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4 Real-Life Examples of Using Machine Learning and Artificial Intelligence to Revolutionise Your Customer Experience

4 Real-Life Examples of Using Machine Learning and Artificial Intelligence to Revolutionise Your Customer Experience

Your website, your app, your call centres, your staff...these are all a means to an end.

That end is a killer customer experience.

One where the customer gets what they need, quickly and without frustration. Your entire tech stack serves only the function of hitting that goal!

But how do you maintain that killer customer experience when your staff need to work remotely? When your website is under more strain than ever before?

There are only two options:

  1. Work harder: more staff. More capacity. More!
  2. Work smarter: leverage the public cloud to relieve some of the burden

In this blog I’ll explore - via real-life case studies - how Contino has used machine learning and artificial intelligence in the public cloud to help our customers revolutionise their customer experience.

Real-Life Case Studies

Let’s get down to some practical examples of what we’re talking about.

#1 Amazon Connect and Cognitive CX in the Public Cloud

At Contino, we are helping customers to deploy cloud-hosted contact centres like Amazon Connect, to protect and boost customer experience in times of unprecedented customer demand.

(For an in-depth examination, check out our recent webinar: Virtual Contact Centres in the Cloud!)

Amazon Connect is a fully operational, omnichannel contact centre that can be accessed from virtually anywhere. We have recently set this up for a leading utilities company to demonstrate how it can better integrate their existing customer data to deliver a seamless customer experience.

Amazon Connect is at the heart of Amazon’s Cognitive CX Portfolio. The services in this portfolio all integrate with Amazon Connect in a number of ways to deliver “a data-driven, AI-enabled CX capability where customer service is intuitive, and machine learning delivers improved outcomes for customers through predictive insights.”

Cognitive CX Case Study at Major Utility Company

Contino recently demonstrated how these services can work together at a major utilities company that was struggling to make the most of their data.

The company’s vision to utilise data to immediately inform and streamline customer service operations was hampered by their existing call centre platform. It was difficult to integrate the real-time data from customers into the contact flow, resulting in customers having to explain their situation to an agent and missing the opportunity to use that information to automatically meet the customer’s needs.

In addition, the call log data was only available by periodic file transfer with no API or event-driven data available, frustrating the vision of a real-time single view of customer.

In a bid to highlight the potential data-driven benefits of Amazon Connect, Contino set up Amazon Connect along with the main capabilities of IVR, contact flows, CSA hierarchy and routing profiles.

Beyond this, integration to data resources, handling of requests via chatbots (Amazon Lex) and Natural Language Processing services and outbound calls were all quickly proven.

Let’s take a closer look at how Amazon Connect can handle call flows with the help of other Cognitive CX services.

When a customer calls into the call centre, rather than putting them into a queue to be handled by an agent, you can have that call answered by Lex, Amazon’s chatbot service – for example, the customer might be prompted at this point to press one to submit a metre reading.

Lex works closely together with natural language processes services such as Amazon Polly (text-to-speech) and Amazon Transcribe (speech-to-text). In this example, when the customer is asked if they would like to submit a metre reading, this is using Polly.

If the customer answers yes, they are then asked to confirm the last three digits of their phone number – this step uses Amazon Lambda to access the customer’s data.

There are two key benefits to all of this integration:

  1. The high degree of automation works to deliver a smooth customer interaction and removes the need to speak to another human. This is good for the business since it lowers cost to serve and it’s good for the customer too – they can call up out of hours and get served quickly and easily.
  2. The data and knowledge that you already have on each customer can be leveraged to personalise and enrich the customer experience. 

#2 Chatbots: Enabling Customer Self-Service at Speed and Scale with a Cloud-Native Chatbot for a Leading UK Bank

Contino built a scalable cloud-native platform on Azure, on which we enabled a chatbot

to serve 8 million mobile banking customers.

The platform was built using Azure Kubernetes Service (AKS) to allow the bank to quickly onboard engineers and develop new applications and products.

Immediately Relieves Pressure

Using sentiment analysis, the chatbot directs users to the appropriate department

so they can get answers faster and easier than before.

This reduces pressure on the call centres, resulting in significant time and cost savings and demonstrating the value of cloud-native.

  • 616,750 chatbot conversations to date
  • 14,000 conversations per day 
  • 33% faster customer response time

How the Chatbot has Scaled to Meet the Surge in Demand

In light of current events, the chatbot has proved critical in coping with a surge in customer demand. 

Being cloud-native, the chatbot has automatically scaled to handle an 180% increase in traffic.

Almost 50% of customer queries are resolved with the chatbot, massively relieving pressure on the bank’s call centres and freeing up time to respond to more urgent customer needs. 

#3 Automated, User-Driven Knowledge Base at a Major Utility Company

Contino built a customer-driven knowledge base to help guide the client’s customer support content to reduce call centre volumes and close any gaps in their support content.

The service automatically harvested key customer questions from mobile search queries, page hits and customer service calls. The resultant data was fed into content writers who could respond by creating content to meet real customer needs.

A highly-efficient content management system hosted on AWS used GitHub to push new FAQ content live in seconds.

New content that meets real customer needs can be surfaced, created and published quickly and easily.

#4 Real-Time, Personalised, Data-Driven: Analytics at a Transport Company for a Global City

A transport company that manages transport in a global city came to us to help them create the next evolution of public transport: real-time, personalised and data-driven.

Contino has worked with them to build a highly-scalable, fully-automated, cloud-native and serverless data analytics platform on AWS.

The platform ingests data feeds from the city’s contactless fare collection system in near real-time while aggregating additional data sources to provide actionable insights on passenger capacity, flow and possible interruptions.

Tableau and web dashboards are used at the front-end to provide customisable and automatable reporting capabilities to enable end-users to segment, analyse and visualise the data across the network to gain fresh insights.

The platform incorporates AWS Sagemaker, a machine learning service, to provide powerful predictive capabilities based on past data. The organisation can now predict how variations in weather will impact transport usage and patronage across each mode of transport, and the entire transport network.

It also provides visibility of activity across the whole public transport network in near-real-time.

The data allows the organisation to gain a transparent view on:

  • How many people are on the network, and on each mode of transport
  • How many trips are planned
  • Taps and boardings in real-time as well as over time, broken down at location level
  • Intermodal transfer percentages broken down by mode and location level
  • Predictive patronage numbers up to 48 hours ahead
  • Usage spikes in particular locations real-time delays and tap-on reversals

Operations staff and the leadership team now have access to the data they need to:

  • Predict and react quickly to fluctuations in patronage, weather, delays etc.
  • Access and deliver insights quickly on what is happening now across all modes of transport, including the newly finalized Metro line
  • Drive new initiatives using the Near Real Time Warehouse by incorporating new data sets 
  • Allow data to be analysed and shared in real time across the whole enterprise, enabling other business units to improve their process and agility

A More Detailed Look at the Platform

A highly-scalable, fully-automated, cloud-native data analytics platform on AWS. It features sophisticated data analysis and machine learning with AWS Sagemaker and a customisable, near real-time data dashboard.

The new platform provides visibility of activity across the company’s whole transport network in near-real-time, enables predictive responses to delays, bad weather and public events as well as personalised customer engagement.


These are just a few examples of the myriad ways that machine learning can be deployed to help create a killer customer experience.

Under difficult conditions - with large numbers of staff working remotely and customers at arm's length - NOW is the time for digital transformation. To leverage the power of the cloud to sustain your business. Good luck!

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