Marketing departments across sectors welcome user data to help them make key decisions. But a clear marketing strategy can be hard to define as marketers are increasingly bombarded with disorganized customer information and big data.
That’s where machine learning and AI come in.
AI and machine learning are helping the top minds in marketing as they drive the performance of forward-thinking brands, and target the right kinds of customers.
But what is machine learning exactly? How is it helping marketing departments? And should every brand just jump in, relying on AI to solve their marketing dilemmas?
Machine learning: a marketer’s wingman
Machine learning and artificial intelligence are used to sort through, label, and analyze huge sets of data. Machine learning allows marketers to define specific goals, using models to interpret specific data that can be used to test and measure goals.
When strategic marketing teams understand the benefits of this technology, they are able to work with data scientists and analysts to create algorithms and machine-learning models. This allows them to answer key questions and make the decisions that improve business results.
For example, when a brand wants to connect with the right kinds of customers, they can use machine learning to quickly sort through huge amounts of data. AI looks at the data, ordering information, identifying patterns, and making predictions. This gives human marketers the time and energy to get creative, develop strategies, and produce better content that connects more effectively with their customers.
Machine learning can’t work on it’s own—humans are still an essential part of the marketing process. But the success speaks for itself. Three out of four brands using and machine learning have increased sales by over 10%, and 75% of companies using this technology have been able to increase customer satisfaction.
It’s clear that machine learning can significantly help brands identify and connect with the best customers and improve their business results. So how are they doing it?
Customer segmentation: let’s put them in a box
Customer segmentation allows brands to categorize customers. These categories let marketers engage more effectively with the right people, providing the most appropriate products and ensuring the best customer experience. Machine learning can be used to look at and sort enormous amounts of customer data to target each segment in a meaningful, personalized way.
Case in point: Under Armour
The sports gear brand Under Armour uses an app and machine learning to collect specific data about customers, analyze it, segment customers, and then make personalized product recommendations for each individual. They collect information about customers’ workout behavior, sleep, nutrition, and other health data points. This is fed to the machine learning algorithm, which performs better as it gathers more data. In the end, they’re able to make better fitness recommendations that improves the customer experience and impacts their profitability in turn.
Connecting products with people
Product recommendations are something that many customers have come to expect. Most of the shows people watch on Netflix, for example, are recommendations made by Netflix itself (and its AI-powered recommendation engine of course).
Recommendation gurus: Netflix, Spotify, Amazon
Some of the most ubiquitous brands today including Netflix, Spotify, and Amazon offer customers just what they’re looking for by analyzing customer data, profile information, likes and dislikes, and demographics using AI. The customer experience is improved and the brand is better able to retain clients.
Most likely to buy
Analyzing customer behavior allows brands to create the most appropriate products, offer them to the correct customer, and see who is most likely to follow through with a purchase. They do this through lead scoring, where marketers rank customers based on their value to a company. Identifying and ranking prospects in this ways allow marketers to create the most effective lead-generation strategies.
Connecting the right spark to the right plug: Mazda
Machine-learning algorithms can be used to track websites visited, emails opened, downloads, clicks, and social media activity. For example, the automotive maker Mazda used AI technology to analyze social media activity and identify the people they believed would be most effective at promoting a new product. The AI-identification model found the most artistic active users across various social networks and invited them to test drive the new car. There was only one condition—they had to share their experience with their many followers. Using machine learning, Mazda was able to identify high-value prospects and let them tell their story to more effectively connect with other potential customers.
Getting customers to keep coming back
Just as machine learning can help marketers identify their highest-value customers and prospects, it can also help identify customers who are most likely to leave. By analyzing customer churn—or customers who are most likely to cancel a service or subscription—brands can take preemptive measures to keep customers coming back.
Less dropped calls: telecommunications companies
Machine-learning risk models identify the customers that are most likely to drop a service, and intervention models measure the most effective ways to keep them happy, helping brands to identify problems and nip them in the bud. High-churn industries like telecommunications companies and even online retailers have used predictions to identify churn with almost 80% accuracy, allowing them to use different marketing strategies for those likely to leave, and those likely to stay.
Machine learning has to be used in conjunction with creative (human) marketing-minds to be fully effective. It won’t do job of creatively or connect to customers in a real way. But when used innovatively, machine learning is helping change-making brands understand their customers and create the kinds of marketing campaigns, products, and services that keep them connected, happy, and coming back for more.