Introduction
Machine Learning in marketing has altered the traditional way of marketing. According to Gartner, by 2024, leading organizations will employ machine learning in some aspects of their sales process. Businesses use machine learning to stay ahead of the competition by handling some of marketing’s most difficult obstacles, such as personalization, real-time customer assistance, and big data.
Marketers use ML to make sense of all the data at their disposal. ML processes massive amounts of data considerably faster and more effectively than humans. This analysis is used to find trends and make predictions virtually quickly. Furthermore, marketers can use these insights to optimize a large amount of their workflow, such as performing additional tests and improving the user experience of their website, personalizing the customer experience, and automating consumer engagement.
Machine learning improves nearly every aspect of your digital marketing activities. How? We will discuss it in this article. So, let’s dive right into it!
Table of Contents
What is Machine Learning?
Machine learning is an artificial intelligence subset that enables computers to learn without being explicitly programmed. It allows a system to “learn” through trial and error. Machine learning is all about using algorithms to model, simulate, and anticipate solutions to real-world issues. Complex machine learning algorithms can replicate the cognitive functions associated with the human mind (namely learning and problem-solving).
Source: Aliz
Machine learning is already being used to optimize in various areas, including healthcare, vehicle finance, and eCommerce. Let’s look at how machine learning can be used in content marketing.
Example of Machine Learning
Consider throwing a piece of paper in the trash.
You realize after the first effort that you used too much force. After the second attempt, you realize you are getting closer to the target but need to increase your throw angle. What is going on here is that with each toss, we are learning something new and enhancing the overall result. We are hardwired to learn from our mistakes.
We can do the same thing with machines. We can train a computer to learn from all of our attempts/experiences/data points and then enhance the outcome. Let’s look at a paper toss example in both the Machine and Non-Machine approaches.
In our previous example, Machine Learning software would start with a generic formula and refactor it after each attempt/experience. The outcome improved when the formula was improved by utilizing more experiences (data points). You may see these things in action all around you, such as YouTube Video Suggestions and Facebook News Feed Content.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This basically indicates that in machine learning, for any work, a machine’s performance improves with experience. This is precisely what we discovered in our paper toss example.
A Machine Learning algorithm is not required to calculate a person’s age given his date of birth. Nevertheless, you would utilize a Machine Learning algorithm to determine a person’s age based on his music preferences. For example, the statistics would show that Led Zeppelin and The Doors fans are primarily over 40, whereas Selena Gomez’s are mostly under 25. Machine Learning may be found in almost every aspect of your life. Therefore, evaluating whether the problem requires Machine Learning to be solved is critical.
Machine Learning in Marketing
By analyzing massive data sets and delivering granular insights about the industry, market, societal trends, and customer profiles, machine learning enables marketers to improve their decision-making drastically.
Organizations may supply hyper-personalized offers, content, products, and services thanks to computing systems’ ever-increasing processing capacity and the increasing sophistication of machine learning algorithms for marketing.
10 Benefits Derived by Companies by Using Machine Learning in Marketing Functions
Source: Spiceworks
1. Marketing Automation
Marketers require precise data to make data-driven decisions. But, with so much data available today, it has become increasingly daunting to sift and evaluate it all manually. This is where machine learning comes into the picture.
For instance, customer segmentation is one of the most important marketing tactics because it entails classifying clients based on numerous factors such as age, gender, income, and so on. Machine learning can not only automatically group these clients at breakneck speed, but it can also identify new customer categories based on a combination of qualities that people do not recognize. For example, Salesforce Einstein AI can analyze huge customer and industry data volumes and automate marketing actions, such as customer segmentation and reporting.
2. Marketing Analytics
If there is ever a need for a superhero in the marketing world, it is for understanding human emotions. On the other hand, machine learning for emotion identification is already widely utilized in numerous industries. Businesses such as BMW use it to assess driver attention, and Disney, to measure viewer emotions toward its films. Marketers can use machine learning and emotion identification to measure how people respond to commercials and correlate these feelings to purchasing intentions.
3. Ad Personalization
Personalization becomes critical to success with an onslaught of advertisements bombarding consumers daily. Marketers today know this and frequently use automated solutions for keyword generation and other related duties. The issue is that these technologies are rule-based and do not truly “understand” a specific customer’s context.
This issue confronted CommonWealth Group, one of Taiwan’s largest media groups. The company profits through reader subscriptions and adverts placed in its four magazines oriented to financial management, lifestyle, career, and parenthood. The marketing staff at CommonWealth had very limited data on its audience and struggled to personalize adverts, so the publisher showed the same ad to all readers.
To help enhance client engagement, CommonWealth turned to a startup that provides brands with an AI-powered platform. By studying how customers engage with the mobile app and website and interpreting data from a CRM system, the company’s bespoke machine learning model was able to determine reader profiles in great depth. CommonWealth can now recognize reader profiles in real-time and dynamically deliver personalized ads.
4. Content Creation
Copywriting and marketing content generation has always been connected with creativity and emotional sensitivity, both of which cold-hearted algorithms clearly lack. Consumer emotion towards marketing communications may be understood as data in the digital arena. Insights derived from that data can be utilized to develop the precise wording that inspires a certain consumer to act.
5. Weather-triggered Marketing
Despite our efforts to affect every aspect of our life, for better or ill, humans have yet to understand how to manage weather conditions. Yet, with predictive analytics software and machine learning technologies, we can anticipate weather and predict how changes in meteorological conditions affect consumer behavior.
On the surface, rainy days cause car washes to be empty, early snow ensures a profitable season for ski resorts, and hot summer days increase ice cream sales. While companies have long been aware of these linkages, machine learning enables a much more dynamic approach to digital marketing and advertising.
6. Contextual Advertising
AT&T’s first internet ad on HotWired in 1994 achieved a 44% click-through rate (CTR). Nowadays, the average CTR for google analytics is 0.3%. With an avalanche of online ads displayed daily to a modern internet user, marketers are finding it increasingly difficult to produce ads that resonate with potential clients. Content marketing has gotten even more difficult as cookie restrictions have tightened, causing businesses to rely more on contextual advertising.
Contextual marketing advertising refers to placing ads that target specific audiences on relevant websites. For example, a consumer electronics news website could be an excellent place to display adverts for a new phone. Although, determining the ideal web pages to display advertisements and developing the right message for a targeted audience is a monumental task if done manually.
7. Identification of Marketing Opportunities
While we frequently ascribe the value of machine learning to enhancing the speed and accuracy of data-driven decision-making, its truly disruptive potential rests in its ability to make sense of massive amounts of unstructured data. While it’s important to base marketing strategies on well-known criteria, smart marketers often dive deeper to discover what societal trends and cultural nuances drive consumer demand. Books, movies, music, and a variety of other media influence our thinking and, as a result, our purchasing behaviors.
8. Recommendation System
Recommendation systems are becoming the foundations of successful e-commerce enterprises. Product recommendation engines assist clients in navigating frequently massive internet catalogs and locating the things they require. Machine learning-powered recommendation systems can significantly increase customer satisfaction and engagement while also enhancing a company’s bottom line.
9. Marketing Budget Optimization
Aside from client profiling and developing new creatives, all marketers must master the art of budget optimization. Inadequate spending results in insufficient revenues, whereas excessive spending reduces profitability. Especially in large organizations with thousands of marketing initiatives running concurrently, a dedicated team of individuals usually decides how to distribute resources to maximize returns on investment. Unfortunately, this frequently takes an extraordinary amount of time, with unsatisfactory outcomes.
Given the scarcity of data on campaign performance and customer behavior, machine learning can be utilized to automate and improve campaign bidding.
10. Lead Conversions
While every marketer’s goal is to produce as many leads as possible, finding and converting the most potent leads is becoming increasingly difficult, especially when there are more leads than a company’s personnel can physically analyze. With recent advances in natural language processing, it can automatically and intelligently estimate lead conversion probability.
10 Use Cases of Machine Learning in Marketing Functions
1. Predictive Recommendations
Recommender systems can be used to surface information that a user would find interesting. For example, recommender systems can be helpful if you want to know what movies to see based on your interests or what music to listen to next based on your mood.
For instance, When travelers visit a new city, Airbnb employs predictive analytics to recommend places to stay. This suggestion tool assists tourists in finding the best space for their needs based on characteristics such as prior stays, facilities, and proximity.
They also employ AI for smart pricing because hosts frequently don’t know how to establish the ideal rate based on current supply and demand and the listing’s individual specifics. Finally, Airbnb employs AI to verify visitors based on third-party data to avoid rogue actors.
The success of Airbnb’s IPO, which resulted in a valuation bigger than that of Marriott and Hilton combined, is partly due to developments like these. Airbnb has demonstrated that they have what it takes to get ahead of the competition by utilizing AI.
Netflix predicts what you’ll want to watch next using machine learning. If you begin viewing a movie and become bored, Netflix’s Artificial intelligence technology will suggest another movie to you. It makes these recommendations based on historical data on users’ viewing habits. If you watched The Hunger Games, it might suggest Squid Game next.
2. Text Classification and Natural Language Understanding
Consumers communicate with your company via textual comments such as product evaluations, tweets, form submissions, emails, etc. It is critical that you can grasp their feelings. Natural language processing based on machine learning simplifies creating and implementing everything from nasty tweet flagging to targeted nurture campaigns.
This provides unmatched insight into your client’s requirements and what they say about your brand. It also allows you to respond fast to unfavorable sentiment and use that knowledge to improve your product or service.
Source: Github
For example, if a consumer tweets negatively about your product or service, this knowledge will be important for enhancing your product or service in the future and providing an opportunity to win back the customer.
AI platforms are being utilized by a prominent consumer electronics business to create machine learning models that categorize and prioritize product input, enhancing efficiency and allowing engineering teams to focus on what matters: improving products and resolving concerns.
Analyzing all the comments to identify the most relevant insights takes time for analytic teams. They were able to construct models that could identify sentiment on a previously unthinkable scale using no-code NLP, allowing their engineering teams to make more actionable product decisions.
3. Lead Scoring and Sales Funnel Optimization
Lead scoring is the science of forecasting which leads are likely to convert. In contrast, sales funnel optimization is the process of refining the sales funnel based on previous sales data to focus sales efforts better. This can help you optimize your marketing and sales cost and increase conversions.
Sales teams would have to manually sift and assess thousands of leads each month if lead scoring did not exist. These same teams can employ machine learning to apply a lead scoring model to automatically identify the most potential leads and prioritize their time and attention, allowing them to boost team productivity while decreasing costs.
Even in a B2C context, lead scoring may be an extremely useful tool, such as assisting online shops in determining which products a user will likely purchase based on previous behavior and displaying the right ad to the right person at the right time.
Machine learning is being used by businesses across many industries to optimize marketing budgets. DoorDash is one example of a multibillion-dollar company leveraging technology to reduce costs by 10 to 30% while reaching the same number of clients.
Optimizing marketing spending has typically been a difficult topic that has stymied many businesses. They can use AI to enhance revenue per customer while decreasing marketing costs.
4. Forecast Targeting and Decision Making
One of the most common applications of machine learning is forecasting. It lets you forecast future revenue, costs, and even commodity prices. This allows you to make better inventory decisions, estimate campaign reactions, and more.
While running Facebook advertising campaigns, you can target people who have previously interacted with your brand or indicated an interest in your product field – a type of forecast targeting and campaign response prediction that allows you to focus your ads on those who are most likely to convert.
Forecasting also assists you in making smarter product decisions, such as releasing a new feature or product. If you know your company’s revenue primarily relies on email signups, it makes sense to focus development efforts on enhancing your email signup flow. This will aid in increasing signups while lowering turnover.
5. Personalization
Personalization is essential for offering an excellent customer experience. You can use machine learning in various marketing activities to tailor your website, emails, advertising, social media and other communications to each individual customer. This increases conversions, engagement, and retention.
Source: Retalon
Machine learning can help you customize your email marketing efforts to be more relevant to each recipient. You can, for example, separate your list based on interests and send various emails to different groups. Alternatively, you might send more focused emails based on data from previous encounters. For example, if a consumer abandons their shopping cart, you could give them a discount voucher to entice them to complete their purchase.
6. Retail
Machine learning is being utilized for pricing in the retail industry. Retailers may make better decisions about pricing their inventory if they understand how demand fluctuates and which products sell at what price. This allows them to remain competitive while also increasing earnings.
E-commerce enterprises use machine learning to target promotions and discounts to certain customers who are most likely to convert. For example, if you have a customer who has shown interest in your products but has never purchased anything from your store, you could send them a discount code to entice them to do so.
7. Continuous Optimization of Marketing Campaigns
You can use machine learning to optimize your marketing initiatives in real time, ensuring that they are constantly performing at their peak. A/B testing different ad copy or campaign methods and improving landing pages for conversion rate optimization (CRO) are examples of it.
Machine learning can be used to A/B test different ad copy to determine the best-performing headlines, descriptions, call-to-actions (CTAs), and pictures for your commercials. This guarantees that your campaigns are always performance-optimized, allowing you to get more bang for your dollar.
8. Customer Segmentation
Customer segmentation is the process of categorizing customers into groups based on shared qualities to more effectively market to them. You can automate this process with machine learning to make it more accurate and efficient.
Source: Marketing Evolution
9. Targeted Promotions and Discounts
You may tailor promotions and discounts to specific clients who will most likely convert using machine learning. This allows you to improve income while lowering customer acquisition expenditures.
E-commerce enterprises use machine learning to target promotions and discounts to certain customers who are most likely to convert. For example, if you have a customer who has shown interest in your products but has never purchased anything from your store, you could send them a discount code to entice them to do so.
You can also utilize machine learning to segment your consumers so that you can offer them the most relevant promotions. For example, if you have a customer who always purchases high-end stuff, you may give them coupons for other luxury items they would be interested in. If, on the other hand, you have a customer who only buys on sale, you could want to send them notifications when products they’ve shown interest in go on sale.
10. Churn Prediction
Machine learning allows you to sift through historical customer data to identify churn patterns and anticipate which customers will leave next.
For instance, machine learning is used by companies such as Spotify to forecast when a client may churn so that action can be taken before the consumer goes. They analyze demographics, previous user behavior, and other data forms to forecast future behaviors.
These businesses may sustain high retention rates with this technology, which improves revenue and helps the bottom line. For example, if they forecast that a customer is about to churn, they might provide incentives to keep them, such as a lower subscription fee for the next three months. As a result, they can successfully use machine learning for marketing to boost customer lifetime value effectively.
Conclusion
Machine learning enables marketers to make better decisions by analyzing huge data sets. With ML, they can derive powerful insights into the industry, market, social trends, and customer profiles. ML can support marketing teams in providing hyper-personalized offers, content, products, and services.
Learn more about Machine Learning with Analytics Vidhya’s Machine Learning certification course for beginners. The course includes python libraries like Numpy, Pandas, etc., to analyze data efficiently. It will give you a basic outlook on ML and its functions.
If you are interested in mastering ML concepts, you can register for the Certified AI/ML BlackBelt Plus Program. The program offers an excellent chance for professionals to learn and implement advanced skills. It provides in-depth instruction in data analysis, machine learning techniques, and real-world case studies, allowing students to construct predictive models and optimize marketing tactics.
Frequently Asked Questions
Q1. How is machine learning used in marketing?
A. Machine learning is used by marketing teams to identify trends in user behavior on a website. This allows them to forecast future user behavior and immediately optimize advertising offers that can be utilized to launch a new product and enhance customer experience.
Q2. How are AI and ML used in marketing?
A. AI marketing can perform tactical data analysis faster than humans and utilize machine learning to get quick conclusions depending on the campaign and customer context. This allows team members to devote more time to strategic projects, which can subsequently be used to inform AI-enabled campaigns.
Q3. How to use ML in digital marketing?
A. Here are some ways of using Machine Learning to Improve Your Marketing
- Analyze Data Sets.
- Create and Optimize Content.
- Increase Personalization.
- Improve Marketing Automation.
- Utilize Chatbots.
Q4. What are the benefits of ML for marketing?
A. The use of machine learning in marketing enhances data analysis quality. It allows you to examine more data in less time while also adapting to changes and new data. It also enables you to automate marketing procedures and prevent repetitive tasks.
Q5. What are the advantages of using machine learning in marketing?
A. In Marketing, ML allows for making critical decisions rapidly based on massive data. Marketers utilize machine learning to discover patterns in consumer behavior. This allows them to forecast user behavior and immediately optimize advertising offers.
A highly motivated Content Writer with knowledge of blogging, copywriting, SEO, and content framework creation. I am familiar with basic knowledge of analyzing blog traffic and deriving actionable insights.
Thank you for useful information. In 2023 huge development in Machine learning and also Ai. So, Keep sharing more valuable insights.