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Transforming your company’s customer support and service model

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In today’s service-led and continuous engagement model, customer support and service has become the most significant cog in our customer satisfaction engine. It is well documented that two-thirds of customers who switch brands do so because of poor service.

Our customers are living in an experience driven world, they expect an even higher level of differentiated and personalized customer support where they get the outcomes that they were promised.

Details at your finger-tips

Customer service and support is usually a reactive motion; answering a pressing request from a time starved customer. We’ve all been on these calls to hotlines in which we have to provide the same details over and over again whilst being passed between service agents. It’s rarely their fault though that they have to repeatedly ask these questions. 49% of American consumers switched companies last year due to poor customer service (New Voice Media). Security is certainly one element of it but more often the reason is pretty simple – information about the caller or context of the issue is simply not readily available to them when they pick up that call.

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Under a magnifying glass 

Customers are usually judging the level of service on 2 factors: How quickly was my issue addressed and was it addressed in line with my expectations?

The later one is down to your business policies and practices and there isn’t much technology can help you with, but the first one can be addressed and solved through technology. Whilst in the last two decades technology introduced more silos of data, today’s applications are focussing on aggregating these silos instead of creating new ones. Thus, customer service agents now get the support from technology they deserve:

  • Customers details at their finger-tips- Personal details, product details, dates, models, renewal dates, previous call history, etc.
  • Accurately answering or providing information with the help of context aware algorithms.
  • Call times reduced due to efficient answers and less time needed to find information about a customer or an issue. 
  • Proactively suggest solutions tailored to the customer based on suggestions made by an algorithm, which were learned from previous cases and calls.

Like customers, customer support team members are under time pressures, mostly because data is hard to find or solutions that work are hard to identify. 40% of customers want customer service reps to take care of their needs faster. (American Express)

Context aware algorithms that, on one hand are simplifying customer service agents’ day to day job, and on the other hand share their learnings with any other members of the team that will make it possible to achieve this shared goal.

The Rollio AI Effect: Click here for a demo to see Rollio AI's Voice-to-CRM and Workflow Process Automation.

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