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As competition heats up and personalised service becomes a must-have, contact centres are leading the charge. Forming solid strategies that powerfully combine artificial intelligence (AI) and cloud, they’re creating more memorable, efficient, and satisfying customer experiences.
This is what Genesys and Google customers like E.ON UK PLC are seeing. The trick is to shape and refine journeys while engaging customers to drive the process.
Things to think about
Traditional automation thinking becomes redundant when designing AI-enabled journeys. Not least because complex IVR call flows, lengthy option menus, and getting customers to repeatedly push buttons on phones do little for growing satisfaction and retention. Indeed, we’re trying to engineer out such friction points, rather than emulate them. Plus, here we’re talking about automating not just voice but other channels as well.
Which is why maximising conversational AI requires a fundamental shift in mindset. Once you’ve worked out where to focus first it’s about starting with short sprints. Then rapidly experimenting and adjusting as you go. Finally, scaling up and extending conversational AI across more customer channels and touchpoints.
Blueprint for success
One company that’s mastered this approach is E.ON UK PLC in the UK. Devoid of chat and social automation, the energy provider relied heavily on five legacy IVR systems using basic speech recognition and touch-tone technology to combat rising call volumes.
The switch last year to Genesys Multicloud CX (formally Genesys Engage) quickly boosted self-service options for phone customers. E.ON UK PLC also gained new features like AI-powered routing along with deeper insight into agent workflows and KPIs. Importantly, they benefited from out-of-the-box integration, too, with Google Cloud AI and machine learning solutions.
In total 37 omnichannel journeys – encompassing voice, chat, social media, and text channels – were identified as prime for automation. That target was hit within 10 weeks, helped by easy-to-pick-up Google tools which the company’s analysts quickly got comfortable with.
They started small, quickly prototyping and testing bots in short daily bursts. For example, to capture customer intent for frequent requests like checking gas and electric bills or submitting meter readings.
The introduction of natural language processing (NLP) routing dramatically reduced the need for transfers as more contacts landed with the right agent, first time. Likewise, the contact centre saw a huge boost in speech recognition success, for not much DevOps effort.
What good looks like
E.ON UK PLC realised large business value at relatively small cost. And the numbers make impressive reading. Just upgrading alone from their old automatic speech recognition system to Google Speech services (speech-to-text and text-to-speech) delivered a net positive return on the initial investment within three weeks.
That’s the point at which business benefits overtook incremental costs. And it presented the company’s finance team with an easy decision to approve the business case for the next phase of AI implementation. Moreover, that cost benefit ratio increased from 1:1.2 to 1:4.7 after six months. In other words, for every single pound the company spent they received £4.70 in benefits.
Further analysis over three years confirmed that:
And that’s not all. E.ON UK PLC has effectively futureproofed its contact centre and AI strategy with Genesys and Google. They can lift and shift AI automation to other parts of the business outside the UK with no extra coding effort. And, with a pay-as-you-go license model, they can scale rapidly from 10,000 to 100,000 calls for not much extra cost.
Executing and getting started
Companies like E.ON UK PLC and others that are getting the best results from AI and cloud tend to follow a similar approach. They start by with targeted bot implementations to inbound voice and chat queues. Then carry out A/B testing to measure the effect, not just on efficiency KPIs but also on the overall customer and agent experience. For example, by looking at shifts in CSAT and NPS ratings plus factors like ease of effort and quality of interaction for the agent.
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