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Application of Statistics and Data Mining to Solve Real-World Business Problems

BI Outsourcing

  • Radha R
  • DM Review Online, October 5, 2008

Radha would like to thank Derick Jose, chief architect with the business intelligence practice at MindTree, for contributing this piece.

 

Industries are hungry to gain insights from the terabytes of raw transactional data they have gathered over the years. While standard reports and key performance indicators (KPIs) give them direction, they are seeing better ROI from their existing data infrastructures by moving beyond reports. Moving beyond reports means building analytical applications by using statistics and predictive data mining algorithms. Moving beyond reporting to surfacing interesting customer behavior patterns in data and scoring behavioral propensities for customers, such as cross selling, churn and campaign response, directly impacts the bottom line of organizations. This impact is directly attributable to the data platform.

 

This column looks at examples of four different industries that are using analytics and data in innovative ways.

 

Travel Industry: Monetizing from Search Mining

 

Every time you go to a travel agent to book a ticket on a flight, two broad types of transactions are generated:

 

  1. Search request and response transactions and
  2. Booking transactionss.

While most travel organizations mine their booking-transaction data, not many insights have been discovered from the search patterns for airline booking transactions. For example, if you are a price sensitive tourist looking for the cheapest tickets between Chicago and L.A. on a Friday evening in November a value-conscious business traveler seeking economy- or business-class tickets at the last minute to ensure that you are on time for a crucial meeting in New York.

 

All the search requests and responses are captured in search log files and flushed out at regular intervals. These search logs, which were traditionally seen just as bits of information occupying a lot of disk space, are suddenly viewed as a gold mine of interesting information. For example, by evaluating the most heavily searched destinations from Boston on weekends, an airline could use this information to expand its fleet of services to destinations that it might not currently serve and increase its share of market.

 

Another scenario consists of segmenting agents based on price-conscious search versus value-conscious search behavior. Business users are typically convenience shoppers (correct timing and service excellence is important), whereas holiday shoppers typically are price conscious (getting the lowest price to San Francisco is more important than catching the flight at a convenient time).

 

 

Retail: Customer Behavior Segmentation and Store Segmentation

 

Customers have collected a lot of point-of-sale (POS) data, and overlaying loyalty data can help retail organization and track multichannel behavior. For example, a customer could shop for products using his or her loyalty card on the Web or through the direct store channel. His or her sales across multiple channels can be integrated to build a holistic behavioral view, which can be used to segment customers based on value.

 

Once various value segments are created, one can understand realtionships in products that exist in high value segments. The product purchase behavior of high value and associated affinities to other products can give insight to the retail store on which product affinities are driving greater value for the retail chain. This can drive merchandise-mix decisions for store by location.

 
     

 

Telcom: Predicting Customer Propensity to Churn

 

The telcom sector in the emerging market is quite dynamic with a lot of players entering the space and cross selling innovative data services and value added services to existing customers. Their overall average revenue per user (ARPU) can be enhanced by selling ring tone services, video on demand services, horoscope alerts, etc.  In order to remain competitive, they can collate information available in call detail records (CDRs), which capture every call made from mobiles and are available in switch data. This can be combined with payment behavior (available in telcom billing applications) and integrated with channel and call center information to create a universal telcom customer behavioral profile. The profile can be mined to surface predictors of risk and then develop a churn-scoring model.

 

 


 

Marketing: Text Mining/Sentiment Analysis of Unstructured Information

 

Today people with high spending power devote more time online in social networks, blogs, etc, than watching TV. There is a lot of unstructured information in blogs and social networks that express this younger generation’s feelings. These unstructured comments can be a source of high-value analysis to understand customers’ views on new products. Sentiment analysis can be done by examining comments for keywords used to express both positive and negative sentiments about a product. This can potentially be used to refine product attributes such as size and  packaging color as well as the marketing message itself.

 

 

Preliminary work is being done by telcom, retail, travel and marketing customers in applying statistics and data mining techniques in innovative ways to gain competitive advantage. As the world starts getting numbed by terabytes of raw data that is captured by packaged- and channel-based operational systems, competitive advantage shifts to organizations that can do targeted interventions based on the output of statistical data mining models.


Radha R heads the Data Warehousing and Business Intelligence Practice for MindTree Consulting, an international IT and services company, co-headquartered in New Jersey and Bangalore, India. She is responsible for overall profitability and customer satisfaction for MindTree's DW/BI business across all global markets. With more than 17 years of industry experience, Radha has handled various product and service lines in her career. In her earlier role at MindTree, Radha was responsible for developing and cultivating all its India-based customer relationships.

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