Transitioning from A/B Testing to Artificial Intelligence: Exploring Differences, Similarities, and Strategies
In e-commerce, as in every business, marketing aims to drive sales and increase profits. With the rise of digital marketing, A/B testing emerged as a standard to optimize marketing content. A/B testing helps teams target market segments more effectively.
Newer on the digital marketing scene, artificial intelligence (AI) and machine learning (ML) take data analysis to a higher level of operation. They let us customize marketing by the individual customer rather than market segment.
With AI, companies can integrate marketing data from every campaign across every format to achieve a specialized level of knowledge never before available on this scale. While AI presents unparalleled customization, the science of split testing is time-honored, and there are applications where A/B testing is the right tool. In this article, we look at the best uses of A/B testing and AI as applied to e-commerce marketing.
What is A/B Testing?
Split testing, or A/B testing, compares two versions of a web page, ad, or application to determine which performs better. It helps your marketing team verify whether changes to marketing content improves conversions or if it was better the first way.
Multivariate tests and other variations increase in complexity and scope, but all split testing is designed to pick the best available option for a target market segment.
When Does A/B Testing Make Sense for E-Commerce Marketing?
A brand new business with two possible ad formats and a target audience can get started with A/B testing.
To see the return on marketing investment (ROI) with split testing requires consistency and strategy. The best results come from repeating the steps: research, experiment, learn and repeat. A/B testing is an iterative process with each new test adding new information. It removes your marketing team’s bias and shows what your audience actually prefers.
Here are typical e-commerce marketing applications of A/B testing:
- Headlines
- Call to Action (CTA) Text or Color
- Color Scheme
- Layout or Placement of Key Elements
- Checkout Process
- Images
- Product Pages
- Navigation Menus
- Quick Product Views
- Upsell Options
A/B tests are a handy tool to improve digital marketing performance.
Limitations of A/B Testing
For accurate results and to keep as many factors constant as possible, split testing requires a large sample size in a short window of time. For email A/B tests, the sample size should be a minimum of 1,000 – 5,000 contacts. E-commerce websites need at least 20,000 – 30,000 unique monthly visitors to avoid investing in paid traffic.
Acting on an insufficient sample size can hurt sales rather than help them. There are natural fluctuations in the data as it’s accumulated. Early on, one option may seem to test higher even though the final results put it behind.
Aiming at too general of a population can muddy test results, while a well-defined target audience improves them. The more refined understanding your company has of a particular segment, the better you can customize to fit them. Accurately customizing by customer segment can be data-heavy and laborious.
E-commerce Case Study: A/B Testing
By simplifying a website banner through repeated split testing, the insurance company Humana increased click-through-rates by 433 percent. The initial banner included an image, a headline, text, and a bullet list. It had a number in the headline and was information-rich, but was not converting the way they had hoped.
Over a series of A/B tests, the marketing team simplified the banner. They removed most of the text, changed the CTA, and changed both the image and the color scheme. After simplifying the copy and changing the image and color scheme, results skyrocketed by 433 percent. The change in the CTA increased conversions an additional 192 percent.
What is AI Marketing?
AI marketing removes the tedium from analyzing vast masses of data. It improves every step of the customer journey by customizing it. Through AI, companies can deliver an automated, tailored message at the right moment. As with any powerful tool, AI performs best in the hands of experts and according to your company’s marketing plan. AI is here to augment your marketing team and execute tactical tasks.
Is Machine Learning AI?
ML is a subset within AI that automates model building. It learns and improves over time. Within goals and parameters defined by your marketing team, ML executes customized communications with active customers without active human input. ML is changing the content marketing game by making a quick sense of enormous data repositories. It identifies trends for uncanny predictions of likely preferences and responses. With help from our experts at MorphL, AI delivers a new world of customization, personalization, and relevance.
At a Glance: A/B Testing vs. AI Marketing
| A/B Testing | AI Marketing & ML | |
|---|---|---|
| Summary | Manual iterative comparing content options generated by the marketing team. | Automated iterative process integrating multiple data sources and learning with each new data set. |
| Best Application | For quick answers on well-researched and formulated webpage, headline, or other marketing options. | For ongoing maximization of marketing funnels, the sales process, and profitability. |
| Strengths | Easily determines which design choice performs better. | Customizes and automates customer experience. Highly targeted segmentation. |
| Limitations | Tests a single to a handful of hypotheses at a time. Limited by human calculations. | Most effective once a company has collected significant customer data. |
How Does AI Revolutionize E-Commerce Marketing?
Higher personalization leads to better outcomes. ML and AI create customer interactions that are timely, contextual, and relevant. AI can model:
- Digital body languages like hover, scroll, and clicks
- Historical behavior from previous visits by a specific customer
- Micro-interaction patterns a human observer would not notice
- Type of device used, automatically optimizing for code and content
- Next click optimizations
AI’s insightful analysis digs deep into keyword searches, social profiles, and other data to create smarter ads, individualized content delivery, and optimized user experience.
Applications for AI Marketing and ML
With AI, you can deliver crafted content and irresistible calls to action at the right moment and with a customer-tailored tone. Each time a person engages with your content, ML converts that new data into insights to optimize your campaign further. The results are more engaging and meaningful content for your buyers and incremental improvements in your sales process. Common e-commerce applications of AI marketing tools include:
- Increase return on Google ads through understanding user intent
- Boost revenues by targeting lookalike audiences
- Reduce customer attrition in Shopify and Klaviyo
- Personalize the customer shopping experience
- Personalize landing pages to align with user intent
- Automate customized product recommendations
- Retarget desirable customers
Similar to A/B testing, the best use of ML insights is not a static glance at your data at a point in time. Real and valuable forward momentum comes through the consistent and strategic application of AI marketing tools.
Over time, ML maps out smarter insights, refined patterns, and detailed trends. It provides a hyper-segmentation at any scale and across every industry. AI integrates into your e-commerce marketing strategy. Advances in ML technology mean you do not need a full-time data scientist or an expanded in-house marketing team to reap the benefits. We can tailor campaigns pre-filtered to capture actionable trends. This could include clicks, actions with emails, behavioral data, and engagement across the web with websites, ads, and social media platforms. Once compiled, we deliver it to you in easy-to-use formats.
E-commerce Case Study: AI Marketing
One powerful application of AI Marketing we use with our e-commerce customers is to upsell the right customer at the right time. The customization and automation of effective customer experience are what make AI seem like magic.
Starbucks is a visible example of the AI customized customer experience. The Starbucks Rewards and Mobile App has over 17 million active users, which fuels a massive data campaign that the company mines with AI and ML in several ways. Starbucks uses AI and ML to:
- Personalize the user experience
- Create targeted marketing based on past actions and preferences
- Allow mobile ordering
- Determine the viability of new potential store locations, including inside grocery stores
- Menu updates to highlight top sellers by location
A/B Testing and AI Marketing Work Together
For any established e-commerce businesses with existing data sources, integrating AI into your marketing strategy means you can target tighter customer segments to create a more personalized and helpful customer experience. Your team focus on building lead-generating content.
When applied to key steps of the A/B testing process, ML gives marketers a better understanding of what audiences want. What emerges is improved segmentation and meaningful A/B experimentation. For example, a shoe company wants to improve conversions within a target age group. Before beginning writing content and creating designs, they decide to use ML to learn more about their desired demographic.
Through ML, they identify three pain points where their target customer tends to leave the sales funnel. For each pain point, they implement a series of A/B tests to find out what improves conversions. The result? A sales funnel that better supports their target audience through the sales process.
Another way ML improves split testing is by automating the repetitive processes required for fruitful insights. The ML algorithms can perform iterative A/B tests and deliver more complete and accurate data.
The digital world demands that successful companies evolve with technological opportunities. AI marketing accesses and organizes data that is already there, automates essential steps, and helps your marketing team remove pain points to accelerate conversions at every point of the sales funnel.
E-commerce Case Study: AI Marketing, ML, and A/B Testing
eBay is a well-known user of AI and ML. With their volume of users skyrocketing, they needed an automated email generation process that still made each customer feel important. Hiring copywriters worldwide to process emails for millions of users and billions of projects was not feasible, but cookie-cutter emails eroded trust with their customers.
They turned to an AI-powered copywriting tool. ML enabled them to set parameters for brand voice and automate the process of writing optimized email subject lines and Facebook ads. Before launching the tool, eBay automated a cascade of A/B tests to refine the subject line, email, and ad copy. The A/B testing provided valuable input to the AI marketing database and helped ensure natural language use that satisfied eBay customers.
Business Stages for Each Marketing Tool
A startup e-commerce business with fewer than 1,000 contacts or fewer than 10,000 unique monthly website visitors will want to focus on building their list and driving traffic to their website. A business that meets those minimums but does not yet have much customer data may want to implement a system for A/B testing and develop its best practices.
When they are ready for the next step of integrating AI marketing, they can advance from a solid foundation of testing and experimentation. For any business with traffic and data, AI and ML are an essential tool. The insights provided support intelligent decisions and sustainable growth.
Is your business in a position to reap the benefits of AI?
What Do You Know About Your Customers?
In the past two decades, online shopping and digital marketing have transformed the world of advertising. Social media provides unprecedented direct access to active consumers as well as new opportunities for social proof.
Online analytics and call tracking integrate to reveal multi-channel feedback from multiple marketing campaigns and provide companies with more raw data than ever before. Now, AI marketing and ML are evolving established split testing strategies into their digital potential. AI marketing permits companies to respond dynamically to each customer individually, a feat that would be impossible to maintain or scale manually.
MorphL helps e-commerce companies maximize the value they provide to their customers and the ROI by infusing AI into their e-commerce flow and personalizing their customers’ buying experiences.
As an e-commerce manager, you can choose to target specific actions at any point in the sales funnel, to improve conversions, or to encourage valuable customers to return. At MorphL, we can also help you get up and running quickly with cloud-based one-to-one customer personalization at scale.
Benefits MorphL AI brings to your business include:
- Hyper-Personalized Experiences – customers are more likely to buy from brands that provide a personalized experience.
- Automated E-commerce Flows – more customer interactions without human intervention.
- Data-Informed Decisions – data-informed decisions and design strategies drive repeat purchases.
