4 Ways Artificial Intelligence Can Impact Your Conversion Rates

At this point, there is little doubt that artificial intelligence is the future of business. The Salesforce "State of Marketing" report found that more than a fifth of businesses currently use AI for marketing purposes, including programmatic buying, personalization, and real-time offers.

Credit: Salesforce

Further, AI is the fastest-growing sales technology, according to the Salesforce "State of Sales" report.

Outside of sales and marketing, companies are frequently using artificial business intelligence for tasks like reporting, dashboards, and data warehousing and analytics.

While applying AI to these business operations is certainly beneficial, it does beg the question of how exactly this technology will impact the future of conversion optimization, as well as the most important person in a business: the customer.

At the end of the day, the thing that really matters in business is the numbers. AI technology for analyst reports and predicting turns in the market is all well and good, but if it isn’t boosting sales, then what is the point?

The good news is that AI is showing promising results in terms of conversion rates, proving once again that big data is paving the way to a more profitable future for many companies. Here’s how.

1. Enriches Customer Experience

The concept of improving the customer experience (CX) is a big challenge for many reasons. CX is not merely limited to the user-friendliness of a website or the customer service that is provided; it is a combination of all of these elements. Yet another report from Salesforce found that consistency is a core element in a positive customer experience, and 70% of customers say connected processes based on earlier interactions and contextualized engagement are important for them to do business with a company.

This means that in order to improve the CX for customers, brands must adjust every part of the experience to create a coherent message.

Studies have found that customers are willing to pay more for a better experience with a business. It also has a strong effect on their likelihood to repurchase and refer the product or company to friends.

Credit: Temkin Group

But what exactly makes up “customer experience” and where does AI fit in?

CX is essentially the accumulation of every interaction a customer has with a business, from introduction, to purchase, to customer service. As experienced business owners know, one small kink in the journey can send people running. AI and machine learning technology can help create a more optimized experience for each customer, from start to finish.

For example, when fashion brand FlyPolar experienced a near 400% decrease in sales in the span of just four months, the business executives knew that something wasn’t right. Because most of its customers purchased online, FlyPolar used AI software to optimize its website landing pages. By using machine learning technology, this AI program “learned” which designs performed best and delivered positive results.

After several weeks of testing, the AI system identified the core roots of the conversion problems and provided the proper insights for solutions. FlyPolar created a simpler four-step conversion funnel on its website, with optimized CTA button placement throughout the landing pages. By using machine learning algorithms, FlyPolar increased its checkout page traffic by 16% and its order value by 13% in just three weeks.

This case study shows that AI technology can quickly and easily identify the root of the problem, arguably one of the most difficult parts of optimizing the CX.

The prediction capabilities of AI-powered systems can also make it easier for your customers to find exactly what they are looking for; which, in turn, improves their experience with your website. Traditional searches base results on matching keywords or similar phrases, which may or may not be accurate. In contrast, present-day search programs use ML to “learn” consumer behavior and accurately return the items that match their queries, based on their previous behavior.

ML-based search takes numerous data points into consideration, including past view and click rates, ratings, and even inventory levels to provide customers with appropriate and targeted results.

It should be no surprise here that Amazon is one of the leading retailers to utilize this kind of technology. Amazon’s recommendation engine uses item-to-item collaborative filtering to provide search results that are based on multiple data points, rather than just keyword matches. Not only does the algorithm take each customer’s past searches, purchases, and product views into consideration, but also the ratings and popularity of each item.

Credit: Amazon

Since Amazon debuted an AI-based recommendation engine, its profits started growing exponentially. By basing search results on multiple criteria, Amazon is able to push certain products while providing shoppers with the results that fit their needs, providing a better experience for the customer with each query.

2. Enhances Personalization

Buying online is no longer a one-size-fits-all experience. In fact, customers are becoming more and more unyielding that businesses customize just about everything to fit their needs. According to Accenture’s "Personalization Pulse Check" report, three out of four customers report that they would be more likely to purchase from a brand that offers personalization and recognition than businesses that do not.

Personalization is also directly related to higher profits. Researchers have found that businesses utilizing big data systems to create personalized experiences for their customers report up to 10% higher revenues.

AI is able to take the guesswork out of personalization. One of the best examples of this strategy in action comes from Starbucks, which reported a 300% increase in customer spending thanks to its highly-customized marketing program. Customers regularly receive personalized offers and incentives to earn more points toward a free drink reward. Every customer’s offer is based on past behavior, including how often each customer purchases and which types of items the customer tends to buy.

Starbucks’ AI-powered personalization system sends out around 400,000 variants of emails with incentives that are almost entirely unique for each recipient. Due to the hyper-personalization that Starbucks offers, many customers find it easy to fulfill the requirements for these rewards. This does wonders to increase consumer participation, purchase frequency, and ultimately, customer loyalty.

Credit: Starbucks App

Of course, loads of consumer data are needed in order for online companies to provide this high level of personalization. Each customer’s preferences, demographics, and behavior must be tracked and analyzed in order for brands to properly adjust their strategies to fit an individual consumer.

The results from integrating personalized messaging and marketing speak for themselves: 63% of marketers report that an increase in conversion rates was the top benefit they saw from personalization.

AI-powered personalization can be used to help customers move their way through the buyer’s journey, as well. Using ML, these programs use predictive analysis to incentivize shoppers with personalized messages, email campaigns, retargeted ads, and more.

The algorithms can study consumer behavior so that ads and other messages are sent at the right time and trigger the ideal response. For example, an algorithm that tracks customers’ click rates and scrolling habits can predict when new customers are likely to abandon their carts and send a well-timed message or personal offer to keep them engaged.

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3. Improves Results of A/B Testing

Most marketing teams and web designers rely on A/B testing to determine the best layouts, color schemes, and messaging to grab their customers’ attention. However, there are obvious limits to the “old-fashioned” testing approach. Gathering the research takes time, and there is not always a clear winner from the results.

In fact, the traditional form of this strategy may not even be effective. Jeremy Miller, marketing director at Sentient, said during an interview:

“In traditional A/B testing formats, you have your control vs. an experiment. You run that experiment against your traffic, and whichever design performs better is the one you deploy … but people have found that six out of seven experiments don’t result in a positive outcome, so you actually have to put a lot of energy and resources to try to determine how you can actually increase conversions using A/B testing.”

AI can solve the three biggest problems with traditional A/B testing: time required, insight, and limited variables. By reducing these weaknesses, marketing teams have the ability to make informed design changes with the results and data to support them. Instead of taking a linear approach to testing, AI can compare thousands of variables at the same time and instantly compare the results to determine the best combination.

For example, online lingerie company Cosabella used an AI-driven testing approach when it was redesigning its website. Rather than comparing designs two at a time, like a traditional A/B test would, Cosabella was able to carry out an A/B/n experiment with 160 different design elements, simultaneously. With that many variables, it would have taken up to a year of A/B testing to gather results; with AI, the process took only seven weeks.

Credit: Cosabella.com

Through this testing process, Cosabella was able to determine the aesthetics that resulted in better conversions. It found that customers bought more when CTA buttons were pink, rather than black. The company also determined that family values resonated with its customers, so it did away with “free shipping” banners and replaced them with “Family Owned Since 1983.” After these short seven weeks of testing, Cosabella reported a 38% increase in conversions and a 1,000% lift in newsletter signups.

4. Speeds Up Customer Service

The faster a company can respond to customer inquiries or issues, the better. For this reason, the demand for live chat grew by 8.29% last year. Unfortunately, most businesses do not have the resources to keep their customer service departments running 24/7, leading to long response wait times for disgruntled customers.

By automating customer service with AI-powered chatbots, businesses can not only solve the issue of wait time, but also the quality of the response and assistance that customers receive.

In 2012, Amtrak’s customer service department serviced 30 million passengers each day. Obviously, with such high numbers, it was difficult to handle individual inquiries in a timely manner, so Amtrak decided to jump on the chatbot train with its AI-powered customer service rep “Julie.”

Julie was able to resolve most of these issues by pre-filling forms through scheduling tools and guiding customers step-by-step through the online booking process. Because most of these problems were handled online, the number of calls and emails decreased dramatically. At the end of the first year, Julie had answered over 5 million questions, increased booking rates by 25%, and generated 30% more revenue, thanks to upsell options included in the messaging.

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In terms of conversions, live chatbots can not only resolve issues in an instant, they can increase the chances that a customer decides to buy. When a customer’s issue is solved quickly, they are twice as likely to repurchase from that brand. Live chat is also the preferred method of communication for resolving problems or issues; however, it is important to note that the quality of the messaging far outweighs the speed of the response.

According to Kayako’s report on live chat service, 95% of customers say that receiving a thorough response that answers their question or resolves the problem is more important than just getting a quick reply. This is a major issue that many companies have with AI chatbots; they are simply programmed to give automated, scripted responses, which 29% of customers report as simply frustrating and unhelpful.

This is where AI-based chatbots save the day; they can adjust their messaging based on FAQs, as well as the customer’s phrasing and responses. This process leads to better and more natural replies from bots that delight customers and give them the timely information they need.

An AI chatbot is not a one-time fix to the issue of customer service. It is a strategy that must be properly monitored, adjusted, and perfected over time in order to deliver the best results.

The Wrap

Many conversations these days are revolving around AI and its impact on the future of business. And, quite honestly, it seems like the answer to just about every current business planning issue out there. Predictive analytics can tell you when things are about to change. Machine learning can understand your customers on a personal, granular level, and big data can keep track of every metric for accurate reporting.

However, one of the clearest benefits of AI is the direct impact it can have on conversions. It eliminates the guesswork from improving the CX of webpages and delivers timely and accurate testing results needed to increase the likeliness of conversions. Big data systems and AI make hyper-personalization possible to customize the experience for each visitor. Finally, chatbots can use ML to instantly engage with customers, resolve issues immediately, and close sales.

Success all boils down to how a business makes the customer feel. Most of the time, this is what determines whether or not a customer will purchase. Studies have found, unsurprisingly, that when customers feel special, important, and satisfied, they are more likely to buy from those brands. AI gives brands the power to do just that.