Embedding Intelligence to Win in the Experience Economy

It should come as no surprise that the advancement of technology has changed the way we live our lives today. We are becoming more digital savvy in the way that we interact with others, consume content and purchase products.

In this modern era of continuous technological improvement and instant gratification, keeping up with customer demands is proving increasingly challenging for everyday businesses. Customers now expect easy ordering, faster delivery times and a personalised level of service. Evidently, in the last few months, we have seen the closure of large retailers in Australia who have failed to meet customer needs and expectations.

Customer experience is becoming more important as customers are not just looking to buy the products alone but rather the experience around them. The airline industry perfectly illustrates this point, while the mode of transportation is the same, what airlines primarily compete on is customer experience. Emirates, one of the highest ranked airlines clearly understands the value of customer experience.

As well as being the first to install TV screens on every seat on every aircraft, they offer a number of small customer perks that really add up. They offer great flexilibilty, with online flight check-ins opening 2 days in advance and convenience, as customers travelling across all classes can check in their luggage from anywhere in Dubai.

So, how can we bring an exceptional customer experience to our brand and products?
To answer this, we need to first better understand what our customers want and why they want them. There are two main approaches to better understand your consumer needs. One is to look at the myriad of Operational data (O-data) the businesses have collected.

This operational data may include past transactions, customer demographics, purchasing behaviour, and many more. Many organisations are now analysing and leveraging customer data to make informed business decisions. Many have also gone a step further by setting up a data division headed by a Chief Data Officer (CDO) whose role is evolving.

The other approach is to directly ask customers for feedback whether it be through customer surveys, interviews, focus groups, etc. The collected data, also known as the Experience data (X-data), helps businesses to understand customer sentiment and values. By combining the O and X data, it provides powerful knowledge to the businesses to understand what their customers really want and why they want it.

Many businesses are becoming aware of the benefits that O+X can bring and so they have started their eXperience Management (XM) journey. For example, through Qantas’s Voice of Customer (VoC) program, it has introduced new innovative products such as wifi on board, project sunrise, world’s first waste free flight, etc.

Qantas has just brought all disparate platforms and processes across its business units in a single Qualtrics platform. Moreover, Qantas brought together their experience and operational data to help them better understand how its customers interact with the check-in systems and to personalise the experience offered. There are more examples on how XM enables businesses to innovate.

Now that we have both O+X data, how can we realise its benefits?
The key here is to understand what’s possible with this data and how to effectively process and turn raw data into actionable insights that support business processes. Machine Learning and Artificial Intelligence (AI) are powerful tools that allow you to mass crunch large datasets in a short period of time.

Below are common use cases across various industries, where companies have been able to leverage their O+X data to gain a competitive advantage.

Text Analytics
It is quite common nowadays for organisations to employ machine learning techniques to analyse qualitative data such as customer surveys, product feedback, and social media. This Experience data enables organisations to discover core customer values and needs. The feedback is even more effective when we analyse them against demographics data.

For example, if a shoe company knows that a demographic group places a high emphasis on comfort. Then they would focus more on the quality of the materials rather than creating stylish outward appearance for such group. A better understanding on your target market can directly translate into tangible financial rewards like increase in sales and revenue. Additionally, it can improve intangible aspects of an organisation such as reputation and customer satisfaction.

Predicting and Understanding Customer Behaviour
Operational data from customer transactions enable organisations to understand buying patterns of their customers. By applying intelligence technologies on top of O+X data, organisations can predict the future buying behaviour of their customers like which new products will appeal to particular demographics. Early and accurate prediction allows organisations to be at the forefront of trends and take advantage of them.

Personalisation
Personalising customer experience includes offering products and services tailored to customer preferences. Many online platforms such as Amazon, Netflix and Marriott International employ machine learning to offer personalised recommendations based on customer past buying patterns. Furthermore, we also see personalisation embedded in every aspect of the customer journey.

For example, a chatbot that does not only answer customer queries but has an intelligence to combine Operational and Experience data to provide a unique customer experience. It combines and stores your past information, such as past product complaints and purchases, brand preferences, product enquires and which staff served you. Equipped with this information, it can tailor its responses to resolve your current issue quicker and more hassle-free.

Augmenting Shopping Experience
Artificial Intelligence is now becoming ubiquitous in a retail environment to improve customer satisfaction. A number of retailers have started looking into implementing Smart Mirrors in their stores. A smart Mirror uses computer vision, touch and recommendation technologies to augment customer buying experience, for example, the option of trying on clothes digitally.

The mirror can automatically detect customer sentiment on the digital clothes they try and collect this Experience data. If combined with Operational data such as customer past transactions, the smart mirror can recommend products that customers are likely to purchase, thus, creating a seamless, easy shopping experience.

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