“Our goal is to help more marketers deliver more revenue, more customers, and more leads to sales,” Yalif told VentureBeat. “We will help more conversion-obsessed marketers dynamically adjust their websites to each unique visitor’s changing behavior over time.”
“One-size-fits-all websites are the biggest squandered opportunity in all of marketing for all industries … Intelligent website optimization is essential tech to the modern marketing stack, and our investors share this sentiment,” he continued.
“[Over the past year,] we’ve been busy helping marketers create high converting websites by combining their ideas with our machine learning,” Yalif added. “We’ve become especially popular with business-to-business brands along with ecommerce. For example, we helped Snowflake generate 49% more leads with their website, while Sumo Logic accelerated decades of traditional testing and optimized across more than 1 billion versions of their site.”
A/B testing is a statistical methodology of comparing different versions of something to see which version performs better. While A/B testing is a scientific approach to problem solving that has been in use for nearly a century, the technique was first adopted by marketers as early as the 1960’s in direct marketing campaigns. But with the rise of the digital era, A/B testing has surged in popularity, partly because launching and analyzing experiments in the online world is relatively easy.
In the context of digital marketing, conversion-obsessed marketers will test different versions of webpages, email headlines, landing pages, ad copy and other user-facing online content to determine which performs better. There are many webpage components that can be tested for performance, including page layout, menu location, headlines, CTAs, images, fonts, colors, image sizes… the list is nearly endless. Results and conversions can differ significantly depending on the combination of elements and the audience.
Why A/B Test?
A/B testing provides a definitive, data-driven approach to determining which version of online content performs better, that is both statistically valid and scientifically sound. In short, A/B testing takes the guesswork out of marketing, replacing subjective decision making with an objective framework for determining winners and losers.
Because the guesswork has been reduced or eliminated, marketers and business managers can consistently improve the results and efficiency of their marketing efforts, or business operations, over time by employing a systematic approach to A/B testing. Often the result of ongoing A/B testing and experimentation is a dramatic improvement in marketing effectiveness and advertising ROI, and sometimes is the difference between the success and failure of a marketing campaign or even a business.
There are many A/B testing tools and software solutions today that can help with launching a successful program of experimentation. These testing tools can help marketers sift through the myriad of attributes, data, and options that can otherwise make A/B testing difficult or cumbersome. Their cost and complexity can vary tremendously depending on the size of your website and your firm’s needs.
In addition to learning and understanding the tools available for proper A/B testing, you’ll also need to have a solid understanding of the statistical principles that underpin all of A/B testing. Without a foundational understanding of how to statistically interpret results, you’ll likely encounter errors and make unreliable decisions. Let’s break down the three most important statistical terms you’ll become acquainted with along the way:
- Mean – The mean (or average) is a measure for determining how each variable we test results in something. You’ll want to tabulate the mean click rate or conversion rate depending on what you’re testing.
- Variance – The variance is used to determine the variability of the data being measured. If the variance is low, the more accurate the mean will be. Likewise, if there’s a large variance in what you’re testing, the confidence interval of the sample mean will be larger and less accurate as a measure.
- Sampling – In order for the data to be statistically meaningful, there needs to be a large enough sample size. If we test only a handful of interactions with a particular website test, the sampling might not be large enough to have significance.
Determining the statistical significance of an A/B test is critical, because it is the statistical validity that gives A/B testing its prescriptive power. Without statistically significant results, marketers are at risk of making either Type I (false positive) or Type II (false negative) errors and misinterpreting the results of their tests.
A/B Testing Challenges on Your Website
While A/B testing can play an integral and even essential role in driving marketing results, it is important to acknowledge the inherent challenges of A/B testing, especially for websites, that have led marketers to adopt more advanced ways of intelligently optimizing their website content.
- A/B testing takes too long. With A/B testing, you test one variation at a time. Testing more than one at a time can confound the results of both experiments, undermining the statistical significance and defeating the point of the controlled experiment. And, depending on the amount of traffic your website gets, the results can take time – weeks, perhaps months, and far too often, a clear winner is never identified. In a business environment that often demands results this week, A/B testing can sometimes fall by the wayside.
- A/B testing ignores smaller segments. A/B testing doesn’t take into consideration each unique visitor, but rather groups visitors into large randomly assigned segments. If one variation outperforms another 60% to 40% (as an example) the winning variation may be more effective for a majority of your website visitors, but there may still be a segment in your overall population that would respond better to the losing variation. In A/B testing, no allowance is made for serving different variations to different segments over time, instead a winning variation is chosen and served to all future web visitors.
- A/B testing takes a lot of work. A/B testing requires marketers to closely monitor metrics, measure improvements, and update the website with new testing variations. This often requires the resources of web developers, programmers, graphic designers, possibly legal and regulatory approval groups within an organization to make site changes.
- A/B testing misses opportunities. An A/B test gathers data by serving one variation that will (hopefully) win and one version that will (presumably) lose to 50% of your selected website traffic users each. While the experiment is gathering data, your website is missing out on conversions from the half of your audience that is viewing the future losing variation. Over time, these missed opportunities will add up. And while A/B testing is better than not testing, A/B testing lacks the efficiency that is delivered by today’s machine learning and artificial intelligence solutions.
- Statistical Complexity. Without a deep understanding of statistical principles, marketers can often come to false conclusions by mis-interpreting the results of an A/B test. Ensuring experimental validity, reaching statistical significance and analyzing statistical confidence & power can be a high bar that trips up even the most experienced conversion optimizers.
Introducing Continuous Conversion™
Intellimize is pioneering new advancements in the industry by introducing Continuous Conversion™, a machine learning optimization approach that outperforms A/B testing. While A/B testing is cumbersome and slow, Continuous Conversion™ is fast and efficient, delivering head-turning results in days not weeks.
Intellimize uses machine learning to optimize the individual steps of each buyer’s unique journey in real time and automatically adjusts web content in response to changes in the buyer behavior over time, delivering better conversion results as much as 25x faster, empowering marketers to test more ideas, faster.
The Results of Continuous Conversion™
Intelligent website optimization can help improve lift and conversions for websites across a host of industries. Here are some use cases of how machine learning can power your website performance improvements.
- B2B – See how Snowflake drove a 49% uplift in meetings booked using Continuous Conversion™ for their website.
- B2C eCommerce – See how Stella & Dot achieved a 52% increase in shopping cart conversions by testing 400 combinations of their checkout page in just a few months.
Recommended Resources – Intelligent Website Automation vs. AB Testing
Here are recommended resources on how and why Intelligent Website Automation outperforms A/B testing.
- Video: Which is better for me? A/B testing, rules-based personalization, or predictive personalization?
Conversion Rate Optimization (CRO)
In the context of digital marketing, conversion rate optimization – CRO – is the process of increasing the percentage of web site visitors that take a desired action or conversion goal. That desired action could be a purchase, a free trial sign up, a demo request, a content download, or even just spending more time on a specific page. Because online audiences (and marketing spending) can be so large, relatively small improvements in conversion rates can sometimes produce significant impacts on business performance and financial outcomes. In fact, CRO can be one of the highest ROI activities in a marketer’s toolkit and can dramatically impact successful customer acquisition through refinements in the conversion funnel.
While website conversion rate optimization relies heavily on analytics and statistical approaches to decision making, CRO is fundamentally about consumer insight and understanding prospective buyers – something that creative marketers are uniquely positioned and qualified to do. Conversion-obsessed marketers use an array of tools – web analytics, heatmaps, session recordings, surveys, user experience testing & customer interviews – to understand how and why consumers behave in the ways that they do.
Using this research, optimization experts develop a list of hypotheses on what may cause web users to change their behavior and complete the conversion goal more often. Then, borrowing from the tradition of scientific research, marketers test each hypothesis in a statistical A/B test using A/B testing software to randomly deliver different versions of a web page to individual users while measuring the results. Soon the level of complexity rose and marketers were embracing multivariate testing, which involved multiple iterations of several different content variations in a single experiment. Eventually marketers would also target specific content to unique, identifiable audience segments through a technique called “Personalization.” But all of these different approaches were focused on refining the conversion funnel and delivering more value for the traffic on a company’s website.
By methodically testing each hypothesis and incorporating winning iterations into the overall web design, a website’s conversion rate will rise over time, increasing the value of the website traffic for the business.
The Importance of Conversion Rate Optimization
Marketers today invest a tremendous amount of money and resources into driving to their websites. By making sometimes minor changes to their , marketers can improve conversion rates and the revenue that flows from those additional conversions, resulting in greater returns on the initial investments.
Companies with high converting website conversion rates stand at a competitive advantage to their competitors with lower conversion rates. Because the firm is able to produce a more profitable return on their investments, the company is then able to reinvest more of those profits back into acquisition, producing even more returns and a virtuous cycle of growth continues. This is especially true in cases where paid advertising is a main driver of and improving efficiency is an imperative.
One point to note, conversions are the lifeblood of any online business, though how they are defined and measured differ from industry to industry. An online might define a conversion as an order or sale. A B2B technology company might define a conversion as a form fill for a white paper download which generates a sales lead. While the process in each of these industries is different, the importance of generating conversions is the same.
The Building Blocks of Successful CRO – What to Test
In a recent research from Forrester and SiriusDecisions, they define three key patterns that digital marketers must follow for website and conversion rate optimization success in today’s online environment.
- Attract – marketers must capture and direct the user’s attention. Evaluate the content on your own site by type:
- Visuals – imagery, color, overall design, and emotional resonance. How these visual components are presented to the site visitor can determine whether they leave or convert.
- Copy – headlines and body copy that connect the site visitor to your value proposition. Refining your story is critical to earning your next customer.
- Calls to Action – a cta button, banners, links, and other similar calls to action move the user through the intended customer journey. How you choose and frame an offer can make all the difference between success and failure.
- Usability and User Experience – unifying the previous elements in an experience that is pleasant for the site visitor helps improve the website.
- Engage – your website, as an important representation of your company or brand, must build trust with your audience. By aligning content to a user’s intent or goal for visiting a website, smart marketers build credibility. This helps facilitate future visits, purchase decisions, and long-term loyalty. The components that are put to work here include:
- Navigation – site visitors can move throughout the site content smoothly with clear navigation that facilitates this process.
- In-depth Content – site content should demonstrate expertise relative to the company’s value proposition. It should also be consumable in different formats – such as text, video, etc. – and speak to visitors at various stages of the buying process.
- Personalization – those sites that speak to the unique needs or pain points of the website visitor will resonate more with prospects and potential customers.
- Convert – While there’s a lot that goes into a quality conversion, building a deep understanding of consumers and what motivates them is critical. Psychological approaches, such as social proof, expertise & authority, also play an important role in the CRO strategy.
- Offering Alignment – aligning the user experience with the site, product, or sales offering is critical. Content delivered along a “buyer’s journey” is an example of offering alignment.
- Decision Justification – leverage confirmation bias and micro-conversions as steps toward a larger goal.
- Paths to Conversion – Some people convert faster than others and thus need a different route to conversion. Explore content formats and purchase paths customized to industry or persona.
Traditional A/B Testing – Is there Time to Manage the Complexity?
In the early days of digital marketing, A/B testing emerged as a winning solution to provide a definitive answer to a simple question – this one or that one? The statistical and scientific rigor of the process reinforced the credibility of data-driven decision making, and online content was forever changed for the better. Testing one landing page iteration against another landing page iteration was and is the foundation of conversion rate optimization today.
However, A/B testing software soon emerged to simplify the work of tracking experiments, measuring performance, and testing statistical significance, leading to a second revolution on the timeline of conversion rate optimization. For a new generation of digital marketers, it was now possible (and easy) to create variations, launch an experiment and report on the results.
But the complexity of managing an online optimization program has grown exponentially as the internet has grown and as the way we experience online content has evolved. Today, growth and experimentation-focused marketers must be cognizant not only of sample sizes and confidence intervals, but also multivariate experimentation, bayesian vs. frequentist approaches, cohort performance, test velocity, win rate, and experimental capacity. All while optimizing over dozens, if not hundreds, of pages & products, ensuring that no one experiment confounds the results of another and tracking the results of each one.
It is in part due to this complexity that A/B testing has struggled to reach widespread adoption in the industry, though A/B testing software is widely available and even free now. While some firms have embraced the mantra of experimentation, other conversion optimization managers note challenges like organizational alignment, conflicts with experimental mindset and time to impact.
Despite these challenges, industry titans, like Amazon, Google, Facebook, and others, highlight experimentation and optimization as foundational elements not only of their online experience but also of their competitive strategy.
With the rise of new technologies, like machine learning & AI, teams from the world’s leading companies have developed a new suite of tools to address the challenges of website optimization, leading to a third revolution in the timeline of website optimization – and turning skeptics into believers with some head turning results.
Intelligent Website Optimization Delivers Faster Results
The third revolution in conversion optimization is now available to the world’s most conversion-obsessed marketers. Intellimize has introduced artificial intelligence and machine learning into the conversion optimization dynamic, outperforming traditional A/B testing and personalization, and delivering better results, faster.
Intellimize uses machine learning to deliver a uniquely crafted web page to each and every visitor, assembled from the dozens or hundreds of variations envisioned by insightful marketers. Using powerful algorithms, Intellimize selects each specific element of content based on all of the available known data – including the user’s behavior.
Then Intellimize beautifully presents the best version of your website to each visitor, delivering a world-class experience for them – and game changing results for you.
Intellimize CRO in Action
Conversion rate optimization can make or break a website’s, and even a company’s, success. With Intellimize’s intelligent website optimization, you can transform your website into a conversion machine that drives results 25X faster than traditional A/B testing. Here are some case studies on how Intellimize has worked for other marketers:
- B2B – See how Drift tried out 25,000 different variations of one of their landing pages, and ended up seeing a 322% improvement, driving higher-quality leads and more sales.
- B2C eCommerce – See how Dermalogica leveraged Intellimize to test over 450 variations on their website, increase revenue, and grow their business across 6 global markets.
Recommended Resources – Conversion Rate Optimization (CRO)
Here are some additional resources on how you can elevate your CRO efforts: