The Stats

  • Forbes says 84% of marketing organizations are implementing or expanding AI and machine learning in 2018.
  • Research shows that by 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate.
  • McKinsey found that implementing machine learning across a retailer’s value chains has the potential to deliver a 50% improvement of assortment efficiency and a 30% online sales increase using dynamic pricing.
  • Gartner says that by 2020, 85% of customer interactions will be managed with no human involved.

What is machine learning?

Let's first quickly review what machine learning and AI actually is.

Here's a quote from one the largest AI companies in the world, Google:

Machine learning—a subset of AI and the “brains” behind many of digital marketing’s automated solutions—gives computers the ability to use data to learn and adapt without being explicitly programmed to do so.

Machine learning can do things much faster, more precisely, and on a scale far greater than what's possible for humans.

What this means for marketers is they can embed machine learning into their marketing and discover new things from their data, ranging from:

  • better targeting
  • more personalization
  • (most importantly) a better customer experience

What does this mean for marketers?

As marketers, we know that we need to cultivate a growth mindset and always be adapting. The best marketers know they need to stay ahead of the competition, and now more than ever that means taking a data-driven approach to growth.

By using machine learning to gain more clarity about how to solve prospective customers problems, marketers can tailor content to qualify leads at each stage of the funnel.

If you've been to a marketing conference recently, you know that machine learning is fast become mainstream in the industry.The reason for this - the usual suspect: competition.

Competition has, and probably always will, force innovative marketing teams to stay up-to-date with the most advanced technologies to gain an edge in the insights they glean from their data.

With the AI revolution firmly afoot, now is the time to start setting aside a portion of the marketing budget to bring the power of machine learning into your marketing mix.

Where to apply machine learning in your marketing mix?

Let's now review where you can apply machine learning to the marketing mix:

Audience & Targeting

  • Machine learning offers marketers the ability to refine their audience targeting by identifying potential customers who are most likely to be interested in your products or services at a given time

Creative

  • Marketers can use machine learning to serve the right message, to the right person, at the right time, and at scale.
  • Machine learning can learn from data to understand consumers intent at different stages of the conversion cycle to serve the right creative at the right time.

Optimization

  • Machine learning can optimize ad campaigns to automatically adjust your bidding strategy to ensure you show up in key moments when people are searching for your products or services

Measurement

  • Every marketer or business owner wants to know the same thing - "did the campaign work?"
  • Use machine learning to find the answer the maximum accuracy through data-driven attribution. This approach uses data to automatically determine which campaign, ad, or keyword is driving the most impact and measure marketing efforts across devices and channels more precisely.

Customer Segmentation

When each and every user generates hundreds or thousands of data points, manual segmentation becomes nearly impossible. Which actions should you segment based on? Which user qualities? How many factors should you include? You get the idea.

As the segmentation company CleverTap states:

Predictive analytics can cut out the guesswork and quickly identify key user segments. Think those most at risk of churn or those most likely to convert.

Beyond that, machine learning powered segmentation can analyze your users and find complex patterns and correlations that would have otherwise gone unnoticed. This is what lets you take personalization to the next level.

Predicting Customer Behavior

What if you could anticipate what your users will do next?

  • One of the applications of machine learning prediction algorithms is extrapolating a users future behavior. It can tell you which users are more likely to convert, which ones are showing signs of leaving a platform or app.
  • These insights all lead to the ability to run targeted promotions that reduce churn and produce a better ROAS.

An example that Shopify points out, is Amazon's famous recommendation engine. In particular:

their machine learning algorithms work so well that 55% of sales are driven by these machine learning recommendations.

A few other uses of machine learning for marketing that include:

  • Chatbots
  • Generating content
  • Content curation

Without machine learning, it would be too difficult to process the enormous amounts of data coming from multiple sources such as website visits, online purchases, mobile app usage, etc, to predict what marketing offers and will be the most effective for each customer.

How can you apply machine learning to your marketing stack?

Alright let's get technical.

As this Forbes article details, here are a few ways you can actually implement machine learning algorithms in your marketing stack:

1. Customer Segmentation with Unsupervised Learning

Every one of your customers is unique, but they all belong to groups. Unsupervised learning allows marketers to dynamically group their audience and engage them accordingly.

2. Regression Algorithms for Dynamic Pricing

Choosing the right price at the right time is crucial for dynamic pricing strategies. Regression-based machine learning algorithms allow marketers to predict and optimize pricing based on pre-existing features. Regression techniques in machine learning can also be used in sales forecasting.

3. Text Classification with Natural Language Processing

Natural language processing, or NLP, can analyze text or voice-based user content and can classify it based on sentiment, tone, and user intent. An example of this is IBM Watson’s Tone Analyzer, which can parse through online customer feedback and determine the tone and sentiment of product reviews.

Case Study: North Face & IBM Watson

A great example of machine learning in marketing is the partnership between North Face and IBM Watson. Powered by IBM's cognitive computing technology, North Face built an app that delivers Fluid’s Expert Personal Shopper to consumers.

This software, which IBM acquired in 2016, is a dialogue-based product recommendation platform that enhances product discoverability while simultaneously offering an improved customer experience.

This tool essentially helps users navigate the online experience as if they were with an in-store sales representative.

You can read more about the case study here.

Conclusion

Marketers know that digital marketing is changing every day, with new opportunities and challenges arising with each technological breakthrough.

As the Digital Marketing Institute highlights:

ML won’t replace existing digital marketing jobs. Rather, it will help broaden the capabilities of the modern digital marketer, providing a base to do and be better at what you do.

By embracing the power of machine learning in your marketing stack, marketers and business owners need to level-up their existing systems in order to stay competitive, and think forward.