Every step in the CRO process is crafted to convert casual visitors into devoted customers. Join us on a detailed exploration through the stages of CRO, and learn how to boost your website’s conversions.
Stage 1 – Research and data analysis
Conducting thorough research before diving into the actual testing is an important step in the CRO process. Without understanding the existing landscape of your website—its strengths, weaknesses, and the behavior of its visitors—your optimization efforts might not flourish as expected.
Starting with research helps you gather important information. It shows what users like, how they behave, and where they face problems on your website. This information guides your CRO efforts, helping you make changes that truly meet users’ needs and improve their experience on your site.
Quantitative research
Quantitative research acts as the cornerstone that empowers us with data-driven insights. It guides our strategy, enabling us to make informed decisions rooted in numerical evidence, rather than assumptions. Learn more on quantitative research in this comprehensive guide.
Begin by establishing clear objectives for your research. What specific metrics or user behaviors do you aim to uncover? Having a structured approach ensures that the data collected is aligned with your optimization goals. Here are some great metrics to look at that can guide your optimization direction:
New User conversion rate VS Returning User conversion rate: This data provides a high level view on strength, potential challenges, and opportunities for strategic adjustments in both acquisition and retention efforts.
For example, if the new visitor conversion rate is low, you may want to focus your CRO strategy around increasing new acquisition strategies, like optimizing your site for marketing campaigns or promotions. You can test different methods in driving immediate conversions, such as incorporating trust signals, utilizing landing pages, enhance CTAs etc.
Visitor demographics: Enhance your conversion tactics by knowing your audience, considering factors like age, gender, location, and education. The relevance of these details varies with your business nature.
Take a jewelry store, for instance. Assessing metrics related to gender, income bracket, and interests can be pivotal.
Device and Browser Usage: Knowing which devices or browsers your visitors use most helps in improving your website for better use. This knowledge lets you make necessary changes to your website so it works best for your audience.
For example, if most of your website visitors, let’s say 85%, use mobile devices, it’s a good idea to make your website mobile-friendly. On the other hand, if most visitors use a computer, you should ensure your website looks and works well on larger screens.
Traffic Source: Understanding where your traffic comes from is a crucial factor. It’s not just about enhancing what’s on your website, but also being mindful of how users are getting there in the first place. Knowing your traffic sources allows for a more tailored and effective CRO strategy.
For instance, if a majority of your traffic is coming from social media, it would be beneficial to optimize the landing pages that these users first interact with, ensuring that the content and design resonate with a social media audience. Conversely, if most traffic is coming through organic search, focusing on SEO and relevant content becomes imperative. In doing so, you create a roadmap that is not only about improving conversion rates but also seamlessly aligns with your audience’s journey from various traffic sources to your website.
Customer Lifetime Value (CLV): CLV represents the total amount of money a customer is expected to spend on your products or services over their entire life. In essence, it’s a prediction of the net profit attributed to the entire future relationship with a customer.
For instance, consider a subscription-based service like a monthly fitness app. If a customer subscribes for $10/month and continues the subscription for 12 months, the CLV for that customer would be:
$10 subscription fee X 12 months = $120
Understanding the CLV helps the CRO strategists make informed decisions about how much effort to put in acquiring new customers and retaining existing ones.
Other: Other common metrics to look at are bounce rates, page views, and micro conversion rates. These numbers offer a vivid picture of user interactions and areas that require enhancement.
Qualitative research
Qualitative research is a crucial part of Conversion Rate Optimization (CRO). It helps businesses get a closer look at how users interact with their websites, and what they think and feel about their experiences. Unlike numbers and graphs, qualitative research gives us real human feedback and behavior. Here are some effective methods to gather these valuable insights:
Heatmaps: This is essentially a map that shows the hottest (most clicked on) and coldest (least clicked on) parts of your website. Heatmaps help in visually understanding what parts of a website are getting attention and what parts are being ignored.
Surveys and Questionnaires: These are direct questions we ask users to understand their thoughts and preferences. Surveys help in getting opinions and feedback directly from the people who visit and use your website.
User Testing: In user testing, we watch and learn how real people use a website. It helps in spotting problems and areas that can be made easier and more user-friendly.
Session Recordings: This is like a video recording of how visitors browse through your website. It lets businesses see where users spend time and where they face difficulties.
Customer Interviews: Talking directly to customers can give deep insights. Interviews help in understanding the detailed thoughts and decision-making process of users.
Each of these methods allows you to gather practical insights and understand the user’s perspective better.
Stage 2 – Planning – hypothesis and prioritization
In this stage of the conversion rate optimization process, planning takes center stage. Here, two key elements come into play: hypothesis and prioritization.
A well-thought-out hypothesis acts as a starting point, guiding us towards improvements that could enhance our conversion rates. On the other hand, prioritization helps us allocate our resources wisely, focusing on strategies that are likely to have the most significant impact.
In this section, we will dive into the essential steps and strategies in the planning phase.
Creating Hypothesis
A well-formed hypothesis is a cornerstone in the Conversion Rate Optimization (CRO) process. It acts like a guiding star, helping you navigate through your optimization strategies effectively. Here’s how to create a CRO hypothesis that is both powerful and practical:
1. identifying the Issues
In the previous stage, we talked about gathering and analyzing user data to find where users are having trouble or leaving your site. Now we need to identify all the issues and list them out.
Look for common problems or patterns that seem to disrupt the user’s experience. For example, are there pages where users seem to drop off more frequently? Identifying these trouble spots is crucial in building a hypothesis that aims to solve real user challenges.
2. Setting a Clear Goal
Have a clear understanding of what you want to achieve with your optimization with eash hypothesis. It could be improving click through rate, increasing lead generation, increasing engagement or boosting purchases. Setting a defined goal ensures that your hypothesis is focused and aimed at solving specific user issues.
3. Crafting the Hypothesis:
Based on the objective, construct a hypothesis that is precise and rooted in the insights gathered. Ensure that it is articulated in a way that is actionable and allows for measurable outcomes. It should clearly state what changes you propose and what impact you expect these changes to have on user behavior or conversion rates.
4. Defining Variables
Make sure to clearly identify what aspects will be changed (independent variables) and what you will be measuring (dependent variables). Clearly defining these variables ensures your testing is targeted and that the results are easy to interpret.
Independent Variables: These are the elements you plan to change or modify in your test. They are the variables that you will manipulate to observe their effect on user behavior. For example, you might alter the color of a call-to-action button or modify the text of a headline.
For example: In a test aiming to improve sign-ups, changing the text on the sign-up button could be your independent variable.
Dependent Variables: These are the outcomes or results that you’ll be measuring. They depend on the changes made to the independent variables. It could be the number of clicks, sign-ups, or purchases.
For example: Following the above scenario, the dependent variable would be the number of people who signed up after seeing the modified sign-up button.
By clearly defining these variables, your testing becomes more structured, and the results become easier and more straightforward to analyze and interpret.
5. Choosing Success Metrics
Decide on the key metrics that will help you measure the success of your hypothesis. These should align with your goals and provide a clear way to see whether your changes are having the desired effect.
Now that you know the 5 steps of how to craft a hypothesis, let’s bring it to life through a practical real-world example:
Let’s say you noticed your online store has a lot of users abandoning their shopping carts. A well-structured hypothesis could look something like this:
“By simplifying the checkout process and reducing the steps needed to complete a purchase, we expect to see a 20% reduction in shopping cart abandonment. Here, the ‘checkout process’ is what we are changing, and the ‘cart abandonment rate’ is what we are measuring, aiming for a 20% improvement.”
Crafting a detailed hypothesis is essential for focused and effective CRO efforts. Following these steps allows for a strategic approach to improving and enhancing the user experience on your website.
Hypothesis Prioritization: Choosing What Matters Most
It’s not enough to just have a list of hypotheses; knowing which one to test first based on potential impact and ease of implementation is vital. Prioritization ensures that effort and resources are strategically allocated to bring about effective improvements in the conversion rate.
Why is Prioritizing Hypotheses Important?
Prioritizing hypotheses is about being meticulous and thoughtful in choosing what to optimize first. Proper prioritization ensures that you focus on changes that are likely to bring about significant improvements in conversions. It allows for a systematic approach to testing, ensuring that resources and efforts are not squandered on less impactful elements, thus fostering a more effective and efficient optimization process.
Here are some of the most commonly-adopted methods to streamline this process:
ICE (Impact, Confidence, Ease)
ICE model evaluates hypotheses based on three criteria: the potential Impact on conversion rates, Confidence in the hypothesis, and Ease of implementation. Each criterion is scored, and an average is taken, guiding the prioritization.
PIE (Potential, Importance, Ease)
The PIE model focuses on the Potential improvement a test can bring, the Importance of the pages where the test will run, and the Ease of implementing the test. It’s a framework that ensures that tests are both impactful and feasible.
PXL Framework
PXL prioritization method places more emphasis on the quality of evidence. It categorizes evidence into strong and weak, affecting the overall prioritization. It also considers the potential traffic affected and the ease of implementation.
Stage 3 – Design and build
This stage in the CRO process involves meticulous planning, ensuring that the tests are not only effective but also feasible considering the team’s development capabilities and constraints. Here’s how to navigate this crucial stage:
Purpose-Driven Design
Before diving into aesthetics, it’s essential to pinpoint the purpose of the changes. Whether it’s to boost sign-ups, increase sales, or encourage user interaction, each design choice must serve this main goal.
Responsive Design
Cater to the diverse array of devices used by visitors. Ensure that the design is adaptable and offers a consistent experience across various screen sizes and devices.
Consistent Branding
Any changes should align with the company’s branding guidelines. Consistency in colors, fonts, and general style ensures the website remains recognizably on-brand.
Visual Hierarchy
Organize and prioritize elements based on their significance. Highlight essential actions or information with strategic positioning, size, or color, guiding visitors’ attention where it’s most needed.
Flexibility in Design
Develop a design that is adaptable. Consider potential obstacles and uncertainties, and be prepared to make necessary adjustments to the test designs as required.
Technical Feasibility
Ensure that the technical aspects of the tests, such as coding and platform integrations, are within the team’s expertise. This aids in avoiding potential roadblocks during the implementation phase.
Loading Time
Optimize design elements to ensure that web pages load quickly. A slow-loading page can deter potential conversions and render your test results unfavorable and inaccurate.
Timeline Management
Define a clear and reasonable timeline for the test execution. Allow room for unexpected challenges to ensure the process doesn’t feel rushed, and quality is maintained.
The design stage sets the foundation for the tangible changes visitors will experience. A successful design phase ensures not just a visually appealing site but one that’s structured to achieve conversion goals effectively and efficiently.
Stage 4 – CRO Testing
Testing is a stage in the CRO process where hypotheses and design alterations are put to the test to discern their effectiveness and impact on conversion rates. The objective is to uncover what works best for encouraging users to take desired actions on a website.
Different testing methods – Bayesian vs. frequentist
CRO testing focuses on evaluating the performance of various website components. There are different statistical approaches available to do so. Two predominant approaches include the Bayesian Method and the Frequentist Method.
This doesn’t mean you need to become a professional statistical analyst to do CRO testing. But having a basic understanding of these statistical methods is beneficial for interpreting test results and making informed optimization decisions.
Bayesian Method
Understanding the Approach:
This method mixes historical (prior) knowledge with new data collected during the test. It provides a fuller picture by combining past learning with current observations, making the analysis rich and contextually grounded.
How It Works:
For instance, if a product page had a consistent 5% conversion rate in the past, this method would use this information to complement the findings of the new test.
In Practice:
Say you’re testing a new feature on the product page. The Bayesian Method evaluates the new feature considering the established 5% conversion rate, ensuring that historical insights are not lost, and changes are assessed within this broader context.
Frequentist Method
Understanding the Approach:
The Frequentist Method concentrates solely on new data acquired in the current test. It provides a focused view, solely weighing the immediate outcomes without being influenced by past results or performances.
How It Works:
In an A/B test comparing two versions of a webpage, this method would strictly evaluate their performance based on the data generated within the test duration, ignoring any prior data.
In Practice:
If one version performs better in this specific test, that version is considered superior based strictly on the latest findings, without any historical influence or adjustment.
In conclusion, choosing between the Bayesian and Frequentist Methods requires a strategic decision based on the goals and context of your tests. Both methods offer valuable perspectives – one offering a comprehensive view with historical context and the other offering a clean, immediate analysis based on the most current data.
Different test types
A/B Testing
This is the most commonly used test type in CRO as it compares two versions of a webpage to determine which one excels in driving conversions. Modifications can range from minor changes, like adjusting the color of a CTA button, to more significant alterations, such as implementing a new page layout. And, despite its name, A/B testing isn’t limited to just two versions. You can also have A/B/C tests or even A/B/C/D tests, all under the umbrella of the test type “A/B testing”.
Example: An e-commerce site might conduct an A/B test comparing the performance of two different product page layouts. By doing so, they aim to discover which layout design contributes to a more intuitive and enticing shopping experience, leading to increased sales.
Multivariate Testing
Multivariate testing is a more complex form of testing that tests different parts of a webpage all at once, like headlines, images, and call-to-action buttons. It’s particularly useful when you want to understand how various combinations perform together and which mix leads to the most conversions or user engagement.
Example: A travel blog could use multivariate testing to find the optimal combination of article headline styles, image placements, and CTA button colors to enhance user engagement and increase newsletter sign-ups.
Split URL Testing
Split URL testing involves creating distinct versions of a webpage, each hosted on a different URL. This method is useful when you want to test major changes that require different HTML structures or designs.
Example: A software company might use split URL testing to evaluate two wholly different landing page designs. Each design would be hosted on a separate URL, allowing for a comprehensive assessment of which design structure garners more downloads or sign-ups.
Multi-page testing
Multi-page testing, also known as funnel testing, is a type of conversion rate optimization (CRO) technique where variations of multiple pages within a user journey or funnel are tested against each other. Instead of focusing on changing elements on a single page (like in A/B testing or multivariate testing), multipage testing allows for the testing of broader changes across a series of pages. This is particularly useful when considering a website redesign or major updates.
Example: If you want to improve the checkout process of your ecommerce website. You could create different versions of the user journey, starting from the product page, going through the cart, and finishing with the checkout page. In each version, the pages might have different designs, layouts, or content, allowing you to see which sequence of pages performs the best in terms of user experience and conversion rate.
A/A test
In an A/A test, the same version of a webpage is compared against itself. There are no changes or variations made to either version. The purpose of conducting an A/A test is to act as a quality check for your testing environment. It’s like a “pre-test” to make sure everything is working perfectly before you start the actual experiment. Any significant difference in the results may point towards issues in the testing setup, which need to be corrected before conducting any meaningful tests.
Example: When an online bookstore adopts a new testing tool, it starts with an A/A test. They show two identical homepages to different user groups, expecting similar results. If there are major differences in conversion rates, it may mean there are setup errors that need fixing. This important first step helps make sure the tool works properly and that future tests will be reliable and accurate.
How to interpret test results
Interpreting A/B test results involves examining specific metrics to evaluate the effectiveness of different webpage versions. By analyzing the conversion rate and confidence level, we can determine whether changes made in a test version lead to improved performance and whether the results are statistically significant and reliable for making optimization decisions.
Conversion Rate
The conversion rate is the percentage of users who take a desired action, such as making a purchase or signing up for a newsletter, on a webpage.
A higher conversion rate in one version compared to another indicates better performance and effectiveness in encouraging users to take the desired action.
Confidence Level/Statistical Significance
Confidence level refers to the probability that the results from the A/B test are not due to random chance. Statistical significance is achieved when the confidence level is high, commonly above 95%.
A high confidence level suggests that the results of the test are reliable, and the changes made in the variant are likely the cause of any difference in performance.
Example 1: Statistically Significant Test
Test Objective: To evaluate which call-to-action (CTA) button color on a landing page generates more clicks.
Variation A: A blue CTA button.
Variation B: A red CTA button.
Results:
Variation A: Conversion Rate = 15%, Confidence Level = 98%
Variation B: Conversion Rate = 23%, Confidence Level = 98%
What the test results entails :
Given the high confidence level of 98%, it’s apparent that the red CTA button (Variation B) significantly outperforms the blue button (Variation A) in generating more clicks, with a conversion rate of 23% versus 15%. The results are statistically significant, leading to the conclusion that changing the button color to red is a beneficial modification for enhancing user engagement and conversions.
Example 2: Test Not Statistically Significant
Test Objective: To assess whether changing the headline on a product page influences customer engagement.
Variation A: Original headline: “Revolutionize Your Experience”
Variation B: New headline: “Transform Your Journey”
Results:
Variation A: Conversion Rate = 10%, Confidence Level = 90%
Variation B: Conversion Rate = 11%, Confidence Level = 90%
What the test results entails:
In this case, the confidence level is 90%, which is below the desired threshold of 95%. Furthermore, the conversion rates between the two variations are quite close (10% vs. 11%). Due to the lower confidence level and the marginal difference in conversion rates, the test is not statistically significant. It’s challenging to decisively conclude that the new headline will bring about a meaningful improvement in customer engagement. Therefore, more testing might be necessary, or other elements on the product page could be optimized for better results.
Stage 5 – Test Analysis: Triumphs and Trials
The conclusion of A/B testing unfolds in two scenarios: a victorious win or a learning-fueled loss. Both outcomes, brimming with invaluable insights, pave the roadmap for strategic refinement and enhancement.
Navigating a Winning Test
Celebration with Caution: Revel in the success, but approach with a critical eye. Ensure that the victory aligns with substantial evidence, fortifying its credibility.
In-depth Analysis: Peruse the winning elements. What enhanced the user experience or simplified the customer’s journey? Uncover the ‘whys’ behind the success.
Strategic Implementation: Transition the triumphant variations from the testing environment to live status. Monitor the changes, ensuring that they perpetuate success in real-time scenarios.
Embracing a Losing Test
Fostering Growth: A lost test is not a defeat, but a rich resource. It’s a profound wellspring of insights that heralds opportunities for improvement and innovation.
Analytical Deep Dive: Dissect the elements that didn’t resonate. What deterred engagement or conversions? Use these findings as pillars for building a more refined strategy.
Revisiting the Hypothesis: Reflect on the initial assumptions and hypotheses. The derived learnings help recalibrate these foundations, fostering a more nuanced and data-informed approach for future tests.
In conversion rate optimization (CRO), each test is a valuable source of information and insights, regardless of its results. Winning tests confirm that our strategies are effective, while losing tests reveal areas where we can improve and innovate. This way, every test helps us move closer to achieving optimal performance and excellence.
Stage 6 – Iterate and repeat from the beginning
Optimization is a continual journey, not a destination. Once you have conducted tests and gleaned insightful data from your Conversion Rate Optimization (CRO) strategies, the cycle renews. This stage, “Iterate and Repeat,” embodies the essence of constant enhancement and the perpetual pursuit of improvement.
Re-evaluation and Insight Synthesis
Description: Re-evaluate your webpage’s performance, incorporating the newfound knowledge and insights. Synthesize the results to form coherent conclusions that can guide your next steps.
Actionable Steps: Utilize the data to identify what worked and what didn’t. Understanding these aspects is crucial to refining strategies and making informed decisions for subsequent iterations.
Refinement and Optimization
Description: Based on the gathered insights, refine your strategies. Make necessary adjustments to your webpage elements and CRO tactics, optimizing them for better performance.
Actionable Steps: Implement changes and modifications that are substantiated by your test results and analysis. Ensure that each iteration is an improvement or variation based on validated learning.
Repetition and Continuous Learning
Description: Reengage in the CRO process, applying the revised strategies and insights. This continuous loop is foundational for ongoing improvement and adapting to evolving user preferences and market trends.
Actionable Steps: Repeat the CRO stages with a fresh perspective, using previous learnings as a roadmap. Continuous learning and adaptation are at the core of this repetitive yet evolutionary process.
“Iterate and Repeat” symbolizes the heart of a dynamic and responsive CRO strategy. It is characterized by a relentless reevaluation of strategies, continuous refinement based on actionable insights, and a commitment to perpetual learning and adaptation. Each cycle is a new opportunity to enhance and optimize, driven by data-driven decisions and a dedication to excellence.
