Machine learning in marketing: 10 use cases and implementation tips
September 19, 2023
Machine learning enables marketers to improve their decision-making by analyzing large data sets and generating granular insights about the industry, market, societal trends, and customer profiles. Machine learning algorithms help companies deliver personalized content, products, and services.
From ecommerce to banking to healthcare, organizations in various sectors are using machine learning for their marketing efforts. So let’s explore why companies turn to machine learning app development to enable marketing automation, enhance marketing campaigns, and improve customer experience.
The state of ML and AI in marketing
60%
of marketers have a fully defined AI strategy
- Salesforce
78%
of marketers say their customer engagement is data-driven
- Salesforce
51%
say AI is very important to their marketing success over the next 12 months
- Drift and Marketing AI Institute
74%
will intelligently automate over 25% of their tasks in the next 5 years
- Drift and Marketing AI Institute
Top 10 ML examples and use cases in marketing
Let’s delve deep into the most popular machine learning use cases in marketing and explore their real-life examples.
Marketing analytics
Machine learning can enhance marketing data analysis with emotion recognition. It has been extensively used in many industries, with companies like BMW using it to assess driver alertness and Netflix for measuring viewers’ reactions to its movies. Marketers can use machine learning and emotion recognition to assess how consumers respond to advertisements and product recommendations and correlate these emotions to purchasing intentions.
Example: Affectiva
Personalization
With the avalanche of ads that consumers view daily, personalization has become paramount for achieving success. Today’s marketers often utilize automated systems for keyword generation and other related tasks. The problem is that these tools are mostly rule-based and don’t really ‘understand’ a particular customer’s context, so data scientists help improve marketing personalization with ML-based data processing.
Example: CommonWealth and Appier
Content creation
Example: Vanguard and Persado
Marketing automation
Marketing teams need accurate information to make data-driven decisions. However, with inordinate amounts of customer data, it has become increasingly daunting and time-consuming to process and analyze all of it manually. This is where machine learning can make a difference. For example, a company can set up automated email marketing campaigns triggered by certain events or customer actions or use ML-powered chatbots to engage website visitors in conversations.
Example:
Amazon
Amazon
Weather-triggered marketing
Example: Walgreens, Clinch, and IBM
Contextual advertising
Example: MINI and GumGum
Identification of marketing opportunities
While we often attribute machine learning's value to improving the speed and accuracy of the data-driven decision-making process, its disruptive potential also lies in its capability to process loads of unstructured data. As much as relying on well-known metrics to craft marketing campaigns is instrumental, marketers also try to dig deeper and understand what societal trends and cultural nuances drive consumer demand. Books, movies, music, and a myriad of other media sources influence our thinking and, consequently, our buying behaviors.
Example:
Unilever
Unilever
Recommendation systems
Nowadays, recommendation systems are the backbone of successful ecommerce stores, helping customers to navigate enormous online catalogs and find items they need. Machine learning-powered recommendation engines play an invaluable role in increasing customer satisfaction and engagement and improving a company’s bottom line.
Example:
LUISAVIAROMA and Dynamic Yield
LUISAVIAROMA and Dynamic Yield
Marketing budget optimization
Aside from customer profiling and generating creatives, all marketers learn the art of budget optimization. Spending too little can lead to insufficient results, while spending too much hurts profitability. Especially when it comes to big enterprises with thousands of marketing campaigns going on simultaneously, a dedicated team of people usually decides how to allocate budgets to get the most returns on investment. Unsurprisingly, this often takes time, and the results can be subpar. Given that there is no shortage of data on campaign performance and customer behavior, machine learning can help automate a large part of campaign bidding and increase its performance.
Example:
Doordash
Doordash
Lead conversion
Lead scoring, or identifying and converting the most potent leads, is an increasingly complex task, especially when there are more leads than a company's employees can physically process. However, with the latest advancements in natural language processing, it has become possible to automatically and intelligently assess leads' chances for conversion.
Example:
Hootsuite and Conversica
Hootsuite and Conversica
Related technologies and services to pair with ML in marketing
Machine learning is not a standalone technology actively used to improve marketing operations in various businesses and industry domains. Itransition has vast experience in other emerging technologies that could be beneficial for marketing purposes:
RPA in marketing departments can generate reports, automate email campaigns, and manage multiple communication channels. It can also automatically adjust pay-per-click (PPC) bids in ad campaigns to improve their performance. You can set an ad schedule for your Google Ads and increase bids when your target audience is more likely to make purchases.
Augmented reality (AR)
The use of augmented reality is growing in marketing strategies. By overlaying audio, visual, and other sensory information on real-world scenarios, AR enables brands to offer immersive user experiences, attract new customers, and increase their interest and engagement.
Virtual reality (VR)
Virtual reality creates a realistic simulation of the environment, which enables companies to showcase their products and services, giving customers a 360° view of their offering. Companies from various domains, from retail, to real estate, to entertainment, leverage VR to let users interact with their services or products, increasing retention and brand awareness.
ML-powered analytics solutions are widely used for marketing purposes, enabling companies to forecast consumer demand and identify market trends, predict customer behavior and automate decision-making. Predictive analytics can help brands improve their marketing strategies, enhance conversion rates, and maximize ROI.
Augment your marketing with machine learning
Benefits of machine learning in marketing
Decreased costs
With ML-enabled marketing automation, companies can reduce operational costs and free marketers’ time for more value-added tasks.
Hyper-personalization
Machine learning allows companies to better understand consumer profiles, create personalized offerings at scale, and increase customer lifetime.
Content optimization
With advancements in natural language processing and generation, ML enables companies to deliver content that increases customer engagement.
Improved segmentation
With the help of ML in digital marketing, organizations can automate customer segmentation, predict their lifetime value, and discover new, more potent customer groups.
Accelerated revenue growth
Accurately predicting consumer behavior and needs, marketers can generate greater ROI for their marketing campaigns.
Percentage of respondents, n=460
Scheme title: Outcome of using AI in marketing
Data source: drift.com — 2022 State of marketing and sales AI report
Challenges of ML adoption in marketing
Despite multiple use cases and successfully delivered projects, ML adoption in marketing can encounter several barriers.
Challenge
Potential solution
Data quality and accessibility
Machine learning models require vast amounts of data for training. Therefore, it could be a challenge to ensure that data from different sources, including sales, customer interactions, and social media, is accurate and easily accessible for analysis.
- To effectively deal with loads of data, you can implement several best practices:
- Start gathering data from all your sales and marketing activities, customer journeys, as well as their demographics, preferences, and churn rates
- Plan which workflows you want to revamp with an ML tool
- Get a consultation from ML experts on how to guarantee your data accuracy and consistency, perform data cleaning, and set up data warehouses to build accurate forecasting models
Lack of expertise
Leveraging machine learning for marketing purposes requires skilled data science professionals. However, not every company has enough resources to hire in-house ML experts.
- Mitigating the shortage of required expertise will require investments for:
- Hiring in-house data scientists with marketing experience
- Training in-house marketing specialists to upskill them in machine learning and data science
- Outsourcing your ML adoption project to external AI/ML service providers (will also require additional investments, as you still will need an internal point of contact involved in the project)
Black box problem
Getting more sophisticated, ML models can generate accurate but hard-to-interpret results. An unexplainable logic behind the solution’s reasoning can impede trust and further technology acceptance.
- To ensure interpretability and transparency of the results, provided by your ML solution, you can:
- Pick the right metrics to evaluate the result. This won't allow you to move AI into the white-box field, but will give you a better understanding of how the system reacts to any changes and get direction for its improvement.
- Divide the pipeline into definitive steps and establish human oversight over the decision-making and the final outcome
- Regularly assess the ML model’s performance and fine-tune it to achieve accurate results
Make your marketing smart with ML
Marketing has always been about establishing meaningful connections with customers. While banking on technology to establish a more personalized approach to every customer may sound counterintuitive, machine learning can be exceptionally good at deciphering human emotions, needs, wants, and intentions by analyzing mountains of data that companies have at hand. Whether it’s marketing automation or analytics, machine learning is ripe for marketers to use, and it’s high time companies augmented their marketing departments with it. Turn to Itransition’s engineers to harness the power of machine learning and increase customer engagement, attract more leads, and improve your company’s bottom line.
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