Marketing automation is an undeniable time saver.
After all, if you can automate something, why do it manually?
But what if I told you that soon you might not even have to automate things… as your computer will do that for you?
Of course, I’m not saying that you won’t have to do any marketing work at all. However, as things like artificial intelligence and machine learning become more advanced, so do computers’ capabilities to make your work life less tedious.
The truth is, the marriage of machine learning and marketing automation can just be the next marketing revolution.
But, before we jump into the details – let’s look at what AI and machine learning actually are.
What is AI and Machine Learning — a quick overview
If you’ve watched at least one Sci-fi movie in your life, I’m sure you’re familiar with the term Artificial Intelligence (AI).
In technical terms, it’s the science of making intelligent machines and computer programs. And intelligent machines are precisely what most people imagine when they think about AI.
It has to do with the way AI is portrayed in Hollywood blockbusters.
But, while most silver screen depictions of artificially intelligent machines range from less than optimistic to straight-up dystopian, in reality, AI has a much more positive side.
The essential benefits of AI? It helps tackle repetitive jobs. Plus, it reduces human error and helps us make better (and faster) decisions.
But how does AI know what’s the right decision to make?
That’s where machine learning comes in.
Machine learning is a branch of AI focused on building algorithms to help machines learn.
In short, it aims to help computers use data to improve their accuracy, imitating the way humans learn.
The beauty of machine learning (ML) is that it allows computers to learn without being actively programmed to do so. This means they can use past data to improve processes, collect new (better) data, improve processes even further, collect more data… you know where this is going, right?
There’s a reason why Bill Gates called Machine Learning one of the most important breakthroughs:
”If you invent a breakthrough in artificial intelligence, so machines can learn, that is worth 10 Microsofts.”
It’s also no surprise that AI is already revolutionizing countless industries — marketing included.
So, how does machine learning benefit marketing automation?
Let’s jump right into it!
How Machine Learning and Marketing Automation work together
Marketing automation is a powerful tool on its own.
With the right strategy, behavior emails, lead scoring, or user segmentation, you can bring huge savings, boost your sales, and help improve customer retention.
Your team can automate even the most complex tasks and spend the saved time on other business activities thanks to automated workflows and rules.
With traditional marketing automation, you’re the one responsible for all the setup, analysis, and tinkering.
That’s where machine learning comes in:
Machine learning algorithms take the data you collect in automating your marketing and use it to further optimize your processes.
Here are a few examples of what some machine-learning algorithms can help you with:
Leverage predictive lead scoring
Lead scoring is the process of “ranking” your leads to determine which of them are most likely to turn into customers.
By assigning them scores for the actions they perform, you can separate the “cold” from “warm”. That way, you get to see how likely that lead is to convert, as well as assess its value.
This, in turn, allows you to segment your leads better and send them different email campaigns, suitable for the stage of the buyer’s journey that they’re in.
The problem is, with traditional lead scoring, you are responsible for developing the whole scoring framework yourself. As a result, there’s a risk that you’ll miss out on some leads due to them being scored incorrectly.
Plus, the whole process can be quite time-consuming – and it’s not a set-and-forget thing. To calculate lead scoring, you need to:
- Develop a buyer persona
- Segment your leads
- Examine your leads’ online behavior
- Rank and prioritize your leads’ actions
- Set score values
- Set the right conditions in your workflows
- Evaluate and adjust periodically
Predictive lead scoring allows you to use algorithms to score and qualify leads based on past data. While you still need to build a framework for your lead scoring system, you don’t have to keep adjusting the system yourself.
Instead, the automation will use predictive modeling to analyze past leads and look for patterns. Then, it’ll use that data to try and predict future behavior. It can then come up with its own ideal customer profile, helping you score leads more accurately.
The beauty of predictive lead scoring is that it does the lion’s share of work for you.
Machine learning in your marketing automation can also help you identify patterns that you’d most likely have missed, helping you score and qualify leads much more accurately.
Gain better customer insights
One of the keys to achieving great marketing automation results is understanding your audience. Still, most businesses make the classic mistake of separating their prospects into no more than two groups:
- Customers
- Non-customers
Some go one step further and understand the difference between “warm” and “cold” leads. But, even then, their audience looks something like this:
- Customers
- Prospective customers
- Disengaged audience
The above is a decent first step. After all, segmenting your audience is key to improving engagement and skyrocketing your marketing ROI.
But that’s still not enough.
Sure, it’s hard to create the right segments.
That’s where the mix of machine learning and marketing automation can help you.
By using algorithms to analyze your audience’s behavior, ML can see things that are easy to miss for the human eye.
As a result, it helps you build way more accurate segments, eliminating unnecessary guesswork. And better segmentation equals better marketing results. This becomes even more important as your list grows bigger.
After all, the last thing you want is to send an untargeted broadcast to thousands of people:
Up your personalization game
Personalization is key to high customer engagement and loyalty.
But, poor personalization efforts can do way more harm than no personalization at all. In fact, 63% of consumers stop buying from brands that engage in poor personalization tactics.
The key to doing personalization the right way?
Understanding not just who your customers are but also how they act.
So, how does machine learning help you become more efficient at personalization?
Meet sequential predictions.
Traditionally, most marketing automation personalization is built on the user’s demographic or shopping data.
To find likely buyers, marketers will take age, gender, education, or purchase history and look for common characteristics with past customers. Then, they’ll use that data to recommend products that they believe those people are likely to buy.
The problem with this approach is that not all common characteristics are equally effective at predicting future purchases.
After all, we’re all unique human beings.
So, even if we come from a similar background, we can still act differently. And it’s the actions of your leads that sequential prediction focuses on.
In short, a marketing automation tool with sequential prediction will look at the sequences of actions that lead your audience to purchase your product.
A sample sequence could look something like this:
- Visit your website
- Scroll down the homepage looking at the advertised products
- Go to a product X category
- Browse through the latest additions
- Open three tabs with favorite picks
- Review each of them carefully
- Select one product
- Scroll back to the product category
- Sort the category by price
- Compare the selected product with similar ones
- Pick a winner
- Make a purchase
Of course, this is just a sample sequence. If you are getting thousands of visitors to your website, you’ll have thousands of sequences to analyze.
This would be impossible to do manually.
Machine learning can analyze those sequences for you and try to figure out patterns that lead to a purchase. It then looks for subscribers who are most likely to follow the same sequence, effectively showing potential customers.
Boost sales with better product recommendations
Continuing on personalization, one of the areas where machine learning is especially effective is product recommendations.
And, if you’re in eCommerce, they’re often the #1 thing you should focus your personalization efforts on. Why?
Think about a giant like Amazon. Their recommendation system is widely believed to be one of the most advanced and effective ones.
A McKinsey & Company research shows that up to 35% of Amazon’s sales can be attributed to recommendations. That’s over 1 in 3 sales in a multi-billion business!
According to another report by Accenture, as many as 91% of consumers are more likely to shop with brands that recognize, remember, and provide relevant recommendations.
Not to mention that offering buyers an opportunity to buy another product they’re interested in is a surefire way to boost your conversion rate.
Of course, you don’t need the latest machine learning algorithms to start recommending products. But the more accurate the recommendations, the higher the chance of a sale.
This means that machine learning can not only help you automate your product recommendations — it can make them more effective, driving your conversion rate up.
And, as you’re about to see in the Netflix example – retail companies are not the only ones that can benefit from automated, ML-powered recommendations.
Create dynamic websites and sales funnels
Most marketing automation tools allow you to track your audience’s behavior on your website.
Some tools allow you to create rules that change certain website elements based on the subscriber’s behavior or lead score.
You still need to do all the work creating the rules and editing the website. Plus, the changes you’re allowed to automate this way are usually relatively minor.
But what if you could use machine learning and marketing automation to personalize the entire content of a website that your users see… in real-time?
Soon, machine learning will allow us to create dynamic site pages specifically for a particular visitor.
Think about different content, messaging, tone of voice, even the colors – all adjusted to create the best user experience possible. And, of course, to skyrocket your conversion rate.
For example, as a SaaS company, you could adjust each sales funnel step based on user segmentation or behavior.
The beauty of this solution?
The more people who visit your website, the more data your machine learning algorithms get.
This, in turn, would allow those algorithms to create even better content, making your website even more effective at converting its visitors.
A/B test faster (and get better results)
Your competitors never sleep.
To grow your business, you need to keep improving your marketing.
You need to find ways to stand out, grab your audience’s attention, and convert more of them into customers.
The key to doing that?
A/B test your marketing campaigns.
A/B testing is the most popular way to optimize your marketing efforts. It’s also the simplest one out there. Simply take two different creatives and test them against each other, comparing results over a given period.
Of course, if one variation is a winner, the other has to be a loser (duh..) This means that, for some time, you’re sending a portion of your traffic to a variation that’s losing you money. Or, that’s at least performing worse than the winning one.
As a result, the overall payoff is equal to the average payoff of all variants in the test, assuming you run them for an equal amount of time. And, considering that a typical A/B test needs thousands of impressions, the losses on worse-performing variants quickly add up.
This problem in A/B testing is called regret.
But, that’s where machine-learning-powered, multi-armed bandit (MAB) algorithms can help you.
What’s a MAB?
Imagine walking to a casino in Las Vegas with the goal of maximizing your payouts from slot machines.
There are two ways you could go about it.
First, you could test all slot machines in a casino, collect the data, and find the one which maximizes your payout. This would give you the most accurate data in the long run, but it’d also cost you the most money. That’s how a standard A/B test works.
The alternative is to focus on a few slot machines that start showing potential right in the beginning. Then, evaluate your winnings, and maximize your investment where the ROI is best. This is what happens during multi-armed bandit (MAB) testing.
Unlike a typical A/B test, machine learning in MAB can spot and evaluate failing tests on its own. Plus, it does so way faster than you or your team members can.
It’ll then start curbing down the amount of traffic those tests receive, minimizing the regret (and your losses).
This, in turn, leads to higher average payouts from the test. It also allows you to find the winners faster in the short run.
The only drawback is that a MAB test may miss out on opportunities that could offer better payout long-term.
But, as machine learning algorithms become smarter, they can become better at predicting the outcome of the test.
Some agencies report that machine learning-powered MAB tests brought a minimal increase of conversion rate by 30%.
Set more effective pricing for your products
Lastly, machine learning can help your business create and implement a more flexible pricing strategy.
Implementing dynamic pricing allows you to better react to market demand, changing supply, or simply your sales targets.
Plus, because the price is still the dominating factor in the customer decision-making process for two-thirds of your customers, setting prices dynamically gives you a competitive advantage.
Naturally, not every type of business will benefit from dynamic pricing. First, to leverage machine learning and dynamic pricing, you need reference points and a wealth of data.
You also need to have a customer base that’s happy to pay fluctuating prices.
If the prices for your product or service are static, changing them dynamically could turn off your customers. They’d feel cheated. This, in turn, could damage your brand and make your customers trust you less.
Of course, dynamic pricing is not a new concept. It’s been around for some time already, mainly in the travel (hotels, flight tickets) and advertising (Google or Facebook Ads) industries.
But, as eCommerce competition is increasing, finding ways to automate price management, at least in some industries, will become a key element of your overall marketing strategy.
Save time automating content creation
In 2021 we saw the rise of AI-fueled content creation tools based on APIs like OpenAI. OpenAI uses the popular GPT-3 technology, and it can be applied to any task or process that involves understanding or generating natural language or code.
GPT-3 itself stands for Generative Pre-trained Transformer 3 (GPT-3), and it’s an autoregressive language model that uses machine learning to produce human-like text. It scours the entire worldwide web, extracts text and other content, and learns how to write and speak natural human language on its own, thanks to ML.
Marketing automation platforms like Encharge have implemented the GPT-3 technology to enable marketers to automatically create impactful subject lines and email content using AI. The Free AI Subject Line Generator can spill dozens of unique subject lines based on a topic and tone.
Case studies: Machine Learning and marketing automation in practice
Now, let’s take a quick look at two big companies that have greatly benefited from mixing marketing automation with machine learning.
Lyft
The ride-sharing app’s team built a marketing automation platform with the goal of improving the cost and volume efficiency of their user-acquisition campaigns.
Their idea was to use automation and machine learning to automate routine decisions, scale efficiently, and build a data-driven learning system.
That way, their team members could ditch mundane tasks and focus on high-impact experiments and innovation.
While the platform that they built had a lot of moving parts, the most important ones were:
Lifetime Value (LTV) forecaster
This component used machine learning to measure the efficiency of different acquisition channels.
It forecasted their LTV and used that data to determine the right budget that should be allocated to the particular channel they’re coming from.
Interestingly, before they could determine LTV for a new channel, they were able to get machine learning algorithms to predict it from historical data.
Budget Allocator
The second component was responsible for collecting marketing performance data in conjunction with LTV forecasts.
It then used Thompson Sampling to determine the optimum cost for each channel. Once the data was ready, it sent each campaign’s allocation to the respective channel bidder.
Of course, there were a few more parts to the platform than that. The long-term success of Lyft’s marketing automation still depends on human feedback.
But, as the team admits, without having to update bids or allocate budgets manually, their marketers had more time to work on new ad formats, messaging or form hypotheses for long-term goals.
And these are all things that, as of now, neither marketing automation nor machine learning can do for us.
Netflix
How much is your marketing automation strategy worth for you?
And how much do you think you’d benefit from incorporating machine learning into it?
As it turns out, Netflix estimates their machine learning marketing automation engine to be saving them…
One billion dollars. Every freaking year!
And the best is, it’s just one element of their marketing automation strategy — Netflix Recommendation Engine — that’s saving them all that money.
What makes it so powerful?
As it turns out, 80% of the content streamed on Netflix is picked following their recommendation system.
What makes their recommendation system so effective?
While we don’t want to bore you with the technical details, the main idea behind the system is to offer their audience the most suitable titles they might be interested in watching.
That sounds like a good idea, right?
If you think about it, it’s what every recommendation system is supposed to do!
So, where’s the $1 billion component here?
As it turns out, for Netflix, it all comes down to the artwork. You see, the company doesn’t stop at merely coming up with the best title the user might want to watch. Their algorithms analyze their show history and come up with the best picture that might interest the user in the show.
Think about a movie like Good Will Hunting. Classic, right? For example, if the user is into romantic movies, they’ll see a featured image containing Matt Damon and Minnie Driver.
On the other hand, a fan of comedies will see Robin Williams.
The same works for fans of specific actors too. Let’s look at Pulp Fiction. Fans of Uma Thurman will see Pulp Fiction artwork centered on the movie’s main female star.
At the same time, if the algorithm finds out the user is a fan of John Travolta, they’ll see the artwork featuring him instead.
Interestingly, to avoid regret in their A/B testing, the company admits to using the MAB approach, where the algorithm works on figuring out the optimal artwork while the test is running.
After all, they have to run similar tests for over 180 million users. And, considering that they all have different preferences, this means running millions of various tests!
Of course, you might say that it’s all automated. Still, someone has to come up with and keep polishing the algorithm. This takes us to the next point in our article.
How much of marketing automation is automated?
Even though marketing automation takes a massive share of work off your team (and offers tremendous benefits), it’s not as hands-off as it may sound. The three main problems are:
- You still need to set everything up.
- You (or someone on your team) are still responsible for reviewing and adjusting the setup for the best results.
- Even if your marketing automation uses machine learning algorithms, you still need to supervise the learning process.
The first problem means that, in reality, marketing automation can automate only what you, or someone on your team, tells it to.
This means that its efficiency will only be as good as your setup. If you fail to create the right marketing automation strategy, even the best tools won’t help you move your business forward.
Then, unless your setup is super simple, you’re unlikely to get it all right the first time around. And, even if you do, there’s almost always room for improvement.
For example, think about your email marketing automation. To make it work, you need to prepare:
- Email opt-ins
- A lead magnet
- Email sequences
- An automation workflow
The above alone is a lot of work.
What’s more, the more you automate, the more you can test. This adds even more tasks to your workload.
In the above example, you could test each element multiple times, with multiple variations. Think about different opt-ins, CTAs, buttons, lead magnets, emails… And, as you test all that stuff, you need to review all that test data. Change the creatives, edit the workflows or adjust lead score benchmarks. But that’s where we get to #3.
With the correct algorithm, automation can learn from past data. We’ve already mentioned predictive algorithms or self-adjusting MAB tests, which could automate some of the work you need to do in #2.
Of course, it could not change the creatives for you. But, as mentioned earlier, certain marketing automation solutions can self-adjust the workflows. When it comes to simple automation, machine learning can already automate almost everything!
A great example of this is a chatbot. While you still need to create the initial setup, some chatbot builders can already learn and build entire sequences based on their past conversation with humans. This allows them to self-adjust and improve the experience they offer to your audience. If you think about it, it’s no surprise that the market for this (relatively new) tool is estimated to reach $1.3 billion by 2025.
Of course, even machine learning algorithms need supervision, especially as you go beyond a simple, predictable 1-1 conversation.
For example, while both Lyft and Netflix achieved great success with their AI-powered marketing automation, neither automation was fully automated.
In the case of Lyft, aside from building and managing the platform, their setup still required human feedback. Without it, the company risked the so-called garbage-in, garbage-out problem. If the data used to train the model was of poor quality, the results provided by the automation wouldn’t benefit the business.
This means that even though computers can automate more of your marketing than ever before, you still can’t treat marketing automation as a hands-off thing. At least not if you want to achieve amazing marketing results.
But, if there’s one thing we’re sure about machine learning and marketing automation is that the work you put into it can pay off 10x or more. And, as you’re about to see, more and more businesses are aware of how powerful that marriage can be.
Is Machine Learning the future of marketing automation?
If you think about everything that machine learning is capable of doing, it seems clear that the only reasonable answer to the above question is a firm “yes, it is!”.
But, I have to disagree. However, that’s not because I believe that machine learning is not the future of marketing automation.
Rather, I think that ML is already an inseparable part of marketing automation. The “arms race” is already on.
Businesses know how valuable the data they collect is — and that they could use algorithms to have their marketing automation software learn from that data.
According to a survey by Martech.org, Marketing Automation and Email Distribution tools were leading the list of marketing tools that businesses replaced in 2020.
The main reason? Over half of businesses cited better features as the main reason they decided to jump the ship and look for another provider. That’s 19 percentage points higher than just two years earlier!
The market sees the opportunity – and responds to it rapidly. According to a MarTech 5000 report, data is the fastest-growing SaaS solutions category, at a staggering 25.5%
Of course, machine learning in marketing automation is still in its infancy. It’s hard to fully predict what the landscape will look like in a few years. And, just like any technology, it comes with certain risks and limitations that you need to be aware of.
Marketing automation data learning risks and challenges
Machine learning is all about data. The more data you collect, the more efficient your ML-powered marketing automation can become.
As a result, many risks are, in some way, related to the data you collect. The most common include:
- Collecting poor quality data. We’ve already touched on this subject before. If the data that you collect is not a good representation of your audience, you can’t really use it to train your machine learning algorithms.
- Regulatory changes. Depending on the markets you operate in, different laws govern what you can and cannot do with your data. Make sure you understand what’s allowed (and what’s not).
- Entrusting handling of your data to non-compliant third-party providers. Data is invaluable – both for you and for the people that share it with you (your audience). When choosing tools to handle the data of your customers, always select high-quality marketing automation providers who are compliant with data privacy and handling regulations.
Other risks relate to the way you think about incorporating machine learning and machine automation into your business:
- The lack of strategy. If you don’t know why (and how) you want to take advantage of machine learning and automation in your organization, you’re unlikely to succeed with it.
- Expecting machine learning to do all the work for you. As discussed earlier, machine learning can do a lion’s share of work for you. But, you still need someone to oversee the algorithm.
- Treating algorithms as infallible. Self-learning algorithms are a great thing. They save you time and provide you with insights you might have never spotted yourself. However, that doesn’t mean they can’t fail or provide you with erroneous results.
The last, but, in some cases, the most significant risk, is all about falling into the trap of treating your audience as mere pieces of data.
Sure, you want to achieve great ROI on your marketing efforts. But don’t dehumanize your audience.
Always remember that behind every piece of data, there’s a human being. It’s that human being that you want to focus on when executing your marketing strategy.
So, while we wholeheartedly encourage you to start looking into how the machine learning and marketing automation duo can benefit your business, don’t forget to give your audience the respect they deserve.
If you can balance the need for data in machine learning with respect for your audience’s privacy, you’re on your way to achieving fantastic business results.
Conclusion. The time to act is now
Despite their “mainstream presence”, there are still many myths surrounding artificial intelligence and machine learning.
Some believe AI and marketing automation will replace humans. Others are even scared that technology is soon going to kill us all.
That may be true if scientists working on it aren’t careful. But, as of now, the only thing AI and Machine Learning are killing are the businesses that fail to adapt.
However, they don’t get wiped out by AI and Machine Learning itself. Rather, by those who implement advanced marketing automation solutions into their businesses. This includes machine-learning-powered marketing automation.
Of course, even the best marketing automation is far from completely substituting us in our daily work. However, there’s no doubt that machine learning can greatly improve the way we handle and gain insights from our data.
That’s as long as you have all the basics in place.
The first step?
Find out how marketing automation and a tool like Encharge can help your business. Let us help you pick the right strategy for your needs.Schedule a quick call with one of our experts, and let’s talk about your business.
