With the latest advances in technology, you can tap into a massive audience using all sorts of online and offline channels. However, these aren’t necessarily advantages. Without the right tools, it’s nearly impossible to figure out how and where to focus your marketing efforts.

Automation makes marketing a lot less complex and exhausting. Implementing machine learning technologies alongside your marketing solutions will help you fully understand which customers respond to which techniques – emails, free e-books, Facebook ads, etc. Additionally, it enables you to deliver a more personalized user experience.

This article lists and summarizes the three general uses of machine learning in the field of marketing, their challenges, and the benefits of implementing machine learning technology to optimize your efforts.

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There’s a story behind every lead and every sale. However, only those who implement marketing attribution will be able to see the story unfold. Unlike vanity metrics, this lets you figure out the answers to the most important question in marketing: Where did your prospects first encounter your brand? What part of the campaign prompted consumers to make a purchase?


One-to-one Matching vs. Predictive Machine Learning

One common approach to solving the attribution problem is one-to-one matching. Let’s say you’re a retailer trying to figure out how much lift you got from driving traffic to your brick-and-mortar stores. With the one-to-one matching approach, you’ll match a user from the campaign group with a user from the control group. Both users need to have similar traits in terms of demographics or other factors. From there, a timeline is created to determine which differences between the groups have brought on a positive or negative change on the campaign group’s habit, using the control group as a baseline.

This method has its benefits, but it also has its disadvantages. For starters, there’s a potential for error because of missing data sets. It also gets complex as more features are added.


one-to-one matching


If you want a more effective alternative to one-to-one matching, you can use a predictive machine learning model. This lets you take all your customer data to come up with a target output which, in this case, is the probability of a customer visiting your store on any particular day. Your predictive model informs you the expected number of visits a customer is going to have. Plus, it shows you if you received any lift from your ad campaigns.


predictive modeling attribution


Implementing automation for your marketing attribution endeavors enables you to track and collect data on all your buyers’ online activity, which shows you which particular marketing initiatives led to revenue generation. This lets you gain actionable insights and helps you make well-informed decisions for your marketing campaigns. In doing so, you’ll be able to gain high-quality leads, generate more conversations, and improve your sales.



To maximize your desired outcomes, you constantly have to improve your marketing efforts. This process is called marketing optimization. You need to optimize each of your marketing tactics. Additionally, you must ensure that these tactics fit perfectly with your overall strategy.

For many marketers, optimization can be quite challenging. You have to figure out the best color to use for your CTA button to entice visitors to click on it. You must also come up with the right subject line to encourage your customers to read your email campaign. These are some of the challenges that marketers face.


A/B Testing vs. Multi-Arm Bandits

A/B testing is a common approach to marketing optimization. For this method, you run two different experiments over a given period of time. Then, you optimize your campaigns based on the winning variant. However, there’s a problem with A/B testing. It’s called regret, which refers to the amount of money you lose every time you explore a sub-optimal variant.

Marketers who do A/B testing go through an exploration and exploitation phase. The former refers to the time you spend testing or finding a profitable marketing tactic, while the latter pertains to the amount of time you spend capitalizing on that solution. Let’s say variant B is the superior variant in your experiment. Thus, during the exploration phase, you’re losing money by testing variant A.


A/B testing


To minimize regret, marketers often turn to an approach called multi-arm bandits. With this approach, you still run all campaigns at the same time, but AI dynamically funnels users to the campaign which proves to be more successful during the run.


multi-armed bandits


Compared to A/B testing, multi-arm bandits drive higher average payouts through dynamically allocating traffic proportional to a particular ad’s performance. This is called aggressive exploitation. For instance, if your first ad starts to perform better than the rest, your algorithm will allocate traffic to that ad based on its performance.


multi-armed bandits


However, a multi-arm bandit model isn’t always superior to A/B testing in determining the most optimum results. A successful campaign may not always be the best in the long run.

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To drive customer satisfaction and client lifetime value, you need to target marketing initiatives and content to an individual customer’s particular interests. Through data collection, analysis, and automation technology, you can deliver individualized content to your prospects and existing consumers.

Here are some of the most common historical approaches to personalization:

  • Demographic-based personalization: You can use consumers’ data such as their age, gender, income, and education to recommend products or services that they’re likely to purchase.
  • Product-based personalization: To find the best product recommendation for a particular client, you can consider their previous purchases.
  • Behavioral clustering: You can also come up with more effective campaigns by using shopping behavior-based customer segmentation to create targeted marketing.


personalized CX


Collaborative Filtering vs. Sequential Prediction

Today, many marketers use a more advanced approach that is called collaborative filtering. In collaborative filtering, similar behavior is used as a filter to define or predict a customer’s purchasing pattern. For example, two different people may have one trait in common, thus making it a possibility that both may like the same product targeted for that common trait. The idea of collaborative filtering is simple enough, but it’s not without flaws. Not all common traits are indicators of common future purchasing decisions.


collaborative filtering


So, more advanced marketing departments are switching to sequence prediction with deep learning. In sequential prediction, data is derived from the sequence of action taken by a potential customer within a site or portal. This sequence of actions and other traits are evaluated to arrive at a predictive action that may or may not lead to a successful purchase. In other words, sequence prediction with deep learning helps you better understand the next action that a consumer is going to take.


sequence prediction


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Machine Learning for Marketers

It’s true that machine learning has limits such as prohibitively high expenses on data collection, computing power, and professional skill. But machine learning is still an exciting area of artificial intelligence that marketing professionals need to consider and look forward to.

With technology constantly evolving and professional skill constantly being updated, we can expect machine learning to become more and more affordable in the coming years. The competition for machine learning will veer away from monopoly, and it will become a commodity in the future market.


In my extended talk below on “3 Ways To Automate Lead Generation With Machine Learning”, I provide a beginner-friendly introduction to Machine Learning and talk more on how you can get started with marketing automation.


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