Machine learning has now become so advanced that algorithms are able to predict how a user is likely to behave. This impacts many aspects of your digital strategy, but nowhere more so than conversion rate optimization (CRO). To understand how it is possible to use AI for CRO, you first need to know exactly what is meant by artificial intelligence and machine learning.
Machine Learning: How It Works
Machine learning is a type of artificial intelligence. Without receiving specific programming for the task, it takes data about previous behaviors and determines what future actions are likely to be. There are a couple of reasons why the functions carried out by machine learning are impossible for marketers to manage alone. First, the massive amount of data it requires is too much for any human to handle. Second, AI machine learning is less likely to develop biases that could lead to incorrect assumptions.
Applied Machine Learning for CRO
There are several ways to apply machine learning to improve website conversion rates.
Chatbots emulate natural conversations with visitors to your website to help them find products or provide them with further information. Learning from previous interactions, chatbots keep improving the experience they provide. In addition, chatbots reduce the workload of your customer service team and give users the chance to communicate with your company outside of business hours. All these benefits mean that you are able to interact with a greater number of potential customers, and therefore increase conversions from your site.
You can easily improve product search on your website by implementing an autocomplete feature. This functions by listing search suggestions as the user types — like what happens when you start typing a query into Google. You can add machine learning and CRO to the autocomplete feature by adding learning search. This means that, instead of just taking into account the letters the user types, the algorithm considers similar terms.
Another aspect of machine learning functionality is knowing what products users are most likely to buy. Customers are more likely to make purchases if you present products that immediately appeal to them. Rather than displaying the most popular products, the algorithm considers what the user (or similar users) looked at or bought in the past, the time of day, and the user’s location.
In addition to suggesting products according to user behavior, you can present similar, more expensive products from competitors. Either display these to users on your website or send customers the comparisons in emails (provided you have their permission).
Adjustments to Pricing
Another way to apply machine learning for CRO is to adjust prices in real time according to the customer. An algorithm calculates the ideal price point to bring the highest profit while maximizing the chance of a purchase.
You can see this occurring on some e-commerce websites if you and another user check out the same product while in the same location but each on your own device. The prices will appear different because an algorithm is presenting a distinct offer according to your past behavior and other factors.
If the algorithm determines that there is no chance that you will make a purchase, you’ll see the full price. The same is true if you are very likely to purchase. However, if the algorithm calculates that you have some doubt or you were unhappy with the price you saw previously, you may receive a discount.
If you have a longer sales funnel, you may need to optimize for conversions like downloads of premium content, signups for email newsletters, and enrollments in free trials. This requires sending users notifications about what they are most likely to act on.
You can use machine learning to figure out what type of notification to present to users as well as what elements lead to the highest conversion rate. You may discover that a sense of urgency is most effective; it could be social proof, or it may be a different factor entirely.
You can also apply machine learning to predict how page design impacts conversions. In this case, the algorithm tests different headlines, fonts, text sizes, button placement and colors, and other page elements through constant A/B testing. In addition, the algorithm can check how elements work together or see how the impact varies according to the type of user. The result is optimized web design determined by data rather than intuition.
When you apply machine learning to raw data, it can detect patterns. The algorithm may discover chances to convert customers that you are currently missing or it may show issues with campaigns that you need to fix. Bear in mind, there is a limitation to using machine learning in this way: the algorithm will give you no indication as to how to fix issues. For that, you do need human input.
Finally, machine learning can determine whether a lead is worth pursuing or if it would be a waste of resources. The algorithm does this by taking all the available information about the lead and deciding how closely the lead matches a profile of existing customers. The algorithm will look at characteristics like location, demographics, and behavior on your website.
In the case that a lead has a high potential of becoming a paying customer, you know that is worthwhile continuing your nurturing efforts. If there is a low chance, you’re better off making no further effort.
The best algorithms for this keep learning. For example, if the algorithm made a prediction that a certain user was unlikely to convert but the user did in fact convert, it will change the way it makes future calculations.
Which Machine Learning Algorithm to Use
When you start researching machine learning algorithms, you may feel overwhelmed by the sheer number of possibilities. You need to narrow down your choices to ensure that you choose the right machine learning technology for your needs.
Focus of the Algorithm
As you saw above, some algorithms increase conversion rate by gathering the data you need to make better decisions. Others score leads and yet others work to improve a particular metric or optimize features of your website. You need to decide what relates most closely to your goals or assess where you are currently struggling to ensure you make the biggest impact on CRO.
Personalization for Users
An algorithm that optimizes experiences for all visitors can be useful. However, a still more effective algorithm will utilize data about user groups and develop the best experience for a subset of visitors or for each individual. In fact, if you’re seeking an algorithm for anything other than design, data mining, or chatbots, individualization is critical.
Some algorithms learn by using preset rules about attributes you determine. Others learn to create their own rules. It is better to have the latter, but you may also like to have the system to accept rules you set.
Manual or Automatic Learning
Ideally, an algorithm will be able to take data you already have and use data it collects in real time. However, some algorithms require you to provide them with a data set and then update this data on a regular basis. This means that the algorithm is slower to learn — and it is critical that you do remember to provide it with fresh data.
Lastly, you need to know that your algorithm is making smart choices; otherwise, it may have no impact — or you could even see negative results to your conversion rate. A transparent system will allow you access to your data, show you why the algorithm is making particular decisions, and explain the subsequent results on conversion rate.
How to Use Machine Learning
How you apply machine learning will depend on several factors, including where you are currently seeing a drop-off in conversions, what are the biggest struggles for your business, and what kind of products or services you offer.
If you have plenty of data, there is no reason why you cannot benefit from all the machine learning projects above. On the flip side, if you have a new website and a limited number of visitors, you have a low level of data maturity. This means that you’ll be unable to take advantage of most machine learning tools.
In fact, if you try to apply machine learning before you have sufficient, quality data, the algorithm could make mistakes that cause your conversion rate to drop. This is because you’ll be making decisions without looking at the full picture. For instance, if you were to apply the attribution model tool from Google without the correct setup, you could risk losing out on conversions from some data sources.
It is difficult to get started if you have inadequate data or you lack knowledge of how to apply machine learning to your data. However, there is a solution: conversion rate optimization services. With full service CRO, you’ll receive the optimization strategy necessary to gather the right quality and quantity of data. Only then will the service implement the most appropriate AI algorithm for your needs.