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:
- Search request and response transactions and
- 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.
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