While the data is collected in real time and the bank could make the offer minutes after the customer deposit, CBA determined the ideal timing. Waiting three days to make an offer was too long, but making the right offer within the day lead to significantly improved conversion, as 60% of customers took action. Previously, only 10% of customers responded. Of those who got an offer, 38% converted and kept their money in CBA's accounts.
Banks that collect Big Data on customers' regular banking habits can also tailor how they communicate offers and pick the right channel (i.e. calling some customers, emailing others, and texting millennials).
Customer Service Goes Virtual
Banks are using Big Data to make sweeping workforce adjustments. By analysing platform and usage data by channel, one bank noticed that the bulk of its loan applications were coming in from customers using tablets between 9 p.m.to 10 p.m., long after branch loan officers had gone home. The pipes of customer service had to be completely changed, and the bank had to be keenly aware of how its loan application processes performed (and appeared) on tablets, both Android and iOS-based devices.
Banks also have to react to new OS upgrades that affect customers on a tablet or mobile. With the recent Apple iOS 8 upgrade, banks had just a few days to prepare their mobile platforms for customers using the new OS. And if that customer needed help late at night to complete a loan application, the bank hadto be ready. So it allocated its workforce accordingly, adding staff to its call center at peak evening usage hours and offering real-time support for customers over IM or chat.
Banking Gets Social
Social media is another source for Big Data that is improving customer interaction. Barclays Vaswani watches a live feed of the bank's Twitter account in his office, so he can monitor tweets relevant to his brand, the banking processes, and the branches. Watching the customer requests for assistance, Vaswani and his team can see which branches are experiencing problems, such as slow customer support or long queues.
Vaswani will call a branch manager to find out what is happening if he sees too many customer tweets asking for help. Barclays has learnt that banks have to be proactive and not just monitor social media, but manage customer support using Big Data analysis from Twitter, Facebook, and related hashtags.
Social media, along with e-mail based Big Data analysis, is helping banks identify trends. They can monitor the noise and chatter to determine customer feelings around a new product. By looking at the semantics in the text, the bank can analyse content and determine if customers are frustrated, happy, sad, or upset with their overall banking experience.
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