by Craig S. Mullins
A large wireless phone service provider was concerned with the number of customers it was losing. Every customer lost costs the company $53 in monthly revenue. Although the revenue looks small on a customer by customer basis, with a large customer base the company was losing millions of dollars each month. Using advanced analytics they were able to develop an attrition model to predict which customers were most likely to terminate their contract. In doing so, the company developed a model to cross-
That is the type of success story common among companies that have deployed advanced analytics to better understand their data. Advanced analytics is a business-
Traditional business intelligence enables us to understand the here and now, and even some of the why, of a given business situation. Advanced analytics goes deeper into the “why” of the situation, and delivers likely outcomes. Although advanced analytics cannot infallibly predict the future, it can provide models for judging the likelihood of events. By allowing business managers to be aware of likely outcomes, advanced analytics can help to improve business decision-
Advanced analytical capabilities can be used to drive a wide range of applications, from operational applications such as fraud detection to strategic analysis such as customer segmentation. Regardless of the applications, advanced analytics provides intelligence in the form of predictions, descriptions, scores, and profiles that help businesses better understand customer behavior and business trends.
Issues in Deploying Advanced Analytics
When implementing advanced analytics projects it is not uncommon to encounter problems along the way. One of the potential difficulties involves managing and utilizing large volumes of data. Businesses today are gathering and storing more data than ever before. New data is created during customer transactions and to support product development, marketing, and inventory. And many times additional data is purchased to augment existing business data. This explosion in the amount of data being stored is one of the driving forces behind analytics. The more data that can be processed and analyzed, the better the advanced analysis can be at finding useful patterns and predicting future behavior.
However, as data complexity and volumes grow, so does the cost of building analytic models. Before real modeling can happen, organizations with large data volumes face the major challenge of getting their data into a form from which they can extract real business information. One of the most time-
For example, consider a financial services provider that is confronted with detecting and preventing fraud. Each transaction must be analyzed to determine its validity. The retailer waits for approval while this is done in real-
Today’s nimble organizations need to assess and respond to events in real-
As good as real-
Let’s move on and examine the solutions Teradata offers to these issues involved in implementing advanced analytics solutions.
Advanced Analytics Solutions
Teradata Warehouse Miner helps reduce the cost of analytics by pushing the data intensive tasks directly in the Teradata database. Teradata Warehouse Miner provides scalable enterprise analytic modeling technology that reduces data mining cycle time and provides faster delivery of information. Because it performs all of the analytic functions within the database, it significantly improves performance and provides the scalability required to build analytic models. Teradata Warehouse Miner also provides an interface that enables other tools to run models directly in the Teradata database and features to facilitate creation of the analytic data set required for all other advanced analytic modeling tools.
Teradata Warehouse Miner is a set of advanced analytic products that automate and optimize your enterprise analytic process that:
Furthermore, deploying best practices can enable a Teradata warehouse to be analyzed using statistical analysis tools such as SAS and SPSS to mine larger volumes of data. For example, a global entertainment company was running a SAS model to forecast the sales of new movie titles. Each forecast required a little over 7 minutes per title. This was reasonable until running the model against 300 titles translated to 36 hours. By splitting the task and pushing 90% into Teradata, the company was able to dramatically improve performance by a factor of 28.
Additionally, Teradata supports real-
The combination of the Teradata warehouse and Compudigm’s seePower software delivers the ability to query against atomic level data across the enterprise in real time delivering intuitive visualizations that can be viewed over space and time. By combining thousands of data points into a visual representation, business users can more rapidly glean trends and patterns from massive amounts of data – making information more readily available, digestible, and actionable.
By using seePower to analyze customer history in a Teradata warehouse you can score customer history and manage real-
Teradata and Compudigm offer a particularly useful implementation of visualization and real-
Visualization and real-
And remember the speed to market issue? Teradata can help reduce time required to deliver on the many tasks required to support advanced analytics projects. Instead of requiring days or weeks for data extraction, joins, subsetting, merging, aggregation, and transformation, all these functions can be done directly in the Teradata Database against large data volumes. Teradata Warehouse Miner facilitates the setup of this analytic data set so SQL novices can quickly create an analytic data set optimized for Teradata. One customer built a ten million customer record with more than 500 different variables. On a server, this task took more than six hours; however, by moving the processing into Teradata Database, the process took 15 minutes.
Best practices are also important. When models supporting analytics projects are run infrequently, or there are only a few models, it makes sense to do this work as a part of each specific project. However, when an organization begins incorporating dozens or hundreds of models into their business environment on an ongoing basis, the repeated manipulation of large amounts of data becomes inefficient. Teradata provides best practices that greatly condense overall processing cycles and vastly reduce the time to create, update, or implement any given model.
As many models are built over time, certain standard metrics and manipulations will surely become apparent. For customer analysis, it is hard to imagine that total customer spending or number of customer transactions would not be of interest in most analysis efforts. Similarly, it is hard to imagine that total store sales for recent periods would not be of interest to most store level analytics. At the same time, any required cleansing or recoding of the detail data required to facilitate such roll ups will be constant once the right analytic procedure is established.
An Enterprise Analytic Data Set (ADS) takes the standard data rollups that are used in a variety of analytic tasks and centralizes their generation. Instead of each analyst or process having to incorporate all of the logic and consume all of the processing time needed to derive the data for each analysis, the standard metrics will be created in an automated fashion on a regular schedule and made available to all analysts and processes. Any entity that will be the focus of a wide range of analytics is a candidate for an Enterprise ADS.
This methodology improves consistency in the methodology that various analysts and processes use to generate their analytical data sets. And, the chance of an error being made due to the omission or altering of appropriate logic is removed. Overall system processing cycles are greatly reduced since variables requiring a lot of heavy processing will be computed once, stored, and shared, rather than being run time and again. Analysts can get straight to adding value with their work rather than focusing repeatedly on the same basic prep work. Basically, the Enterprise ADS provides a simplified view of the data warehouse by providing a condensed manageable number of analytic tables to view that represent hundreds of tables of detailed data.
A Customer Example
In 1994 Continental was ranked at the bottom in most airline customer satisfaction categories. Today, Continental has turned that around winning accolades for its business performance.
Continental didn’t have a clear idea of who their customers were. The company had multiple definitions of “customer” and “value,” but no data to define each. They were running dozens of databases with duplicate information and too often their highest-
Continental deployed the Teradata Warehouse to build a compensation history and standardized compensation matrix, resulting in improved customer satisfaction and loyalty. It also identified where the customers fit on the value chain. Continental was able to identify three primary segments which aligned with its customer’s profiles. The high-
Now, based on these profiles, if there’s a flight delay of 90 minutes or greater, customers in the highest two segments are treated differently. Since compensation doesn’t motivate the high-
The result is happier customers being treated individually by the airline and cost savings because compensation is only given to the customers who best react to it. Advanced analytics programs such as this have helped Continental to move from Worst to First to Favorite among US airlines.
The Bottom Line
The end result of deploying advanced analytics is increased productivity with the ability to gather and analyze large volumes of data to deliver faster, more-
From Teradata Magazine,December 2006.
© 2012 Craig S. Mullins,