has conditioned us as consumers to expect extremely personal experiences when shopping online. According to some articles, Amazon generates 35 percent of its revenue through recommendations. Seems like that is certainly one area that Amazon will focus on to increase the return on their planned $13.7 billion investment in Whole Foods.


If you are losing sleep trying to figure out how you can compete with the Amazon juggernaut, allow me to offer up a key piece of what you may be missing — our Product Recommender solution. The solution is built on a variety of advanced analytic techniques including collaborative filtering (which items are purchased together), frequent pattern growth (which sets of items are purchased together) and personalized stochastic approach to link structure analysis (what should I recommend to customer x based on what similar customers are buying).

If you don't know anything about these techniques, don't worry  we can guide you on which techniques are most appropriate and yield the best results based on your use cases.

Product Recommender visualizations

In addition to powering your recommendations, the Product Recommender provides visualizations that help you understand the results of recommendation analyses.


If you’re interested in learning more, I’d be happy to discuss the Product Recommender solution or provide a demo. Feel free to send a note to ryan.garrett@thinkbiganalytics.com.


Our healthcare analytics experience includes various Applications addressing a variety of issues encountered in the complex healthcare delivery network.  Some of the most obvious and pressing use cases are those aimed at improving the quality of healthcare outcomes to provide better levels of patient care.  However, other applications of our analytic tools address different issues surrounding the business of providing healthcare.  One such case recently encountered is a prime example of applying our analytics not only to patient outcomes, but also to business operations supporting healthcare delivery.


Our client is a large, international healthcare provider operating almost 300 hospitals, clinics, and surgery centers.  One of the business challenges they face is the periodic prediction of costs and revenues for each of their hospitals.  Gross under (or over) estimates negatively impact their ability to track operational and financial metrics which materially impact their planning and ultimately their market valuation. 


After digging into their available data, we discovered that the uncertainty originates mostly from just one sub-segment of their patient pool. This segment consists of patients who, at month end, were not yet assigned a Billing Code (also known as a Diagnosis Related Group or DRG in the client’s vernacular).  Assignment of a DRG code significantly narrowed the expected procedure pathway, material costs, length of stay, etc. for any particular patient.  Categorizing a patient into one or more DRG families allows hospital operations to predict a patient’s final cost/revenue with fairly good accuracy. In contrast, patients who were not yet assigned a DRG code were considered completely unknowable.


In reality, any unassigned patient has data that can be used to help predict his or her eventual DRG code. We used information such as medical history, personal profile, and initial symptoms and mined for signals that predict the eventual DRG assignment.  These signals were fed into one or more predictive models to give the best guess of the patient’s expected DRG classification and the expected cost and time metrics.


To validate the usefulness of this approach, Teradata data scientists built and trained a number of predictive models based on SVM, Naïve Bayes, and Decision Trees to determine which had the best predictive accuracy.  Some DRG’s were predicted with 100% accuracy while others proved more elusive. There were at least 2 levels of predictions with different granularities and the best model(s) were ~80% accurate at each prediction.  By using data already available in the client’s systems, we were able to significantly reduce the uncertainty originating from their un-billed patient pool. Unclassified patients who previously caused so much variability no longer wreak havoc on the business operations.


We are happy to employ our analytic techniques such as data mining and predictive modeling to assist clients on any aspect of their business.  However, in many cases (including others presented by this same client) simple reporting and statistical analysis provide sufficient insights without the need for more advanced approaches.  If your prospects have business problems (in Healthcare or otherwise) that might be addressed using our data or analytic capabilities, please get in touch to set up a discussion with our team.


Marketing professional need to know which ads, promotions, emails etc. are actually working.  This feedback allows marketers to optimize their content, promotion, and channel strategies and often determines resource allocation.  Marketing Attribution analyzes and scores exposures along the conversion path and quantifies the impact of each impression.  In Aster, this analysis is available via the native Attribution function call with its various parameters.


The Field Applications team recently released a Guided Interface that makes this function more accessible  and provides graphical outputs.  Once the data sources are configured, the Guided Analytic Interface presents the user with a dashboard with a timeline of impressions and analysis results that can be accessed by business users via a browser.  The interactive results can be filtered to show results by product, campaign, or channel.  Analyses can be set up, shared, and refreshed as data is updated.





The Interface launched with 5 of the most widely used attribution models:


  • First Click
  • Last Click
  • Uniform
  • Exponential
  • Weighted


Users select from these different scoring models to best fit their particular use case, but can also compare multiple models and explore the impact on their results.


There is a recorded demo and a data sheet (attached) for reference, but if you have further questions, please reach out to Russell Ratshin or Dave Gebala or anyone else on the Field Applications team for assistance.


Every analytics vendor steps into the conference room prepared to mesmerize you with an arsenal of PowerPoint decks and data sheets. And almost every vendor's slides, videos and messaging look and sound very similar to the last... especially when you have already heard the pitch 5 times.


Teradata Visual Customer Paths is running in the AWS public cloud RIGHT NOW. And we invite you to take it for a no-hassle test drive. This is not slides or videos or messaging. This is hands-on, in-your-face business value. And no hounding by pesky sales folks while you take it for a spin. We'll hand you the keys and and then check back with you in a few days.


To sign up for the test drive, just fill out the form at Teradata - Path Analysis or contact Ryan Garrett.  We all know there is a night and day difference between the promises of slide-ware and the reality of getting hands-on with technology. Many vendors claim they can let you do self-service path analysis. But not every vendor can hand you the proof. We can.


During a recent engagement with a national retailer we developed and deployed tools designed by and for their business users. In this particular case, the client suspected that their e-commerce site could use some help, but they didn’t know where to start. The data was available in their data warehouse, but not accessible by the product- and category-managers that need it.


Their existing reporting tools summarized and characterized the traffic to their site: source of traffic, pages most visited, time spent on page, etc., but there was a blind spot when it came to the conversion process. We proposed to build an easy-to-use dashboard that summarizes important metrics (KPI’s) for their e-commerce site, with a focus on how and what their site visitors are looking to purchase.


The dashboard puts a visual interface on the results of our nPath function, which handles all the pathing analysis at scale. The user is presented with graphical and textual summaries of the KPI’s needed to track visitor all along their conversion/non-conversion journey. Of the user has the ability to filter and focus on specific time frames, products, categories, etc. as desired.



The metrics now accessible by any user include:


Search Performance

  • Total Search count
  • Successful/NULL Search (counts & percentages)


Search Terms

  • Top 10 Successful/NULL Search Terms


Buying Sessions

  • Revenue $ (Search vs. Non-Search)
  • Order Count (Search vs. Non-Search)
  • Average order Volume (Search vs. Non-Search)


Product Metrics

  • Product-specific conversion stats
  • Activities that lead to click through or purchase



The dashboard has delivered great value for users on a number of fronts. First, it provides a single, shared source for an online store’s performance metrics. It also provides a baseline (from which to measure an ROI once they embrace improvement measures) while simultaneously shining a light on the underperforming areas that are impacting their top line revenue. The dashboard itself is fairly portable and quick to implement since most of the data is already being collected and stored.


Data Science Informative Articles and Blog Posts

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