Remember the time you had to shop alone and you wished you had somebody suggesting what might look good with the cute top you picked? Or the time when you went to that popular café next door and you had to repeat the order of your favourite pizza and cold drink every time, even though you have been a regular customer? Not to mention the time when you wanted to talk to a concerned personnel regarding a specific issue you faced with an account but you were placed on hold for what felt like…hours? 

Well, they were in fact some of the real-world problems that needed solving. Long gone are those days and zipping ahead, our lives have significantly been made more convenient, simpler and faster thanks to the technological advancements over the past few decades.
However, the growing population, their differing tastes, varying characteristics and preferences gave rise to changing expectations. Which meant, obviously – more problems. And more problems meant more (read millions!) data to process. 

The Birth and Rise of a Revolution

Scientists and engineers were visionaries to have seen this coming decades ago. From being a mere concept, their efforts over the years of transferring human intelligence onto machines, popularly known as Artificial Intelligence (AI), have been generating variables and mind-boggling outcomes, otherwise incomprehensible to the human mind.

Fueled by processing this Big Data, Artificial intelligence has taken us from a single human brain to thousands of brains working together in real time to solve innumerous business problems we face today. Machine learning (ML), a subset of AI, uses past data to draw patterns and continuously improvise and evolve on its own, known as unsupervised learning. Together, AI and ML has made its way into various sectors and industries to improve reach, create engagement, enhance customer experience and witness competitive growth to name a few.

Many successful brands realized the scope and breadth of this ground breaking technology quite early in the boom. By using customer data at the granular level, today AI and ML enable you to tap into the interests, emotions, and habits of the consumer. It allows you to observe consumer psychology to understand what drives them and what doesn’t, what influences their behaviour and a lot more.

The Juice behind Modern Marketing

Marketers adopted it to manage and execute full-fledged marketing campaigns by rolling out the right message at the right time and through the right medium to the desired audience, along with having the power to analyze subsequent success or failure of campaigns. And all a marketer needs to know are the problems that need solving, how to analyze the data and to implement creative solutions based on the information processed.
The idea of being able to offer a consumer something even before they knew they wanted it, would have sounded surreal a few years ago. And yet today, it couldn’t be more real.

Let’s have a broad look at the benefits of using AI and ML in marketing and how it is revolutionizing marketing today.

Predictive Analytics

Just as it sounds – feed large amounts of historical customer data and it helps predict the future behaviour and actions of your consumer. It is also used to predict the life time value of your customers, identify customers likely to be more loyal to your brand, the value of a lead, the amount of time and resources a marketer should spend on a lead, sales forecasting and much more.

Nike, a multinational corporation in the sports business, known for its innovation and marketing was one of the firsts to embrace this technology. It arrived at a new idea for realizing sales – letting customers design their own shoes! The vast amount of data collected allows the sports equipment manufacturer deliver personalized experiences and design products for the future.

Could it possibly get any cooler than that?

Clustering and Segmentation

ML makes use of large amounts of data points to identify patterns that allow you to segment your audience based on their interests and purchasing behaviour rather than categorizing simply based on demographic, psychographic and geographic characteristics.

So, if you thought you had a successful business going and yet notice people unsubscribing your service, analyze the subscription data to find out why and substantially reduce the churn with some great campaigns!

Natural Language Processing

As the term suggests, we pretty much ask the computer to understand and recognize human language. It is used for sentiment analysis – to understand how consumers feel about your products, brand and competitors. And this leads us to figuring out what a customer is really feeling at the moment by talking to them and arriving at solutions in real time.

Communications

Speaking of conversations, if you thought you’ve been chatting with a customer executive to help you track your order of the mobile phone you recently purchased, then you’re most likely mistaken. Chatbots are used to deliver round-the-clock customer service and would chat with you to solve a query, just like a human would.

Along with chatbots, digital assistants have forayed into the market after a rise in voice search by users. Amazon Alexa, Microsoft Cortana and Apple Siri are some of the biggies in the market today with optimized content. ‘Olly’ the digital assistant by Emotech takes a few leaps forward by recognizing your facial expressions, your tone and voice. It can move towards you, talk to you, give you suggestions and even play you your favourite track in case you mentioned you’ve had a long day!

Feels like someone’s watching you doesn’t it?

Personalization

Marketers use this algorithm to connect with users one on one through website, emails and other digital platforms and to design various marketing campaigns that target different user personas. It is also aimed at driving traffic back to the website and prompts the customer to take action. Registering on a new website or leaving a purchase halfway would find you receiving emails that welcome you or that notifies you of the abandoned cart.

Coffee giant Starbucks uses personalized marketing messages and recommendations to direct you to the nearby store or to re-order your customized cuppa. And you wondered how they remember! 

Dynamic Pricing

There is no better example than the e-commerce giant Amazon – a pioneer in the use of AI to drive its business. From charging customers based on the items they pick up using the Amazon Go app at check-out free stores, to recommendations on what to buy based on your browsing history, to Alexa at your fingertips – you get served by AI from A to Z.

Marketers use AI for dynamic pricing to achieve sales and revenue. Analysis of data of customer’s past purchases and purchasing patterns helps to increase and reduce prices of products based on the demand and to drive sales. Discounts and coupon codes are very much a part of this game and act as motivators for you to click on the ‘Buy Now’ button.

Now you know why you paid a few extra thousand bucks for those hygiene products.

Recommendation Engines

Widely used to enhance customer experience, engagement and improve retention, the data is used to identify upselling/cross selling opportunities and deliver product and content recommendations.

Major brands such as YouTube, Netflix and Spotify utilize recommendation engines in the form of playlist generators. Social media, job portals, food ordering as well as dating apps use it to recommend content to users.

So the last time that you were shown what other customers bought along with the striped shirt you added to your cart or images of garlic bread, cola and the dip that popped up as the perfect sides to go with your meal, you know who was lurking in the background.

Creating Content

AI has had a breakthrough into creating content as well! By gathering data of customers, their goals and preferences, it has successfully been able to publish industrial reports and in cases such as Persado’s campaign for Dell, it helped create mailers, headlines and advertisements for the multinational computer technology company. However, opinions and blogs like this one are not its forte…yet.

Think this is it? Have another sip!

Using computer vision, marketers use GANS (Generative Adversarial Networks) to create new data instances that resemble your training data to generate realistic images and faces of people even though it does not belong to a real person! Visual Listening is used to uncover insights about a brand and Visual Search, employed by companies such as Pinterest is used for visual product discovery, ruling out the need for tags. Social media giants like Facebook make use of image recognition for photo tagging, ad targeting and to tackle abuse.

There’s More to the Fruit than Meets the Eye…

The touchpoints of AI and Ml seem limitless and its scope unfathomable. Its involvement in optimizing and automating tasks has helped marketers channelize their energies to handle more complex problems and better CTA (call to action). It is increasingly becoming an integral part of marketing – aiding in generating psychographic personas, scaling campaigns and in elevating the overall customer experience.

Although these aspects reduce human errors, one cannot entirely rule out human intervention. Only a human mind using the insights provided by ML can make use of various tactics and develop a connect with the consumer based on feeling. After all, what good is all that data if a message or a campaign cannot be executed without a hint of emotion and creativity?