According to a Salesforce survey, 51% of marketers already use artificial intelligence, and 27% more marketers plan to integrate it in the upcoming two years. This adds up to 78% of marketers using AI by 2026.
The question arises—why do a large majority of marketers from across the globe want to incorporate AI into their marketing plans? More importantly, what exactly has AI done to encourage its adoption by digital marketing agencies?
Let’s break it down. Instead of focusing on the entire world of artificial intelligence, how about understanding a crucial part of it—machine learning in the marketplace?
Even if you are not familiar with the term, you would have already encountered it in real life. Whether you have shopped for a pair of shoes online or communicated with a chatbot on a company’s website, you have experienced ML.
In fact, we all encounter ML in our daily lives — from personalized promotional offers to the spam filters on email to chatbots.
If you are planning to use ML in your company for marketing purposes, this blog is going to be particularly advantageous for you since it covers stories of ML being used by giants like Amazon and Netflix. Keep reading to find out how you can benefit from machine learning for ads, just like multinational tech companies.
Use Cases of Machine Learning in Marketing
Customer Segmentation
Customer segmentation refers to the process of segregating customers into categories based on shared behavior, preferences, and actions. It can be done through data collection from numerous sources, such as website analytics and social media interactions. This allows businesses to market to them more effectively.
Using machine learning, companies can automate the process, making it more efficient and accurate. Distillery interprets company data to comprehend potential customers and develop customer-specific profiles. They provide prebuilt models and custom training on customized datasets.
Content Personalization
Personalization is crucial not only to make sure that the right content reaches the right target audience but also to ensure that customers get a great experience. Using machine learning in online advertising, ventures can customize ads, websites, and much more based on unique customer preferences. This aids in improving retention, engagement, and conversions.
One of the best examples of content personalization is Spotify. The music platform’s Discover Weekly playlist utilizes machine learning to craft tailored music recommendations for the users according to their listening habits.
Predictive Analytics
Machine learning in marketing analytics or predictive analytics is a powerful tool that predicts future results based on analyzing historical data. This can lead to personalized and targeted marketing campaigns according to consumer data, further resulting in higher ROI. Also, it reduces ad spending since the campaigns are stopped or altered based on real-time analysis if they do not perform well.
Stitch Fix is one of the best use cases of machine learning. As a matter of fact, they have among the most advanced algorithm repositories in the e-commerce and retail industry. The company uses data science to comprehend the kinds of products that have higher demand at which places. This helps them best match the outfits according to customers’ requirements and reorder their inventory most efficiently.
Dynamic Pricing
Real-time price optimization or dynamic pricing uses ML to adjust prices according to customer behavior, competitor pricing, and market demand. In addition to this, ML-driven insights are also based on customer characteristics and browsing history. This is what helps Amazon optimize pricing to attain maximum profitability, remain competitive, and meet customer demands.
The most common examples of dynamic pricing through machine learning for digital marketing are airlines and hotels. For instance, American Airlines utilizes revenue management systems that analyze demand and booking patterns. The algorithm helps the airline adjust ticket fares based on factors like time of booking.
Ad Campaign Optimization
Another area where machine learning has led to its adoption by marketers is ad campaign optimization. Machine learning algorithms interpret ad performance in real time and adjust targeting and ad spending accordingly to maximize ROI and efficiency. This includes refining landing pages for conversion rate optimization and A/B testing for various campaign strategies and ad copy.
Programmatic advertising platforms, like The Trade Desk, Google Ads, and MediaMath use ML to automate the bidding and placement of advertisements, making sure the ads reach the right audiences at the right time. This leads to lower costs, higher conversion rates, and better overall targeting.
How Effective is Machine Learning in Marketing?
Even though machine learning is quite new, it is rapidly evolving and transforming the ways in which companies function. Truth be told, the majority of businesses are relying on ML since they are dependent on complex, large datasets for decision-making and strategy development. Machine learning models are capable of helping businesses manage and organize their data and even analyze raw data to provide valuable insights.
How has ML Proven to Be Effective in Marketing?
Instead of focusing on what ML can do, let’s first talk about what it has already done. What impact has machine learning made on the marketing industry?
70% of high-performance marketing teams state that they have a clear AI strategy, unlike 35% of their underperforming marketing peers. Chief Marketing Officers leading high-performance marketing teams believe in fostering a growth mindset and continual learning. In fact, 56% of CMOs plan to adopt ML and AI in the upcoming year, according to Salesforce research.
“36% of marketers predict AI will have a significant impact on marketing performance this year. 32% of marketers and agency professionals were using AI to create ads, including digital banners, social media posts, and digital out-of-home ads.”
– Forbes
What’s more, world-renowned businesses are integrating ML into their existing platforms to improve marketing. Today, Disney is using AI modeling techniques, including machine learning algorithms for advertising, to optimize and refine its media mix model. Its approach to obtaining novel insights into the model is to analyze data from across the company, comprising partners, organize the model data, and then alter it to be used in a model.
That’s not all. “High-performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year,” claims a Forbes article. It further states, “High-performing marketing teams and the CMOs lead them to invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences.”
Machine Learning: Optimizing Marketing Workflows
Marketing teams require correct information to make data-driven decisions. However, due to large amounts of consumer data, it has become increasingly time-consuming and daunting to process and analyze the data manually. This is where machine learning steps in—it allows companies to manage automated email marketing campaigns based on consumer actions and events.
The retail giant Amazon is a great example of this use. It utilized machine learning algorithms for marketing to predict consumer demands and set personalized marketing campaigns based on their needs. By analyzing the data automatically, recognizing patterns and insights, and anticipating when a customer is likely to require a new bottle or packet of the same product they ordered earlier, ML helps Amazon in ways that wouldn’t have been possible manually. Besides, it can also send special offers or automated notifications, leading to more purchases.
Delivering Personalized Experiences to Customers
With the large number of advertisements that customers view daily, personalization has become crucial to attaining success through marketing. Marketers usually use automated systems to generate keywords and other tasks related to the job. The challenge is that these tools are generally based on rules and do not properly comprehend the context of a specific customer. Thus, data scientists aid in improving marketing personalization using ML-based data processing.
Machine learning for advertising enables organizations to better comprehend consumer profiles, craft tailored offerings at a large scale, and augment customer lifetime. One of the best examples of this use is that of CommonWealth Magazine. Among Taiwan’s major media outlets, CommonWealth turned to Appier, which helps increase customer engagement through an AI-powered platform.
As a result, Appier’s custom ML model could recognize reader profiles in detail by analyzing how consumers engage with the website and mobile app, as well as analyzing data from a CRM system. This not only enables CommonWealth to identify the profiles in real time but also allows it to deliver personalized advertisements. Just by changing the keywords, the model led to an augmentation of the ad’s CTR by six times.
Data-Driven Decision Making
As stated previously in this section, machine learning for marketers offers data-driven insights that empower businesses to make more informed decisions. Rather than simply relying on assumptions, machine learning aids marketers to forecast campaign results, trends, and consumer behavior with accuracy and precision.
Businesses have stopped following the one-size-fits-all approach. Instead, they now customize their strategies to various customer segments based on user data. This makes users feel more valuable since the content that reaches them is tailored to their needs and preferences.
A subset of ML, predictive analytics, enables marketers to predict which kinds of products customers are highly likely to purchase, when they are likely to buy, and which types of messages in marketing will relate to them the most. Such predictive ability results in more effective and efficient campaigns.
Challenges to Consider
While machine learning for digital marketing has numerous benefits, there are some challenges organizations must consider before considering its integration into their existing platforms. Some of these challenges are discussed briefly below. Remember to take note of these before you finally plan to adopt ML for marketing purposes in your business.
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Data Quality and Accessibility: ML models rely on large amounts of data for training. In order to deliver meaningful, valuable insights, the data must be clean and accurate. It may be challenging to ensure that data from a variety of sources, including social media, customer engagement, and sales, is easily accessible and correct for analyzing purposes.
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Implementation Costs: Implementing machine learning can be expensive for businesses since it requires skilled expertise as well as technology. It can be quite challenging to find the right professional.
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Need for Continuous Updates: Machine learning models require ongoing updates and training to function as accurately as possible. The results must be relevant to the changing market conditions and consumer behavior. Organizations may find it difficult to keep their models up-to-date.
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Black Box Problem: While focusing on training ML models with large amounts of data, businesses may end up getting more sophisticated. In this case, the models will produce accurate results. However, it will be extremely difficult for marketers to interpret these. This can further impede trust and the acceptance of AI and ML in digital marketing.
Successful Stories of ML in Marketing
Now that you have learned all about machine learning and marketing — from benefits to challenges to use cases — it’s time you find out about the most successful stories of ML in the world of ad campaigns and personalization according to customer needs. Below, we have shared successful ML implementation stories of Netflix, Airbnb, Spotify, DoorDash, and Twitter.
Netflix
The popular streaming platform with customers across the globe, Netflix, uses machine learning to anticipate what the user would like to watch next. If they start watching a TV show and get bored, the Netflix AI system will recommend another TV series for them. It uses historical data associated with users’ viewing behaviors to make these suggestions. For instance, if users first watch Stranger Things, the site may recommend The OA to them.
According to The Motley Fool, Netflix’s suggestion engine is known to save the company a billion dollars a year through higher retention and reduced churn. Moreover, the personalized approach has also assisted it in maintaining a loyal subscriber base and substantially boosting user engagement, with 80% of content watched being an outcome of its suggestions. Thus, AI and machine learning in marketing are game-changers for the firm.
Airbnb
Through predictive modeling, Airbnb recommends locations where visitors may want to stay when visiting a city they haven’t explored. The service aids travelers in looking for the ideal space according to their requirements and factors such as their previous location, amenities, and stays.
In addition, Airbnb also uses artificial intelligence for dynamic, smart pricing since hosts usually are unaware of how they should set the ideal prices based on demand and supply. Last but not least, the company also utilizes the technology to vet users according to third-party data.
The company’s successful IPO—valued at more than $100 million—was worth more than Hilton and Marriot combined. It is a result of innovations that ML has led to and portrays the technology’s contribution to its dominance in the hospitality sector. Through AI and machine learning in marketing, Airbnb has demonstrated that it has what it takes to stay ahead of the curve.
Spotify
Spotify uses machine learning to anticipate when a user will churn in order to be able to take action before they leave. This is done by analyzing data formats such as past user behavior and demographics to predict future actions.
Using this technology, Spotify maintains high retention rates, leading to revenue growth and an overall boost. For instance, if Spotify anticipates that a user is almost churning, it can provide incentives to retain them, such as a discounted price for a quarterly subscription. This is how machine learning in marketing helps them augment a customer’s lifetime value.
Furthermore, Spotify uses machine learning algorithms to see if a user listened to a song for over 30 seconds to recognize their interests. It is due to such personalization that Spotify has been able to retain its users. In fact, the company’s 2023 data shows that Spotify has more than 626 million users, with 246 million premium subscribers.
DoorDashto
Organizations in all kinds of industries are using machine learning in marketing to optimize their ad spending. DooDash is one such instance of a multi-billion-dollar firm using the same technology to reduce its costs by 10% to 30% while continuing to reach the same target audience size.
Refining market spending is conventionally a problem without any solution. In fact, it has affected several businesses. Thanks to AI, this isn’t a challenge anymore. Using machine learning, ventures can focus on increasing their revenue per customer as they decrease their marketing expenses.
X (formerly Twitter)
Our last real-world success story is that of X (formerly Twitter). If you have known it just as a social media platform, allow us to change your perspective. X is a lot more than that—it is a user insights engine. Using machine learning in advertising, X can crop images intelligently, suggest relevant content and timelines to make users continue scrolling, and even filter out content categorized as hate speech.
These are just some of the ways in which artificial intelligence aids X in improving its service for consumers, which contributes significantly to its fight to stay in power and social relevance. With more than 500 million monthly active users in 2024, X’s use of ML plays a major role in user retention and satisfaction.
Key Takeaways
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Increasing Adoption of Machine Learning: 51% of marketers are already using artificial intelligence, and an additional 27% are planning its integration for marketing purposes. Since this adoption is due to ML’s ability to automate tasks, improve personalization, and optimize marketing campaigns, it is crystal clear that AI is rapidly revolutionizing the marketing landscape. LITSLINK offers cutting-edge artificial intelligence services to such companies seeking growth through AI.
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Optimization, Personalization, and Data-Driven Decision-Making: Machine learning in online advertising has proven its efficiency in companies by optimizing workflows and personalizing customer experiences. ML models streamline workflows by interpreting large datasets, further leading to data-driven decisions and improved ROI.
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Use Cases and Success Stories: ML has several uses, including customer segmentation, content personalization, predictive analytics, dynamic pricing, and ad campaign optimization. Leading businesses like DoorDash, Spotify, and Airbnb have leveraged machine learning for personalized recommendations, customer retention, and dynamic pricing. These instances portray how the technology helps organizations stay competitive, increase customer lifetime value, and reduce ad spending.
For ventures seeking to refine their marketing strategies and ad campaigns more effectively and efficiently, integrating machine learning services into the existing processes can prove to be highly beneficial. While there are some challenges to consider, getting expert help can optimize business processes while significantly reducing costs and increasing ROI. LITSLINK can provide the expert help you need to implement advanced machine learning solutions to your existing platform hassle-free.
Today, ML is no longer a luxury. Instead, it has become a necessity for marketers to stay competitive in an increasingly transforming world where the needs of customers and the market change continuously. Machine learning not only makes the process more efficient for companies but also empowers them to make smarter, data-driven decisions.
So, now that you know what kind of positive impact will machine learning have on the advertising industry, what are you waiting for? Try LITSLINK’s services today!