There's silence as your rep calmly listens to this obviously unhappy customer - and pulls up a raft of information about him, ranging from a few years' worth of transaction data (from the data warehouse) and service call information (from the service department databases) to call history (from the CRM system) and what he's said about your company on Twitter, Facebook and the blogosphere. There may also be a stream from previous online chats or, thanks to cookies, a list of where he's been when searching your website.
All this information is compiled so the rep can see, through a visualization tool, that this is actually a good customer who's just having a bad day: He hasn't been troublesome in the past, he frequently Tweets and therefore has a high Klout score (which makes him a social media influencer, presumably with lots of followers), he gave you a Facebook "like" and he spends a fair amount of money with you.
This gives the rep the green light to offer this customer a refund, a free return shipping label and a coupon for 20 percent off his next purchase. The customer is happy; and, even better, he's decided you aren't so bad after all. Case closed.
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Keywords: supply chain management IT, supply chain solutions, value chain IT, retail supply chain, analytics on retail customers, analyzing customers' buying patterns, 'big data' analysis of customer purchasing patterns
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