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6 Ways to Use Machine Learning for Better Marketing Results

By SCUBA Insights

Less than a year ago, AI and ML felt out of reach for many companies. Maybe you lacked the technical skills and resources to manage AI, or ML algorithms were too niche for your uses. Those days are over.


In a short time, machine learning has gone from a cool, experimental tool to an ingrained piece of the modern marketing stack. Two-thirds of marketing leaders say ML lets their teams focus more on strategic activities. And nearly three out of four marketers are already using AI, or plan to soon.


But simply having AI-enabled marketing isn’t enough. You need to understand how to leverage machine learning to best find, connect with, and serve your customers.


What is machine learning in marketing?

Machine learning is a type of AI that learns and improves without explicit guidance. It uses algorithms and statistical modeling to analyze vast amounts of data and infer trends. And as it processes more data, the model optimizes its own ability to draw accurate conclusions.


For marketers, this means you can leverage ML to process the vast quantities of data you have. An ML algorithm can analyze customer journeys and ad performance to spot trends. And ML can also look toward the future, developing predictive models about customer behavior.


The key to every ML application is good data. If you feed an algorithm garbage, you'll get skewed analyses and flawed predictions. But by focusing on high-quality first-party data, machine learning can help you maximize your data's value and yield actionable insights.

6 ways machine learning can elevate your marketing

Here are six powerful ways marketing teams can leverage machine learning.

1. Ad optimization

A/B testing is a tried-and-true method to optimize ad campaigns. But think about how many variables you’re working with:


  • Ad images and copy
  • Landing page content
  • Ad placement on publisher sites
  • Target demographics like age, gender, income, interests, etc.


Taken together, a single ad could have hundreds of permutations. Managing the process by hand would be a nightmare, but machine learning handles it seamlessly. By plugging in basic guidelines, ML can intelligently experiment with ad setups to quickly hone in on what’s working.

2. Recommended content and products

Predictive analytics allows marketers to guess what users are looking for based on past experience. For example, if someone has seen the first nine Fast & Furious movies, they’d probably watch the newest one as well. 


With machine learning, you can spot complex and subtle trends in what content users consume. And as the model observes how people react to recommendations, it continually improves the algorithm.


Netflix is one of the best examples of how predictive analysis can impact a business. Their recommendation algorithm drives 80% of the content users watch. Strong predictive analytics drives engagement by keeping users on your platform longer. And it improves retention as customers feel like they’re getting value from your service.

3. Customer segmentation

Understanding customers is critical to engaging and retaining them. Segmentation and audience modeling are two common approaches. But with thousands of data points for every user, spotting trends can be challenging.


That’s where machine learning comes in. Algorithms outpace humans’ information processing by orders of magnitude, finding trends and shared values at blistering speed. And as it tests those trends, the algorithm learns which are meaningful and which are coincidental.

4. Messaging personalization

One of the foundations of great customer experience is personalization—delivering what a user wants, exactly how and when they want it. This is especially important for messaging, as you aim to float in the sea of email or text messages.


Machine learning can help you improve messaging by tracking a variety of user preferences:


  • How frequently do they want to hear from your brand
  • Which omnichannel marketing methods are most effective
  • What topics they’re interested in reading about
  • What times of day, week, or month they’re most likely to engage
  • What types of offers will drive action


By tailoring communications to user preferences, you can lower your costs and increase your open and conversion rates.

5. Chatbots

Conventional wisdom says people don’t like talking to a robot. However, 74% of consumers prefer using chatbots when looking for answers to simple questions.


Automated chats are most effective as a first line of support. Help users find basic information, then transfer them to a live agent for complex questions. Machine learning can support more effective chatbots in two ways:


  • Analyzing queries and the resulting answers to determine what users are looking for with more accuracy
  • Track automated results’ effectiveness to optimize when users should reach live support

6. Customer journey analytics

No two customers have the same experience with your brand. But many of them will share key touchpoints, be it seeing ads, signing up, or using popular features. Customer journey analytics helps you optimize each of those processes.


Manually viewing customers’ journeys is useful, but machine learning takes it to the next level. ML algorithms work faster and more comprehensively than any human, to give you more insights in less time.


For example, say an online gaming company launches a cross-channel promotion for a new game. Numbers look good on the whole, but Facebook campaigns are showing intermittent drop-offs in engagement. It might take a human hours or days to spot this trend. But ML algorithms can spot small deviations in minutes, raise alerts for further analysis, and adjust marketing budgets to minimize losses.

Using machine learning to power decision intelligence

Machine learning can help any marketer better connect with their audience—but it’s just one tool in your belt. To maximize ML and AI, you need a platform that ensures algorithms are working from good data. And it needs to produce real-time results and actionable insights. That’s where a decision intelligence platform comes in.


A decision intelligence platform lets you:


  • Unify all of your data streams, giving ML algorithms access to clean and cohesive data
  • Monitor ad placements to keep your funnel full and of ideal prospects
  • See a 360° view of customers' journeys and use AI to discover pain points
  • Build custom KPI dashboards to track marketing performance in real-time
  • Keep your marketing efforts compliant with security and privacy regulations

Learn how the right decision intelligence platform can help you use machine learning to elevate your marketing.

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