The textbook definition of artificial intelligence refers to any device with a comprehension of its environment that can make choices that maximise the odds of accomplishing a set goal.
In other words, AI describes devices that show cognitive functioning like learning, reasoning, planning and problem-solving, much like the capabilities of the human mind. Thinking of it as a ‘mechanical brain’ wouldn’t be far off.
As AI advances, its scope changes dynamically over time to accommodate rapid progress. Things that were once considered AI have become so commonplace that they no longer fall under the definition.
Machine learning (ML) is a subset of and the driving force behind AI’s advancement in recent years.
ML is a device’s capacity for compound learning, based on algorithms that improve automatically through experience.
Whereas AI describes feeding machines data on what they need to know to carry out tasks, ML teaches a computer how to learn for itself.
If you could accurately predict your customers’ wants and needs, develop a marketing strategy designed to get the best results, and create and deliver content based on consumer trends, would you?
Of course, who wouldn’t?
Sure, you can get customer feedback through focus groups and collecting data from customer surveys, but this method barely keeps you abreast of the curve.
What if you could predict your customers’ wants even before they realised it themselves? ML can decipher underlying patterns within patterns and use them to understand and analyse a customer’s intent and predict their purchases.
In addition to foretelling consumer trends, AI can offer several advantages to SMBs and Enterprises:
AI can be a powerful tool in e-commerce to create a hyper-personalised user experience for customers by providing companies with access to large scale and consistent data analytics.
Netflix and Spotify are excellent examples of companies deploying AI-related solutions to benefit the customer experience. Both companies are famous for their streaming recommendations based on user listening and viewing histories.
This application of ML is a proven way to successfully maintain customer retention by keeping users engaged through a customised experience.
Its ML-driven customised user experience is used right down to the thumbnail. Netflix adjusts the way movie recommendations are presented to the viewer through the thumbnail to maximise viewership and subscription loyalty.
This detail may seem minuscule, but a study conducted by Netflix shows that the artistic content (actors shown, filters, artwork, etc.) in the thumbnail is the most significant influence on a user’s decision on what they watch. This could be the difference between a user spending weeks’ worth of time watching an eight-season series or losing interest and searching for entertainment through competitors like HBO and Hulu.
Spotify is the most prominent on-demand music service in the world, with more than 100 million users.
Its feature, Discover Weekly, is yet another product of an ML application to enhance user experience. Every week a personalised playlist of never before heard songs are generated for each user based on their listening history.
It’s like having a good friend create a mixtape just for you.
Heightening the customer experience is not the only thing companies are using ML applications for.
Amazon enlists the help of data science to extrapolate information from buyer trends and locations to provide intuition into the most convenient places to stock items. This application of deep learning helps Amazon to optimise delivery and maintain smooth warehouse operation.
Ever wondered how Amazon Prime maintains its one-day shipping policy at such a massive scale? Its operation teams leverage AI to automate multiple processes in each of their fulfilment centres and continuously cultivate its technology in real-time to determine which orders should be processed together to serve time specs.
Customer surveys and focus groups meant to engage with consumers on a deeper level prove to be a challenge because these approaches only offer one-way communication.
Many AI platforms gather customer insights which empower companies to predict what customers intend to buy and then present customers with the perfect suggestion when the time is right.
Cross-selling is a business practice used to sell additional products or services to an existing customer at the point of sale. This technique both increases consumer spending and nurtures the customer relationship with the company.
Using elements like the purchasing trends of customers who have bought the same item and cross-referencing data from customers with similar purchasing histories, companies can show the buyer other products they are expected to like.
Some companies use both techniques hand in hand to maximise profits.
Fast food companies, in particular, have mastered both practices of cross-selling and up-selling. Think about when you go to McDonald’s, and the cashier asks you if you would like to add fries or other sides to your meal. This is cross-selling.
In e-commerce, ML collects customer data such as age, location, gender, buyer history and even marital status to offer a personalised recommendation. This gives companies the advantage of predicting what the customer wants before they do and presenting it at the right moment using automation software.
Amazon’s “Frequently bought together:” and “Customers who bought this item also bought:” are perfect examples of up-selling and cross-selling used respectively.
Like many other e-services, Netflix offers customers a choice from three subscription plans with the ability to upgrade or downgrade at any time: Basic, Standard and Premium.
The difference in price remains reasonable as the plans get more attractive. The basic plan allows users to video stream from one screen at a time; however, the premium plan is available in HD and Ultra HD, and users can stream from up to 4 screens simultaneously.
Using these techniques are beneficial to not only large enterprises. SMB’s stand to benefit from upsells and cross-selling just as much.
The fastest-growing companies rely more on upsells for revenue than from the acquisition of new customers.
In recent years, the use of chatbots has become quite popular in automating customer relations.
As a business owner, you might wonder if your business needs a chatbot and the answer is yes.
Big names like Taco Bell and Sephora are reaping the benefits of using chatbots to manage customer interactions, and you can be sure that your competition is already getting on board.
An estimated 80% of businesses employ a chatbot to deepen customer relationships through simulated conversations. Within the next two years, this number is expected to rise to accommodate 90%  of all customer support requests.
Despite negative misconceptions, chatbots will not replace people. It’s best to consider chatbots as time-saving assistants to streamline customers’ flow based on their queries to the relevant representatives for optimum effectiveness and efficiency.
Due to its ML capabilities, creating a learning cycle for a chatbot fosters continuous improvement and optimisation.
It’s clear that deploying AI technology and using ML applications is beneficial to companies of any size. Applying ML is a powerful way to boost sales at less risk to costs and promote customer loyalty through hyper-personalised experiences.
Maximising business benefits by leveraging ML algorithms, companies can use customer insights to sharpen their predictions of buyer forecasts and be prepared to offer their customers the best experience.
 Balas, V. E., Kumar, R., & Srivastava, R. (2020). Recent Trends and Advances in Artificial Intelligence and Internet of Things. Cham, Switzerland: Springer.