Machine learning for marketing is in a nascent stage, but it is poised to play a much more important role as the industry gains a clearer understanding of the possibilities it presents.
Although they are buzzwords that are often interchanged, artificial intelligence (AI) and machine learning are not one and the same. Machine learning is a subset of AI that is being used to make AI even more intelligent.
Ultimately, machine learning helps humans solve problems in a more efficient way. Machine learning occurs when a computer is fed information to analyze, then it "learns" as it discovers trends and patterns. Without explicit programming, the machines teach themselves algorithms that enable them to gain better insights from the data. By feeding the machines more data, the algorithms continue to be refined, making the data analysis and insights even more accurate.
Machine learning for marketing is one of the most impactful technologies available to marketers today, enabling them to quickly and efficiently analyze huge amounts of data without errors - something that is not humanly possible to do. Machine learning is playing an increasingly important role in several aspects of digital marketing, including personalized marketing, content optimization, segmentation and intelligent chatbots.
Let’s look at some more specific examples of how marketers can apply machine learning to improve engagement, increase customer lifetime value (LTV) and make them more loyal to your brand:
Customer behavior prediction - Anticipating what a customer wants or needs before they realize it is something that marketers have been trying to achieve for years. It's only with the introduction of machine learning that this can become a reality.
Machine learning enables brands to gather insights on customers’ preferences and determine what they will do next. These insights can help a brand understand if some is likely to leave your website to look for a better deal elsewhere, or if they are a candidate for conversion, then react accordingly.
Amazon is most prominently known for its use of customer behavior prediction. They have used machine learning and prediction models to refine product recommendations, which drive 55% of its sales. Amazon also leverages customer behavior prediction to forecast product demand and optimize inventory, to ensure they're prepared for seasonal or trend-based consumer demand.
Engaging Users with Intelligent Chatbots - If you've used Siri, Alexa or other similar interfaces, then you've had conversations with intelligent chatbots. And increasingly, you see these solutions integrated into websites, as pop-ups, to assist with customer service and support. These virtual robots also play a critical role in digital marketing.
Through the use of natural language processing (NLP), intelligent chatbots enable personal conversations between consumers and brands. These conversations allow the intelligent chatbots to collect data about users and continually learn from them to provide more accurate answers over time. These results then can be analyzed to help deliver better service and a more personalized experience for your customers, and improve loyalty to ensure they continue to return to your brand to purchase more products and services.
Customer Segmentation - In the past, marketing efforts, such as advertising, have been scattershot, leading to a lot of waste and inefficient use of marketing dollars. However use of machine learning in marketing is helping brands better target their messages through customer segmentation. The insights resulting from machine learning take out a lot of guesswork. Marketers can better understand which messages will resonate with their target customer groups and spur them into action - either greater engagement or conversions.
With so much purchase data available, machine learning can help you understand which products are often bought together, and the types of items on which consumers may splurge, to deliver more effective promotions. Machine learning also supports customer segmentation into groups of high-value prospects, who share attributes with existing customers and will be the most likely to become customers.
Improved personalized marketing - Consumers today face a barrage of marketing messages, whether they're online, watching TV or out in the 3D world. The best way to cut through the noise is if a consumer believes your brand is speaking directly to them as individuals. In fact, Salesforce research shows that more than half of consumers are likely to switch if they feel brands are not personalizing their messaging.
Personalized marketing requires that every touchpoint in the customer's buying journey - from emails to product offers to promotions - be tailored to each individual. This level of personalized marketing is not possible without machine learning, which leverages the continuous flow of information about each consumer - from purchases to clicks to preferences - to ensure that the right offers can reach the right people, at just the right time.
Now that you have a better understanding of the ways in which you can use machine learning for marketing, let’s dig in and take a deeper look at the five top types of algorithms that deliver the results you’re seeking:
A type of unsupervised machine learning algorithm, clustering algorithms are used to analyze unlabeled data and put it into groups with similar traits. These would then be assigned into clusters, which can be used for customer segmentation based on purchase history, app behavior and other metrics; social media analysis; or developing recommendation engines.
This supervised method of machine learning defines relationships between a dependent and independent variable. The most popular types of regression analysis are linear and logistic modeling. Linear regression looks at various data points to define which variables are the most significant predictors, to determine trends. Logistic regression predicts the value of data based on prior data set observations, and is commonly used in customer service to personalize offerings using historical shopping behavior.
One of the most commonly used recommendation algorithms, collaborative filtering examines individuals with similar interests, analyzes their behavior, then recommends the same items. The basic approaches include user-based or item-based collaborative filtering. Either way, the steps are the same: first, you need to find out how many users or items in the database are similar to the given user/item, then you weight the arithmetic mean based on the degree of similarity.
These types of algorithms use deep learning to combine collaborative filtering and content-based models. This enables marketers to fine-tune what they know about interactions between users and items, and removes the possibility of over-simplifying users' tastes.
Also known as Markov chains, these are a way to statistically model random processes. These take into consideration real-time data without factoring in historical information. These typically have to be combined with other machine learning methods to achieve hyper-personalized offers.
By applying one or all of these types of algorithms into your machine learning for marketing, you will be able to more effectively target customers, improving engagement, strengthening customer LTV and growing revenue.
Learn more about machine learning and how it can provide value and relevance to your customer loyalty members. Read our latest white paper, “Bridging the Gap Between Data Science and Marketing.”
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