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03 November 2016

Key things:

- Retail banks are providing innovative customer services via beacons, internet of things (IoT), and wearable technologies.

- Mobility has been the biggest disruption of recent times, and a growing proportion of customers are now demanding banking applications on their mobile phones and smartphones.

- However, offering consistent banking services across all channels in an omnichannel scenario is one among the many other challenges faced by banks.

The omnichannel approach involves a harmonious integration between the different banking channels including online, mobile, social, ATMs, and physical branches. This approach modifies the customer experience from 'fragmented banking blocks' to ensure a seamless banking experience. The sharing and integration of consumer data at the back end of the omnichannel approach results in a unified consumer experience whether in sales, transaction or service.
The internet age has brought about increasing levels of customer expectations. Driven by the demands of millennials, banks will need to deliver real-time customer service on digital channels. Instant gratification rules supreme on the web and customers are no longer willing to be left listening to the irksome tunes on a customer service hotline. It’s unclear exactly how challenger banks will gain a distinct competitive edge with customer service but better use of data and Artificial Intelligence will likely be key.
Advancements in data science and AI make it possible to give faster and constantly-improving levels of online customer experience across much larger customer numbers, meaning companies with the best algorithms – and especially the most data – can dominate. And mobile has grown internet usage while simultaneously increasing the amount of time we spend online, making this the pre-eminent channel for customer engagement and extending the rewards to the platforms that succeed.

The machine learning technology will assess the context of a customer enquiry and respond. If the algorithm is unable to identify a suitable response the enquiry will be forwarded to the human support team. In theory this would mean instantaneous relevant responses to customer enquiries. In practice, the actual relevance of the responses may be questionable at least in the early phase. Machine learning algorithms work best when they have ‘learned’ from historical input data. A new bank may not have enough input data from customer enquiries to provide relevant responses.

Supporting business goals while investing in technology that can allow banks to better understand the rich amount of information that is processed on a daily basis to promote a closer, more intimate relationship with customers at scale and this must be a priority more than a smart move.

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