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User Trials in Bank

“Let’s analysis the records from our cashier, I’d like to know the preference of our customers!”
“Let’s analysis the text cloud to get more closer to our customers!”

Does these sound familiar to you? As a spy of data world, we found that the cashier record doesn’t show the real user behavior of our customer. Event the comment left by customers doesn’t help too much when we do the Kiez Reduction*. The missing information will show us why the customers didn’t make up their mind to pay the bill. So, where to find the information? What are the 101 things that we’ve missed when doing User Behavior Analysis?

To reproduce the location and behavior information that customers left, we built a complete data collection service. Crawling abundant of GIS Data, public opinions from various websites, and cross-analysis with inner CRM collected data. Instead of counting the popular wordings, we found the connections between words and words, did some mathematics by making them into vectors.  We made the business decision making more straight-forward. With precise GIS and user preference prediction, we got more data-driven evidence to discuss with our business developer and marketing partners.

 

Crawling GIS Data, public opinions
Cross-analysis with inner CRM collected data
Found the connections between words and words


Made the business decision making more straight-forward. With precise GIS and user preference prediction, we got more data-driven evidence to discuss with our business developer and marketing partners.

 

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