Discover how AI marketing tools truly work and find the answer to the question: Can they really increase your website’s conversion rates?
Do you know how machine learning is impacting conversion rate optimization for marketers? We all know what the acronym “AI” stands for: “As If”. Data scientists are telling us that by using AI, they’ll will be able to create a predictive model of the visitors to your website that will tell you exactly who is ready to buy.
I say, “As if.”
We may marvel that such things can be done, but we also recognize that these things require a great deal of data and the skills of some serious brainiacs to get a machine to tell us something we don’t already know.
The truth is, you are probably already using “AI”, or more accurately, machine learning in your marketing. It’s hiding in the tools we use, like monsters under our bed. Machine learning and the more sciencey-sounding AI will change the way you take products to market, but your human mind will still be needed and loved.
Unless you resist – “as if.”
Augmenting Our Brains: AI-powered conversion optimization
Things like AI-driven predictive models are exciting, because our job as marketers is to predict the future. We’re like that exotic fortune teller gazing into an empty tea cup or a crystal ball.
We say things like, “If you give me a budget, I’ll generate six times that amount in revenue.” This is is like saying, “If you put a chicken foot under your pillow, you will find true love.” As if.
But this is what we do, and the data on which we base our predictions is often no more valid than the layout of tea leaves at the bottom of a cup. Our brains are wired to find patterns in anything, even when a pattern isn’t really there.
If I came into your office and said, “The last three leads we generated were all visiting the website using a Firefox browser,” your brain would jump to the conclusion, “If I can get more Firefox users to visit our website, I’ll generate tons of leads.”
Purveyors of AI, or more accurately Machine Learning (ML), would tell us that the machine doesn’t make mistakes like this. Our 100% genuine intelligence just doesn’t stack up to their Artificial Intelligence.
The problem is that machines will make exactly the same mistake if we don’t give them lots of data.
Just as machines need data, we know that we need more data before we start an ad campaign targeting Firefox users. We’ll ask our analytics person to pull together all website visits for the last year, and calculate the conversion rate for each. This increases the size of our dataset from three to many.
If this analysis goes the way of most analyses, we’ll find that there’s not a meaningful difference in conversion rates among browsers. Most experiments end up being inconclusive. That’s just the way it is.
In this scenario, we “wasted” an hour of our data scientist’s time, an hour of our time, and another twenty minutes explaining to our boss why we were so unproductive today.
“What if,” the AI crowd says, “you could get a machine to sort through your data looking for clues and figuring out who’s more likely buy. You don’t have to waste your time. Let the machine do it.”
This is an exciting proposition. The machine wouldn’t just look at the browser. It would look at the time of day, day of week, and week of the year that visitors converted. It could consider the device being used, screen size and operating system. It could add in the source of the visit, the number of times a visitor has been to the website, and whether the visitor has bought before.
After crunching through all of your analytics data, the machine would give you a percentage chance that the next visitor to your website will convert. And here comes a person with a Safari browser on a Mac computer at 3:30pm EST on a warm Tuesday afternoon who’s never been to the site before.
The machine might spit out, “There is a 51% chance this person will complete the lead form.” Actually, the machine will just say, “0.51”. Machines are so boring.
It’s amazing that a machine can so accurately predict a human being’s behavior. This is incredible.
But, is 51% good? And if this is true, what should my website do differently to make this Safari visitor more likely to buy? Do I reduce the price by 49%? Do I flatter this visitor for being above average? Do I ignore them?
This is “the rub” with machine learning. The machine can’t tell us what to do with the data it gives us. There are systems that will tell us if a visitor is “at the top of the funnel” or “in the consideration phase.” Still, what do we do with that? A price-sensitive buyer may want to see a discount when “at the top” of their purchase process. A relational buyer may not care about discounts until they’re “at the bottom,” ready to buy.
The machine won’t tell us, “Target Internet Explorer visitors coming late at night on a Windows computer during the springtime months with a picture of a cat.” It spits out the probability for each visit: “0.51, 0.34, 0.71, 0.92”.
Wait! A 92% probability? Is that important!? Well, no. They’re probably going to buy no matter what we do. “As if.”
Scoring customers in a customer relationship management (CRM) platform has required that marketers hand-code the algorithm. We decide which actions indicate that a prospect is moving closer to buying. We decide how to value each action. It can work, but it isn’t rocket science – or AI.
Alternatively, we can dump sales data into a machine learning algorithm and let it calculate the probability that each prospect will turn into a customer. The sales force can focus on those high-probability clients and disregard the low-scoring leads. It’s using past performance to predict the future, and should be more accurate than arbitrary assignment of values to actions.
This is how machine learning is entering your life as a marketer.
AI Conversion Rate Optimization: Can AI Marketing Tools Increase Your Website’s Conversion Rates?
Amazon famously introduced product suggestions to the eCommerce world. “People who bought this also bought that and that and that.”
It’s not an easy problem to solve. There are a lot of variables to crunch and it has to be done quickly. This is a prime area for AI.
Mailchimp launched a similar tool to add product suggestions to the emails of its eCommerce clients. Every time you send an email to someone, Mailchimp will include a few product suggestions at the bottom of the email. The machine learning algorithm will compute the probability that one or more products will appeal to a subscriber, based on the behavior of all email recipients. Those products with the highest probability get added to the email. This prompts the visitor to buy.
It’s hard to know how well the machine has learned what your visitors buy collectively. This is the limitation of AI. We can’t really see what is inside the box. All we get is a number.
If you implement a suggestion engine on your website, we recommend running an A/B test to measure its effectiveness. This is done by adding the “Also bought” suggestions for half the visitors and hiding it for the other half. This will give us some conclusive data about how the suggestion AI is performing. Is it increasing the order size on average, or reducing it?
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