How Global Brands create Insanely Great Customer Experiences

They’re in the unique position to provide context and offer a broad direction in which the data scientists should begin their exploration.

Exploration Begins With Supervised Learning

Predicting customer behaviors depends heavily on machine learning algorithms used for “supervised learning” models. We “train” these models by feeding them data that contains known historical outcomes. This is nearly the opposite of “unsupervised learning” models in which a set of data are unlabeled and without context.

For example, a supervised learning model to predict customer drinking behaviors would require historical contextual data about where customers drink (e.g. in a donut shop, at a New Year’s party, in a football stadium) and the actual drinks they consume (e.g. coffee, beer, milk, champagne, soda, etc.).

That’s because customer behavior is highly contextual. Customers in a donut shop eating a hot donut, for example, are more likely to buy milk or coffee to go with it than a light beer. Revelers in Times Square on New Year’s Eve are more likely to be holding a plastic cup of champagne than a vegetable smoothie.

Sharing is Caring

When brands share contextual data on the “state of the customer” with their customer service partner, the possibilities for revolutionizing the customer experience are boundless. Imagine, for example, customer engagement experts being able to anticipate which incoming calls are frustrated customers and knowing the most likely reason why they’re calling. Immediately after answering a call, the engagement experts could state the most likely issue causing frustration and ask callers if that’s why they were calling — that’s white glove service that would impress most people. From a data analytics perspective, this is a similar approach to how online retailers provide product and service purchasing recommendations to customers surfing their websites.

What’s most exciting in the CX space is the emerging technologies that didn’t exist just a few short years ago. For example, Alorica recently deployed robots that automated repetitive order fulfillment and billing tasks for a client, freeing up our engagement experts to focus on more complex assignments for the client. Within our talent acquisition department, we now use chatbots to streamline the recruiting process to facilitate faster staffing ramps to serve clients in highly seasonal industries. Another technology solution our clients are using is AI to match the personality profiles of incoming callers with customer engagement experts best able to help, improving customer satisfaction and loyalty while reducing costs.

However, one of the most innovative and promising technology solutions is advanced speech analytics. We can now more deeply analyze the recordings of incoming client calls on a vast scale that’s not possible with manual processes or random surveys. In near-real time, the company can share critical insights with clients, including emerging customer topics, the root causes of complaints, which operational processes need to be fixed and a variety of other observations.

It Begins and Ends With Partnership

From supervised learned models to sharing contextual customer data, partnership is the key for any global brand working with its customer service provider to elevate their customer experiences. Match that partnership mindset with a provider that has a vast network of customer engagement experts (think 100,000!) across multiple continents who are empowered by the best technology and you have a formula for growing in today’s competitive global marketplace.