We love it when Amazon recommends us other things we can purchase based on what we’ve already put in our carts. How does Amazon know what we are looking for? That’s a recommendation engine, a great marketing tool that uses Machine Learning.
A store manager for luxury luggage brand Tumi tried a new product recommendation tool that showed what one of his regular customers was most likely to buy. The tool suggested all women’s products. Having worked with the customer for years, the store manager assumed the recommendation tool was wrong. The customer, so far, had only purchased men’s accessories. The manager decided to call the customer to find if he was currently shopping for something specific, and was surprised to discover that the customer was indeed looking for a gift for his wife.
“We knew recent browser behaviour, email open rates, search behaviour — it’s far more predictive than past purchase,” Charlie Cole, Tumi’s chief digital officer, explained, adding, “What I bought yesterday isn’t always going to predict what I buy today. To get to that layer, you have to combine a customer’s purchase history with his browser behaviour and email open rates. That’s what AI can do.”
Data Science, AI, and Machine Learning (ML) are powerful tools in the hands of marketers. These allow marketers to accurately research a consumer and then create a suitable branding strategy made of engaging content that the audience wants to see. ML and data analytics have made lives easy for marketers as well as consumers.
An Orbis Research study titled, “Machine Learning Market” claims that the ML market is expected to grow to USD 39.98 billion by 2025.
We know that AI and ML aren’t the same thing. While AI brings forth the idea that a machine or a computer can complete a task that usually requires human intelligence, ML automates model building for data analysis.
Machine Learning can be defined as the branch of AI that allows a computer to learn from the data it analyses by identifying patterns and then to make decisions without human interference. It will enable marketers to gain valuable information about their customers.
How Can Machine Learning Help Companies
You can apply ML to your strategy in various ways. These include:
1. Customer Segmentation
Machine Learning can help in creating segments of similar customer types based on a customer’s social media behaviour. This gives marketers an idea of where the customer’s interests lie. For example, you can get segmented groups of customers who are food fans, Netflix watchers, travel geeks, and so on.
2. Content Optimization
Previously A/B testing was considered an effective way to measure what content was working vs what was not. However, in this method, you would spend considerable time and money on the test that wasn’t working. With Machine Learning’s bandit tests, this loss of time and effort minimises drastically.
Machines are programmed to optimise content, explore options, and upgrade communication to give better results. For example, when you use Google Maps, the machine learns about your usual locations and routes and will automatically update this information to provide you with suggestions in the future proactively.
Also Read: 5 Ways You Can Use Chatbots in Marketing
3. Sales Forecasting & Pricing
Machine Learning can help marketers predict the numerical values based on previous data, thus enabling them to enhance the customer experience with the right prices. ML can also predict the sales volumes based on this pricing. Mobile companies utilise ML techniques every time they launch a new product in the market.
4. Text Classification and Personalisation
Natural language processing (NLP) under machine learning helps in classifying content by tone, sentiments, topics and thereby gaining user insights. These insights then enable marketers to give personalised information.
5. Voice-based Search
Personal virtual assistants like Siri, Alexa, S-voice, and so on are a result of advanced ML systems. These are highly intelligent systems that adapt to a customer’s voice, their search patterns and also enable touch-free shopping. Introducing a voice-based experience for the customer with your brand is a fool-proof marketing strategy as of today.
The list is endless when it comes to using Machine Learning for your brand. Beyond the few listed above, marketers can use ML to automate processes like reading emails, opening and analysing email attachments, data entry for standard template reports, tracking/engaging social media triggers, and so on.
Also Read: Is Artificial Intelligence Creating a New Mobile Marketing Wave in the Middle East?
According to a study by Quantcast and Forbes Insights,
- More than 500 marketers, 52 per cent of respondents said that they had seen an increase in sales, and 51% said they had seen an increase in customer retention since introducing AI & ML capabilities.
- As a result of this, brands are showing an increased interest in further investment. 53% of marketers plan to make a 25 to 49% increase in AI spend in the next three years, while 17 per cent promise a boost of between 50 to 74%.
- 80 per cent said AI technology would enable them to focus more time on strategy and less on day-to-day tasks, 58 per cent said it would help them to refine the online customer experience, and 55 per cent said it would help them generate personalised messaging.
Machine Learning is changing the way leaders can operate. Knowing how to use it to your advantage can be a valuable tool for your business.
