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Landing page optimization has become an essential process in modern marketing to boost engagement, conversion, and overall company effectiveness. A/B testing is a widely used strategy for landing page optimization; however, this test operates from an old-fashioned marketer’s perspective and relies on a time-consuming process and human-generated findings and conclusions to make assessments. Yet with machine learning entering the world as a consistent, reliable approach, A/B testing undergoes transformation to render faster, more efficient, and more meaningful discoveries during the optimization process.
What is Machine Learning as it Relates to A/B Testing?
Machine learning is the use of algorithms and statistical models by a computer system to perform a task without explicit instructions from a human. It relies on patterns and inference from data, and when related to A/B testing, machine learning algorithms automatically detect and assess the results of user behavior within varying pages. For those focused on growing your own SaaS, machine learning offers a major advantage: it takes little time to assess the small differences in performance over time because it can do so quickly, meaning less time determining what’s best for conversion and engagement can come from page elements.
The Advantage of Automated Results
One of the greatest benefits machine learning brings to A/B testing is automated results. When humans are behind determining A/B testing results, a marketer must observe and track learnings over time and apply those figuratively and literally with opinion as they attempt the next A/B testing round. Yet machine learning does its data analysis at speeds never before seen, rendering results almost immediately from thousands of learned and tracked interactions and detecting what variants of a landing page work better almost instantaneously. The newfound successful designs are applied sooner for quicker conversion, saving time for the entire optimization process.
Improved Prediction Potential
Machine learning offers the possibility of more productive predictions throughout the A/B testing process. Since machine learning draws from not only past data but real-time interaction data as well, it has a wealth of information on which to draw for accurate predictions of what users want to see. Through advanced patterns and observations, it can determine which buttons, headlines, or calls to action are more likely to resonate with certain audience segments. From this perspective, marketers will gain accurate predictive capabilities since the landing pages will always reflect what the users want instead of educated guesses and an assured improved conversion rate.
Dynamic Personalization of Landing Pages
Another way in which machine learning assists in optimizing landing pages is through dynamic personalization via A/B testing. A/B testing can seem rather static and a bit limiting as it’s based on fixed options over time; however, machine learning can assess engagement fast enough to learn how to personalize the page via an algorithm based on what the visitors are doing while they are on the page. This creates a heightened sense of personalization, which translates to better engagement and conversion rates as users are more likely to respond favorably to the information they see.
Conversion Rates Increased By Ongoing Optimization
Ongoing optimization is yet another perk that machine learning can provide. Instead of optimizing based upon what small, incremental tests may determine at certain points in time, machine learning can determine what’s working or not based upon activity and interaction in the moment. Essentially, as people try to convert on landing pages, machine learning can learn from their feedback as they may have to search for something else on the page. Subsequently, landing pages can always be in a state to address what people require in the moment, whether it’s for fleeting trends, offerings by competitors, or changes within the field that could negatively impact conversion rates if not processed immediately.
Fast Identification of Winning Combinations
Winning combinations can be discovered quickly thanks to machine learning’s ability to assess multiple variables at once. For instance, rather than A/B testing different versions of a headline or color schemes of a call-to-action button, machine learning can assess the headlines simultaneously with button color schemes, images, and placements of calls to action all at once. This multivariate approach increases the confidence level of finding optimal outcomes because, instead of A/B testing, it reveals the complex relationships between elements for an immediate increase in landing page effectiveness.
Reducing Human Bias and Errors
Because A/B testing analysis is read and interpreted by humans, there’s an element of human bias and error or assumptions that could lead to faulty findings or misinformed action. Machine learning negates this issue as it doesn’t provide suggestive answers based on human or manufacturer bias; instead, it relies upon the concrete, empirical results of the trends it finds within the data. This means that marketers have more reliable actions to take and can trust their action paths, increasing better, more accurate optimization strategies.
Better Audience Segmentation with Machine Learning
Audience segmentation efforts via A/B testing are enhanced exponentially with machine learning. Machine learning can analyze vast amounts of data pertaining to users, which enables it to segment visitors into defined, actionable segments based on behavior, interest, and conversion potential. With this level of segmentation, marketers can then create more relevant landing pages for various audience segments, which increases relevance to the user, greater engagement, and more chances of conversion.
Cost and Resource Efficient Testing
Yet another way that machine learning in A/B testing increases efficiency is by being resource efficient. When A/B testing is completed with machine learning, a lot of the work is done automatically, which reduces the need for manual reporting and data collection. This means that marketers can spend their time and energy on more strategic initiatives as opposed to being bogged down with data, redundancies, and research. Additionally, the less time spent on A/B testing means decreased costs associated with running many different A/B tests over time. Efficiencies in resource spend allow for scalable efforts with no added costs.
Predicting Market Changes and Trends
The ability to perform A/B testing through machine learning not only provides results that are optimized in the moment, but it also allows marketers to predict and respond to changing market trends and developments. Over time, through continuous real-time evaluation, machine learning can flag new trends in visitor engagement and feelings as well as brand competition. This data allows marketers to get a better picture of evolving visitor needs and wants, enabling them to adjust for landing page engagement sooner than later so they are always one step ahead of the competition and providing the best experience.
The Ability to Optimize at Scale
Machine learning also enables the ease of scaling A/B testing. Since the analysis and testing are done automatically, marketers can run A/B tests concurrently across multiple landing pages and across multiple branded campaigns. There need never be any additional staffing or resources required to compensate for intensive optimization, as machine learning allows companies to constantly optimize across extensive digital marketing portfolios without needing to increase time or exposure efforts proportionately. Machine learning easily facilitates scaling and efficiency irrespective of size.
Human Intelligence Still Needed but Strategically
Despite machine learning creating a faster and more accurate opportunity for A/B testing assessment and implementation, there still needs to be a human component to maintain sustainable effectiveness. Marketers must collaborate with algorithms to credit expected goals and limits while still allowing for creative energy. Human intelligence is still necessary to use machine learning as a catalyst for A/B testing engagement rather than a substitute for creative license, instinct, and managerial decision-making for ultimate success.
Enhancing User Experience Through Real-Time Enhancements
Machine learning significantly enhances the user experience because it allows for real-time enhancements of landing page elements through A/B testing. For instance, algorithms monitor user interaction and behavior continuously. Even if something isn’t working as expected, for example, a headline not getting the first attention it’s detectable in real time. Machine learning will find another headline alternative to swap out and enhance a user experience before that user leaves. This kind of engagement ensures users are engaging with desirable elements and having a great time, only increasing time on site and likelihood for conversion.
Reinforcing Brand Consistency Across Tests
Brand consistency is one of the more challenging aspects to maintain when A/B testing landing pages. So many different campaigns run simultaneously that marketers need to juggle each with its variations and iterations. Previously, without machine learning, marketing analytics and monitoring would be required to determine if colors, fonts, logos, branding messaging, and imagery fit within brand guidelines. The human effort for application could fail and overlook brand requirements instead, confusing potential clients and creating a disjointed, distrusting brand identity.
However, brand consistency can be achieved through machine learning efforts since it reduces and simplifies this effort almost entirely via automated, intelligence-based efforts that protect. Machine learning harnesses the power of historical assessments over time to know what elements work best for branding and what has or has not worked for particular audiences across different campaigns; thus, it can apply similar techniques moving forward. For example, machine learning can easily recognize and access important visual congruences matching color schemes with brand-specific images and the associated tone of voice to create effective landing page variations with minimal human access needed from marketers.
Furthermore, machine learning continuously assesses A/B tests and landing pages in real time. If something is not working for a particular audience or if it goes against brand guidelines, machine learning can almost instantly assess this and make adjustments on a dime, changing the other components of the new A/B element to comply again with brand standards or compromised community expectations. This cuts down the need for marketers constantly to assess their work manually when their time can be better spent on strategic moves instead of mundane monitoring.
Therefore, the confidence that machine learning provides regarding A/B tested landing pages with branding compliance bolsters user perception for users, consistent branding means reliability, professionalism, and authenticity. If someone sees one thing one day and something else another day, they’re less likely to believe they’ll have a consistent experience with the brand; instead, they’ll think they, too, might be getting a bait-and-switch or worse, a subpar experience compared to others they’ve experienced. Machine learning fixes this situation and in the long run, provides better prospects for brand identity recognition which brings better loyalty over time from consumers who value brands that know what they’re doing versus brands that are inconsistent with their mistakes. In short, using machine learning for this branding consistency sets the metrics up not only for now but also for successful revenue-generating efforts down the line.
Conclusion
Machine learning is changing the landing page optimization experience through A/B testing in such a way that marketers now find and use high-performing aspects of pages with unprecedented ease. First, machine learning fosters an easier approach to testing that eliminates potentially tedious steps in the review process. After A/B testing occurs, there is usually a need for marketers to backtrack and compare results manually with other designs or attempts. Machine learning, however, uses algorithms that process and compare vast amounts of data at a speed far exceeding human capability. Thus, marketers can quickly and easily implement recommended uses for high-performing design alternatives, messaging approaches, or even content configurations than ever before.
Second, A/B testing with machine learning presents a deeper understanding of performance through a predictive approach. Machine learning algorithms have extensive access to historical data taken from years of similar marketing attempts and successes as well as real-time reporting of users on the landing pages. Through pattern acknowledgment, machine learning can accurately predict how a user might act, what works best for them, where they will go next after hitting the landing page, and how they respond to elements contrasting other attempts upon entering the page. The accuracy allows marketers to adjust and create changes in advance so that all aspects of the landing page from headings to images to calls to action are precisely what users expect and need.
Finally, with ongoing processes for machine learning within A/B testing for landing page optimization, pages can be relevant and responsive for as long as desired. A/B testing does not have to be a one-time fix; often multivariate testing is available through the machines that allow relative efficacy to emerge through repetitive processes. Companies can rely on machine learning to more effectively determine optimal combinations of content over time, creating pages that retain effective qualities instead of going stale when new trends emerge. Businesses that leverage such opportunities offer their audiences unique and highly valuable experiences that not only generate fleeting competitive advantages but also foster customer retention and effective long-term business efforts.