With the economy still on the mend and consumer spending far from robust, retailers are scrambling for every sale. What's more, they're also struggling to make sense of customer interactions across retail stores and e-commerce websites even without the new complications of mobile commerce and social commerce.
The Customer Intelligence Appliance adds a retail industry data model; data-cleansing and extract, transform, and load (ETL) routines; and multi-channel analytic capabilities to the company's ready-to-run data warehousing package. The data model and data-cleansing and ETL software are designed to unite point-of-sale data, e-commerce clickstreams, call center records, and even keywords from Facebook and Twitter posts. The package is designed to get retailers up and running quickly, analyzing tens or hundreds of terabytes--even petabytes--of cross-channel information for a better understanding of customers.
[ Want more on retail strategy? Read 10 Big Tech Ideas For Retailers. ]
So-called "unstructured" or "semi-structured" textual information from social networks is often associated with Hadoop, a base IBM covers with its IBM BigInsights software. The Customer Intelligence Appliance is a high-scale relational data warehousing product for structured data, so it can't do everything Hadoop can do with such data. But using software contributed by partner Aginity, the appliance can bring text into the warehouse and reveal the gist of customer sentiment and purchase intentions.
"We can predict behavior based on what we can capture, so if a shopper is tweeting that they're looking to buy a washer and dryer and we can attribute it to a particular customer, then we can send a relevant promotion to that customer while they're still in the market," said Jim Kelly, a VP in IBM's retail and distribution sector, in an interview with InformationWeek.
Once data from multiple customer interaction points is ready for analysis, the system can apply classic RFM (recency-frequency-monetary value) and behavioral-segmentation analytics to better understand and predict customer behavior. It can also enhance what's known about customers by matching records of online customer behavior to known in-store customers and loyalty program members.
"Most retailers are thrilled just to be able to integrate their point-of-sale loyalty data with the online channel and understand the cross-channel interactions," said Kelly.
These analyses than then inform downstream decisions about campaigns and offers delivered by third-party CRM systems or IBM's own Unica, Coremetrics, and Sterling e-commerce products.
A majority of retailers are still updating their warehouses with overnight batch uploads, according to Kelly, but the Customer Intelligence Appliance is ready for near-real-time performance with micro-batch updates and continuous feeding of point-of-sale data within a matter of minutes.
IBM's grand (though yet-to-be-realized) vision is to integrate the appliance with its network-sensor, real-time stream-processing, and BigInsights capabilities to support real-time customer analysis and marketing initiatives.
"If I'm at Best Buy looking at a 52-inch TV, and you just found out, through your wireless network or through my tweets, that I'm on my smartphone price shopping at Walmart.com, wouldn't you want real-time capabilities to be able to send me an appropriate offer?" said Kelly. "That's the goal and the long-term promise of the technology."
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