Machine learning, a subset of AI, is a powerful tool that’s rapidly transforming marketing.
Around 35% of marketers are using AI to simplify their jobs and automate tedious tasks, according to HubSpot’s latest research. However, the same research reveals that 96% of marketers still adjust AI-generated outputs — indicating that it’s still far from perfect.
In today‘s post, you’ll learn how machine learning can supercharge your marketing team. We’ll also share actionable examples from real-world companies implementing machine learning and noticing significant improvements.
Table of Contents
Machine Learning and Marketing
Machine learning is a form of artificial intelligence (AI) that enables software applications to become more accurate at predicting outcomes without being explicitly programmed.
Marketers use ML to understand customer behavior and identify trends in large datasets, allowing them to create more efficient marketing campaigns and improve marketing ROI.
For example, Netflix uses machine learning to enhance its recommendations algorithm, forecast demand, and increase customer engagement.
By leveraging customers’ viewing history, the company gains powerful insights into customer preferences, enabling them to make relevant content suggestions.
Look at the image below to see what makes business professionals adopt ML and AI technology.
Image Source
How Machine Learning Can Improve Marketing
Machine learning can improve marketing in umpteen ways. Here are the most common use cases:
1. Gauge Customer Sentiment
Machine learning algorithms can automatically identify customer sentiment, encompassing positive, neutral, or negative opinions.
Initially, they gather textual data from diverse sources like customer reviews, social media mentions, feedback forms, or survey responses.
Subsequently, the data undergoes preprocessing and is labeled according to the corresponding sentiment. This allows marketers to gain insights into customer sentiment and make improvements based on feedback.
2. Personalize User Experience
Machine learning models can analyze user behavior and historical data to predict customer preferences. Marketers use this opportunity to create personalized offers for customers, such as product recommendations, promotions, or discounts.
Additionally, ML can curate content feeds based on user interests and send personalized reminders to customers.
3. Optimize Content Distribution Efforts
Machine learning can analyze the performance of different content distribution channels and offer optimization strategies.
By accessing historical data, it can determine the best time for posting and the optimal frequency of content distribution to avoid overwhelming the audience.
It can also identify the most effective distribution channels, allowing marketers to allocate their resources wisely and achieve maximum engagement alongside ROI.
4. Optimize Ad Targeting and Bidding
ML is revolutionizing targeted advertising.
By analyzing a vast amount of сustomer data, machine learning predicts customer behavior and groups users into segments based on shared traits and characteristics.
Marketers then use this data to tailor ads to those segments, connecting with target audiences that are more likely to engage with the ad.
5. Streamline A/B Testing Processes
A/B testing plays an important role in marketing, as it clearly shows what‘s working and what’s not.
ML helps automate A/B testing processes and make them more accurate. Real-time monitoring of the testing process reduces manual intervention and the likelihood of potential errors.
Furthermore, machine learning decreases the test duration, saving time and resources when one variation significantly outperforms the other.
15 Examples of Machine Learning and Marketing
Forrester forecasts that nearly 100% of enterprises will be implementing some form of AI by 2025. Two more years to go, but numerous companies have already successfully adopted AI.
Here are 15 examples from real-world companies that saw significant improvements after implementing machine learning.
1. Amazon increased its net sales by 9%.
Machine learning has long been an integral part of Amazon, one of the largest retailers in the world.
The ecommerce giant has been using ML for a variety of purposes, such as getting insights into customer behavior and analyzing browsing and purchasing history to provide personalized product recommendations.
These enhance the customer experience as users easily find new products that are similar to their previous shopping experience. Additionally, Amazon creates targeted ads for users based on demand forecasting.
According to its latest financial report, the company’s net sales increased 9% to $127.4 billion in the first quarter, compared with $116.4 billion in the first quarter of 2022.
2. Netflix became an industry leader due to its personalized movie suggestions.
One of the main reasons why Netflix services are popular is that they are using artificial intelligence and machine learning solutions to generate intuitive suggestions.
The company uses machine learning to analyze its customers’ movie choices and make relevant content suggestions. But how does it work?
When you browse their movie directory, their intelligent algorithms watch what kind of movies captivate you, where you click, how many minutes you keep watching the same movie, etc.
Then analyzing your viewing habits, Netflix curates a personalized movie/TV show feed for you. It’s a win-win.
3. Armor VPN predicted lifetime value and maximized user acquisition efforts.
Armor VPN is a consumer cybersecurity (VPN) software that wanted to create a solid user acquisition strategy to attract new customers. With limited marketing budgets, the owners didn’t want to go through a trial-and-error process.
Thus, they partnered with Pecan AI, a predictive analytics tool, to make strategic decisions with the help of predicted lifetime value (pLTV) models.
Image Source
With the tool’s predictions, the client identified a 25% gap on average between the actual user lifetime value and what they expected users’ value to be.
This way, Armor VPN could create a more effective and data-driven strategy to fuel its user acquisition efforts.
4. Devex scaled its content creation processes and decreased costs by 50x.
Devex, based in Washington, D.C., is a major provider of recruitment and business development services for global development.
The company receives approximately 3000 pieces of text weekly, which require manual review by the content team. Eventually, only 300 of these pieces are deemed worthy and tagged accordingly.
Until recently, the evaluation was done manually, which took around 10 hours to complete. To automate the process, Devex contacted MonkeyLearn, a text analysis platform powered by machine learning models.
Devex built a text classifier that helped them process data and then tag if the text was relevant.
It resulted in 66% time savings, and the operation costs decreased by 50x, as less human interference was required.
5. Airbnb optimized renting prices and created rough estimates.
Airbnb faced challenges when trying to optimize the renting prices for customers.
To overcome this, Airbnb used machine learning to provide rough estimates to potential customers. The prices were based on different criteria such as location, size, property type, seasonality, amenities, etc.
Then, by performing EDA, they could understand how rental listings spread throughout the US.
In the final step, the company implemented ML models, such as linear regression, to generate estimates and visualize how prices change over time. It allowed them to create attractive marketing offers and win new customers.
6. Re:member increased conversions by 43% with heatmaps and session recordings.
Re:member is one of the leading credit card companies in Scandinavia. Recently, their marketing team noticed that users were bouncing off their credit card application form more than usual.
Frustrated, the marketing team turned to Hotjar to gain a complete picture of how customers were using their website and what was causing the issue. They utilized session recordings to replay the entire time a user spent on the website.
Heatmaps helped them identify which pages customers tended to click more.
Combining the data, Re:member’s marketing team noticed that many people coming from affiliates were leaving right away.
After reviewing heat maps and session recordings, the team concluded that visitors were initially interested in the benefits section but needed more information.
Consequently, they redesigned the application page, resulting in a 43% increase in conversions.
7. Tuff achieved a 75% success rate on partnership proposals.
Tuff is an SEO marketing agency that achieved significant ARR growth in just three years. Initially, they struggled to create client pitches due to the lack of a reliable SEO tool for thorough competitor and keyword research.
After using Semrush, a leading keyword research tool with machine-learning algorithms, Tuff could analyze prospective customers’ organic performance and create personalized proposals tailored to their specific needs.
This led to a 75% success rate in winning new clients.
8. Kasasa grew organic traffic by 92%.
Kasasa, a financial service company, aimed to scale its content operations and drive organic traffic. They adopted MarketMuse, a content optimization tool based on AI and ML, to save time and resources.
Using simplified content briefs from MarketMuse, Kasasa produced meaningful content much faster. This established the company as an industry expert and increased its recognition, leading to a 92% growth in organic traffic.
9. Spotify created personalized playlists and boosted customer engagement.
Spotify utilizes machine learning algorithms to analyze customer data, such as playlists and listening history.
This allows the digital music service provider to create customer segments based on music preferences, enabling personalized music recommendations and playlists for each user, ultimately increasing customer engagement.
10. Sephora built long-term customer loyalty with Sephora Virtual Artist.
Sephora, a giant cosmetics retailer, has been leveraging cutting-edge technologies, including AI and machine learning, for over a decade. Their virtual artist allows customers to virtually try new products without wearing them.
Through face recognition technology, machine learning algorithms automatically recognize the most compatible shade and recommend products, offering personalized product recommendations, driving customer engagement, and fostering loyalty.
Image Source
11. Coca-Cola improved its sales and distribution efforts by almost 30%.
Coca-Cola has been at the forefront of implementing ML and AI solutions in its marketing strategies.
To maintain its industry leadership, the American company created an AI system to analyze sales data and detect trends in customer preferences.
They also employed machine learning algorithms to optimize their product packaging and distribution, resulting in a remarkable 30% increase in profits.
Additionally, they developed a virtual assistant to help customers with common queries.
12. Yelp is sending personalized recommendations weekly.
Yelp is a user reviews and recommendations platform that utilizes its machine learning algorithms. They leverage machine learning and algorithmic sorting to create personalized user recommendations.
With machine learning, users receive weekly recommendations based on businesses they have viewed in the previous week or within their specific interests. In 2023, the company also introduced its AI-powered review writing service.
13. Cyber Inc. doubled its video course production.
Cyber Inc. is a security and privacy awareness company based in the Netherlands. The company offers training programs and wanted to scale its video course creation process.
They teamed up with Synthesia, an AI-powered video creation platform, to streamline video creation and produce videos in multiple languages.
The collaboration cut down costs on hiring actors since the tool offers an avatar as a replacement. Cyber Inc managed to produce video content two-times faster and expanded its global reach.
14. Uber created targeted ads personalized for each user.
Uber, an American taxi service provider, uses machine learning effectively. With the help of ML, they analyze customer data, such as location and travel history, and create targeted ads tailored to individuals.
Algorithms allow them to optimize ad campaigns for maximum efficiency, resulting in higher customer engagement and usage rates with Uber.
15. Farfetch increased its email open rate by 31%.
Farfetch is a luxury fashion retailer that experimented with AI and gave a fresh look to its email marketing campaigns.
They collaborated with Phrasee, a tool that picks the most relevant brand voice and generates content ideas based on that.
The company witnessed impressive results, with an increase of 38% in average click rate and a 31% average open rate surge in its trigger campaigns.
5 Tips for Using Machine Learning in Marketing
Machine learning can be highly beneficial, but you should know how to use it effectively. Here are five tips for effectively leveraging machine learning in your marketing efforts.
1. Be specific with your marketing goals.
Since ML processes enormous data sets, you’ll likely get loads of unnecessary data. You can easily avoid this if you clearly outline what you want to achieve.
Narrow down your marketing goals and group them into categories such as customer segmentation, ad optimization, conversion acceleration, etc. Start with small-scale experiments and iterate once you have some results.
2. Don’t stick with one ML model.
Experimenting with multiple machine learning models is essential. Different ML models have different capabilities, each with its pros and cons.
For maximum efficiency, you’ll have to test different ML models so you can compare their performance objectively.
For example, one ML model can excel in a certain type of data task but might underperform in a different scenario.
3. Don’t become over-reliant on ML tools.
While machine learning can generate valuable insights, over-relying on it can be detrimental for marketers. ML models are still evolving, and they are not perfect and can’t fully function without human expertise.
For maximum results, it’s better to combine ML with human knowledge. Clearly define each role and set a healthy boundary of when to use ML and when to rely on human decisions.
4. Partner with data scientists.
Not everyone has in-house data scientist knowledge. If you‘re just starting out, it’s a good idea to collaborate with a data scientist to implement the right ML models.
Make sure to ask the machine learning experts to explain the limitations of ML models so you don’t have unrealistic expectations.
5. Respect data policy and be transparent.
AI and ML tools pose a threat to data breaches and privacy concerns.
Since customer data is vulnerable, you’ll need to make sure you comply with data privacy regulations. Avoid unethical usage of customer data and be transparent.
These are crucial to building trust with your customers.
5 Machine Learning Tools for Marketers
As the market is saturated with ML tools, we have narrowed down the list and included only the best ones. Here are five ML tools that will help you streamline your marketing efforts and maximize your profit.
1. Hubspot Content Assistant
Get started with HubSpot’s AI tools.
HubSpot’s content assistant is a powerful tool that allows marketers to supercharge content operations and improve productivity.
It natively integrates with HubSpot products, and you can toggle between AI and manual content creation to create copy for email, website, blog posts, etc.
To use the content assistant, you simply need to fill in the form, describe what content you want, and then click “Generate.” In a few seconds, you’ll have your copy.
Core Features
- Create personalized sales and marketing emails, blog post ideas, and outlines
- Generate paragraphs and create compelling CTAs
- Integrate with the other Hubspot products
Price: Free for Hubspot CRM users.
Pro tip: Segment prospects based on shared characteristics, and then add the lists to the content assistant. The tool will process the data and create personalized emails to streamline your outreach.
2. Monkey Learn
MonkeyLearn is an AI tool that helps businesses analyze data with machine learning. It extracts data from different sources, such as emails, surveys, and posts, and visualizes customer feedback in one place.
Core Features
- Different text formats are supported, such as emails, support tickets, reviews, NPS surveys, tweets, etc.
- Text classification into categories: Sentiment, Topic, Aspects, Intent, Priority, etc.
- Integrations with hundreds of applications such as Zendesk, Airtable, Typeform, Intercom, etc.
Price: There are two pricing plans. The “Team” package starts from $299, and there is a free trial. The “Business” tier’s pricing is not publicly available, and you must contact the sales team.
What we like: The tool is super intuitive, and no coding experience is required. Plus, customers have a wide range of text analysis options and can look at feedback in one central location.
3. Pecan AI
Image Source
Pecan AI is a predictive analytics platform that uses machine learning to generate accurate, actionable predictions in just a few hours.
The tool effectively leverages large amounts of raw data and predicts revenue-impacting risks and outcomes, such as customer churn, LTV, etc.
Core Features
- Pre-built, customizable SQL templates
- Demand forecasting
- Campaign optimization using SKAN
- Integrations with third-party apps
Price: The tool has three pricing plans. The “Starter” plan is $50 per month, “Professional” is $280. You should book a meeting for Enterprise accounts to know the pricing details.
What we like: The tool allows us to harness the power of AI and eliminate guesswork while making strategic decisions.
4. Jasper AI
Image Source
Jasper AI uses machine learning and Artificial Intelligence to generate human-like copy for blogs, websites, emails, social media, etc. This copywriting assistant helps businesses scale their content production efforts and save precious time.
You simply choose the tone of voice, upload the campaign brief, and select the type of content. It will generate a copy in just 15 seconds.
Core Features
- Multiple tones of voice options to match your brand style: cheeky, formal, bold, and pirate
- Content translation in over 30 languages
- 50 different use-case templates
- AI art generator to create visuals for your copies
Price: The tool comes with three pricing plans. The “Creator” plan costs $39 and the “Teams” plan $99 per month, respectively. You’ll have to contact their sales team if you need the “Business” plan.
What we like: Different tones of voice and pre-made campaign templates to create personalized content. An easy-to-use browser extension to access the tool right in your browser.
5. AI Marketer
AI Marketer is a predictive analytics tool that allows you to identify and target your most valuable customers.
By using machine learning models, it predicts the likelihood of customer purchases and sends time optimization notifications to target customers at specific times.
You can also target customers who are at high risk of churning. This helps you boost customer retention and maximize the impact of your marketing campaigns.
Core Features
- Customer behavior predictions on an individual basis
- Smarter targeting
- Data-driven optimization recommendations
Price: The pricing information is not disclosed publicly. You should request a demo. There is also a free trial.
What we like: Different tones of voice and pre-made campaign templates to create personalized content. It also features an easy-to-use browser extension so you can access the tool from your browser.
Using Machine Learning to Maximize Marketing Efforts
AI and machine learning solutions are stepping up the marketing game. Though they‘re still evolving, integrating cutting-edge technologies into your daily stack won’t do any harm.
Instead, it’ll help you automate repetitive tasks and gain powerful insights into customer behavior, enabling you to create highly effective marketing campaigns that yield results.
Keep an eye on technology trends and harness the power of machine learning algorithms.
Credit: Source link
