Putting a Rocket Booster up your NPS with AI and Cloud

Guest Blog by Alan Logan, Head of Customer Experience at IT consultancy ECS

Putting a Rocket Booster up your NPS with AI and Cloud

Led Zeppelin might not be the first thing that comes to mind when talking about contact centre transformation. But their Dazed and Confused anthem is a fitting description of how many of us feel after a few rounds with contact centre agents. Something that should be a welcoming front door into a business can often seem more like a frustrating maze.

Making the customer experience quicker and frictionless to improve the net promoter score (NPS) is a particular challenge for large organisations. Because they are weighed down by ageing contact centre technology and valuable customer data is stuck in multiple silos, contact centre agents have no hope of a 360-degree customer view making it pretty much impossible to deliver a consistently good experience.

Alan Logan

Alan Logan

The problem is exacerbated by consumers judging every business against the high bar set by a handful of firms with top-notch customer service.

But the good news is that putting a rocket booster up your NPS is not as difficult as you might think.

Your contact centre has two strengths: it is customer-centric and rich in data. This makes it a strategic weapon in today’s fast, hyper-connected world. Moving all of this siloed data to a cloud-based contact centre sporting AI-based analytics results in a single source of truth that can be used to great effect to enhance the customer experience.

The trick is to embrace a data-centric culture and aggregate and analyse the data generated by your contact centre to boost customer experience – not just in their interactions with your contact centres but across all of your digital channels.

For example, insights gleaned from behavioural analytics can help you understand customer needs. You can predict why the customer is getting in touch and be proactive in meeting their needs efficiently.

But new tools make it possible to go further and faster.

Perhaps surprisingly, some of the most highly regulated businesses in financial services are leading the charge. A number of retail banks are building AI-enabled contact centres in the cloud using platforms such as Amazon Connect, based on the same technology Amazon uses in its own contact centres.

Blending the human touch with AI and other automation tools has been shown to significantly reduce customer resolution times. By refining this process over time using insights from the analytics, you can find the optimal mix for your business.

Initial results are impressive.

One European bank that moved to a cloud-based contact centre platform has halved its customer incident resolution response times, and slashed the cost of managing customer information by almost 60%. It uses a analytics and automation technologies to unify previously siloed digital and mobile data and giving its managers near real-time performance analysis.

And a top South African bank that introduced a virtual call centre solution that understands the context of clients’ questions is now able to answer pre-programmed questions at just 10% of the cost of live agents. Customers with a simple query now have a highly efficient service, and it also frees up call centre staff to handle more complex requests.

If you are looking to move your contact centre into the cloud, here are suggestions on incorporating AI and machine learning techniques:

Responding to routine customer questions

Embedding AI in virtual customer assistants (VCAs) and chatbots is ideal for providing fast responses to frequently asked customer questions such as “How do I return an unwanted online order?” or “What balance is in my account?” Using text-based contextual analytics, these technologies can provide accurate answers and encourage self-service, freeing up agents . It’s important, though, to make it easy for customers to speak to a human agent when required.

Augmenting the human touch

While the percentage of customer engagements handled by agents will go down, the calls they take will be more complex. Customers often complain that agents don’t empathise with their situation, failing to spot and respond accordingly when they’re angry or upset. AI tools can be used to scan customers’ interactions for context, sentiment and emotion before they speak to an agent. This way, agents are presented with background on the customer’s query and suggested personalised, empathetic responses as they take the call. This allows agents to establish an emotional connection with the customer, which can resolve issues before they escalate.

Speech analytics

Integrating intelligent conversational bots into contact flows to turn automated interactions into natural conversations is another great use of AI. With speech analytics you can transcribe calls and show caller sentiment in real-time. By aggregating and mining this data you can reveal new insights that can feed back into your customer communications.

Upselling and cross-selling

By adding external data and applying machine learning techniques, enterprises can identify trends and uncover the unexpected. This paves the way for personalised customer experiences that are prescriptive and proactive. For example, buildings insurance companies can use blended data to identify flood seasons and locations, and proactively engage existing customers about purchasing flood cover.

Today, simply meeting customers’ expectations is no longer sufficient. Customers stay loyal to the companies that resolve their issues quickly and painlessly. And that means a move away from contact centres that leave customers dazed and confused, and a move towards ones that use analytics, AI and machine learning to keep their customers close – and put a rocket booster up that NPS.

Guest Blog by Alan Logan, Head of Customer Experience at IT consultancy ECS

Alan is Head of Customer Experience at ECS, the UK’s largest independent enterprise-native technology consultancy. His role involves helping large enterprises, including top retail banks, transform their contact centres using Amazon Connect. Prior to joining ECS in 2012, Alan spent over 25 years at The Royal Bank of Scotland, where he held several senior infrastructure technology roles. These included: responsibility for infrastructure delivery for the bank’s divestment programmes; acting head of infrastructure projects; and responsibility for the bank’s technology infrastructure integration of ABN Amro across 26 countries.



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