Machine learning in marketing:

10 use cases and implementation tips

Machine learning in marketing:
 10 use cases and implementation tips

September 19, 2023

Aleksandr Ahramovich
by Aleksandr Ahramovich, Head of AI/ML Center of Excellence
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. Let’s explore how machine learning experts can enable marketing automation, enhance marketing campaigns, and improve customer experience, building their digital marketing strategies using ML.

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

Affectiva, a company that specializes in building machine learning models that understand human emotions and cognitive states, provides its Media Analytics solution to 28% of the Fortune Global 500 companies including Mars, Kellogg's, and CBS. Emotion recognition could be a valuable ML-based technology in retail, however, given very strict regulations around the storage and analysis of biometric information, companies can use Affectiva’s tech only on volunteers. As viewers watch advertisements, Media Analytics captures their facial emotions, analyzes them, and presents findings in an easy-to-use dashboard. It can also correlate facial emotions with crucial marketing indicators like a person's purchase intent or brand recall.

Image title: Affectiva Media Analytics platform in action
Data source: affectiva.com — Affectiva Media Analytics for Ad Testing

Affectiva Media Analytics platform in action

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: 

Related technologies and services to pair with ML in marketing

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.

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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

Challenge

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.

Challenge

Potential solution

  • 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

Challenge

Leveraging machine learning for marketing purposes requires skilled data science professionals. However, not every company has enough resources to hire in-house ML experts.

Challenge

Potential solution

  • 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

Challenge

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.

Challenge

Potential solution

  • 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.