Artificial Intelligence (AI) can help improve efficiencies in a variety of ways and replicates what humans can do to provide a better and more personalised customer experience. This article explains how AI improves customer experience.
Artificial Intelligence evolves daily and is now part of everyday life. From predicting weather forecasts, to filtering email spam, to responding to voice commands.
Artificial Intelligence replaces humans in doing repetitive tasks and frees up the workforce to focus on other tasks humans are better equipped for.
As mentioned by “The RIQ News Desk” https://www.readitquik.com/articles/data/how-artificial-intelligence-has-evolved/
“In reality, AI is everywhere, influencing most of our decisions. Our smartphone-led personal assistant is using machine intelligence to recommend the best traffic route to us.”
AI seamlessly understands the customer and their changing behaviour and then analyses their preferences. AI based personalisation aims to provide better customer experience as the information presented is personalised to them. AI has enabled us to push boundaries and has allowed to create Hyper-Personalisation, AKA personalisation 2.0.
What is Hyper-Personalisation?
As suggested by Todd Lebo in his study:
“Hyper-personalization leverages artificial intelligence (AI) and real-time data to deliver more relevant content, product, and service information to each user.”
Improving customer experience has become the top priority for organisations across the globe. As the results from research competed by ascend2 shows below:
Source : https://ascend2.com/
Ascend2 also suggests in their report looking into leadership perspectives, that “…the majority of marketing leaders surveyed (87%) consider hyper-personalization an effective strategy for achieving top priorities, with 38% deeming it a best-in-class tactic and another 49% identifying it as somewhat successful.”
Many organisations are at their initial steps of adopting hyper-personalisation. As research has proven, AI ensures a better customer experience for the target prospects, improves engagement and accelerates the sales process.
Use of AI for Hyper-Personalisation
This brisk diffusion of AI and machine learning technologies has provided hyper-personalisation to give birth to various technological breakthroughs. In this blog we are going to focus on two key areas of hyper personalisation which includes recommendations and content curation.
Recommendation engines are increasingly being developed to increase engagement and interaction with customers. Today all platforms provide recommendations to their customers by analysing the data within the system. This includes customer profile, history of transactions, customer preferences, previous enquiries and their social media presence.
These recommendation engines use machine learning and artificial intelligence in the background, combined with a set of algorithms to produce the result. Also AI continuously monitors, processes and learns the choices made by the customer so that it can automatically predict future recommendations.
Based on our research in AI, here are some of real-world challenges that AI has solved:
1. How can retail ecommerce platforms provide context sensitive products and offers to customers?
Firstly AI analyses customer behaviour including historic interactions and preferences to provide a set of recommendations. It is key that AI provides more relevant and context sensitive products and offers for the customers.
Secondly, machine learning uses prediction models after analysing customers of similar behaviour and then presents relevant products for the customer.
2. How do tourist operators provide personalised booking experiences to customers and increase customer satisfaction and sales?
Booking platforms have again used recommendation engines to help them solve this this problem. AI has utilised Recurrent Neural Network (RNN) and deep learning algorithms to analyse the sequence of locations in customer’s itinerary history in order to recommend their next destination.
Another key aspect of booking platforms was to provide recommendations for customer accommodations. AI with Natural Language Processing (NPL) were used within recommendation engine so at to understand customer enquiry and then provide the right type of accommodation. A sample enquiry could be “private home with a swimming pool and fully equipped kitchen”
3. How restaurants improved customer dining experience:
Gone are the days where restaurants provide a standard menu for their diners. Restaurants introduced self-service with diners ordering from the kiosk upon entry or at table using touch screens and voice ordering. This requires a bot with speech recognition functionality.
Also, AI has helped in personalising the dining experience by introducing personalised menus. These menus are automatically generated by analysing customers’ previous orders, current weather condition, location of the restaurant and taste profiles. For example, a customer always orders a “chicken burger, chips and cold coffee” on a sunny Friday in July and August” so the recommendation engine provides these at the top of the menu making it easy for the customer to quickly order.
“In March, McDonald’s announced it is buying Israeli start-up Dynamic Yield — which develops AI-driven personalized menus for customers — for $300 million. In May, Mastercard and Zivelo, a self-service kiosk technology firm, announced their AI-powered voice assistant and personalized menu systems. “ Source: ozy.com
2. Content Curation
The next biggest technological breakthrough with artificial intelligence and machine learning has been for content curation. Even though content curation has been in play from the early days of 20th century, it has always evolved with the introduction of new platforms, new algorithms and is continuously improving in order to provide personalised content to users.
Based on our research, here are some of focus areas where AI has helped in solving the challenges of content curation:
3. Personalised messaging
Using personal profiles, organisations are now able to provide more personalised and targeted messages to their users. Let us look at couple of examples below.
Using personalised messaging to help home buyers kickstart their house move journey:
Information shared about new home buyers is used to send personalised messages from packaging / removal companies, utility suppliers, insurance companies etc. This has helped the home buyers to quickly reach out to companies who can help in their overwhelming house move journey.
4. Email content curation
Marketing teams spend a lot of their time building and understanding segments, so they can send emails to their marketing lists that are more personalised at a segmented level. Now content curation engines use naive bayes algorithm and decision trees to provide more personalised content at the customer level.
Car dealerships can send the relevant content and to engage prospects:
Car dealers uses their prospect information to provide personalised emails depending on the prospect profile such as new car launches, events, personalised video, user directed storytelling etc.
5. Content based and collaborative filtering
As organisations start producing new content every day it is very difficult for the customer to keep-up, content-based and collaborative filtering allows customers get to see only filtered content which is more relevant for them. These filters can be fine-tuned to be either user-based, or item-based.
How Netflix feeds the right content to its customers to keep customers engaged with their platform:
Netflix uses content based and collaborative filtering combined to provide more personalised recommendations for customers to give recommendations as to what movies series to watch.
“The presence of AI in today’s society is becoming more and more ubiquitous— particularly as large companies like Netflix, Amazon, Facebook, Spotify, and many more continually deploy AI-related solutions that directly interact (often behind the scenes) with consumers every day.” Source: becominghuman.ai
AI is increasingly being used to provide a better customer experience. Artificial intelligence uses various ground-breaking algorithms, machine learning practices, and natural language processing techniques to help organisations improve their customer experience. Without a doubt, using strong AI for your customer experience will allow you to provide a great customer experience and improve customer satisfaction.
Ashine Consultancy Solutions provides industry-leading advice on Customer Experience Strategy. For bespoke Customer Portal solutions, data management solutions, business process automation, custom software development, website development, mobile app development and other bespoke software solutions to improve your customer experience (CX), get in touch for a free consultation.