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if(!vwo_$.fn.vwoRevertHtml){
return;
};
var ctx=vwo_$(x),el;
/*vwo_debug log("Revert","content",""); vwo_debug*/;
el=vwo_$('[vwo-element-id="1729489786308"]');
el.revertContentOp().remove();})("HEAD")}}, C_6_1315_1_3_1:{ fn:function(log,nonce=''){return (function(x) {
try{
var _vwo_sel = vwo_$("`);
!vwo_$("head").find('#1729489786315').length && vwo_$('head').append(_vwo_sel);}catch(e) {console.error(e)}
try{}catch(e) {console.error(e)}
try{document.querySelector(".feedback-btn").addEventListener("click",(function(){window.VWO=window.VWO||[],VWO.event=VWO.event||function(){VWO.push(["event"].concat([].slice.call(arguments)))},VWO.event("alfredNotHelpfullClick",{notHelpful:!0,details:"HelpfulNotHelful button clicked."})}));}catch(e) {console.error(e)}
return vwo_$('head')[0] && vwo_$('head')[0].lastChild;})("head")}}, C_6_1315_1_3_2:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
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if(!vwo_$.fn.vwoRevertHtml){
return;
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var el,ctx=vwo_$(x);
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var el,ctx=vwo_$(x);
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var el,ctx=vwo_$(x);
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if(!vwo_$.fn.vwoRevertHtml){
return;
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})("#intercom-container, .intercom-lightweight-app")}}, C_6_1426_1_2_0:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
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/*vwo_debug log("editElement",".Fz\\(\\$font-size-36\\)--sm"); vwo_debug*/(el=vwo_$(".Fz\\(\\$font-size-36\\)--sm")).html("Website A/B Testing and CRO Software");})(".Fz\\(\\$font-size-36\\)--sm")}}, C_6_1426_1_6_0:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
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/*vwo_debug log("content",".slick-dots > li:nth-of-type(2) > span:nth-of-type(1)"); vwo_debug*/(el=vwo_$(".slick-dots > li:nth-of-type(2) > span:nth-of-type(1)")).replaceWith2("Minimum IT Dependency"),el=vwo_$(".slick-dots > li:nth-of-type(2) > span:nth-of-type(1)");})(".slick-dots > li:nth-of-type(2) > span:nth-of-type(1)")}}, C_6_1426_1_6_4:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("changeSRC",".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(2) > img:nth-of-type(1)"); vwo_debug*/(el=vwo_$(".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(2) > img:nth-of-type(1)")).vwoAttr({alt:"High Speed Testing@2x",title:"",width:934,height:570,border:"",src:"//useruploads.visualwebsiteoptimizer.com/useruploads/6/images/fcbf0901013e8b8798567fdc15f90e8c_agilityintests2x1.png",removedAttributes:["srcset"]});})(".slick-track > div:nth-of-type(3) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(2) > img:nth-of-type(1)")}}, C_6_1426_1_6_5:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
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/*vwo_debug log("editElement",".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(1)"); vwo_debug*/(el=vwo_$(".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(1)")).html("A/B testing");})(".slick-track > div:nth-of-type(2) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(1)")}}, C_6_1426_1_6_7:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
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/*vwo_debug log("editElement",".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(2)"); vwo_debug*/(el=vwo_$(".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(2)")).html("Multivariate testing");})(".slick-track > div:nth-of-type(2) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(2)")}}, C_6_1426_1_6_9:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("editElement",".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > p:nth-of-type(2)"); vwo_debug*/(el=vwo_$(".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > p:nth-of-type(2)")).html("Experiment with dozens of changes on your landing pages, all at once, and discover the winning combination.");})(".slick-track > div:nth-of-type(2) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > p:nth-of-type(2)")}}, C_6_1426_1_6_10:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("editElement",".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(1)"); vwo_debug*/(el=vwo_$(".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(1)")).html("Point-and-click visual editor");})(".slick-track > div:nth-of-type(3) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > h3:nth-of-type(1)")}}, C_6_1426_1_6_11:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("editElement",".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > p:nth-of-type(1)"); vwo_debug*/(el=vwo_$(".slick-current > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > p:nth-of-type(1)")).html("Edit elements, get copy suggestions, preview to debug, and apply changes to multiple pages.");})(".slick-track > div:nth-of-type(3) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > div:nth-of-type(1) > p:nth-of-type(1)")}}, C_6_1152_1_2_0:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("editElement",".Fz\\(\\$font-size-50\\)--sm"); vwo_debug*/(el=vwo_$(".Fz\\(\\$font-size-50\\)--sm")).html("Looking for the #1 Optimizely Alternative?");})(".Fz\\(\\$font-size-50\\)--sm")}}, C_6_1152_1_3_0:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("editElement",".Fz\\(\\$font-size-50\\)--sm"); vwo_debug*/(el=vwo_$(".Fz\\(\\$font-size-50\\)--sm")).html("Looking for the #1 Optimizely Alternative?");})(".Fz\\(\\$font-size-50\\)--sm")}}, C_6_1152_1_4_0:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
/*vwo_debug log("editElement",".Fz\\(\\$font-size-50\\)--sm"); vwo_debug*/(el=vwo_$(".Fz\\(\\$font-size-50\\)--sm")).html("Get started today with VWO's free starter plan.");})(".Fz\\(\\$font-size-50\\)--sm")}}, C_6_1426_1_6_15:{ fn:function(log,nonce=''){return (function(x) {var el,ctx=vwo_$(x);
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Key Learnings:
Be detailed: Provide a comprehensive description of the product, including its size, weight, and even how it feels.
Focus on the customer journey: Show how your product fits into the customer\'s life. Include details on customer service experience and delivery time.
Highlight value:Address price and convince the customer they\'re getting their money\'s worth. Back up your claims with facts and expert sources.
Share personal experiences: Especially for personal care products, real-life stories can be powerful persuasion.
Build trust: Ensure your review page design feels professional and trustworthy. This can help convince potential customers they\'re on a legitimate website.
Craft scannable emails: People skim online content. Use bullet points, contrasting colors, and short phrases to deliver your message effectively.
Compelling subject lines and sender names: Grab attention with clear, intriguing subject lines and a recognizable sender name to encourage email opens.
Strategic use of visuals: Images can break up text and visually communicate your message. Choose high-quality visuals relevant to your email\'s content.
Clear calls to action: Tell readers exactly what you want them to do next. Use strong verbs and prominent buttons to guide them towards conversion.
Write for the scanner, not the reader: People often scan emails, so prioritize key information and make it easy for them to find what they\'re looking for.
ML personalizes the shopping experience: Machine learning can analyze customer data to personalize product recommendations, offers, and page layouts for each visitor.
Faster, more accurate optimization: Machine learning automates A/B testing and analyzes results, leading to quicker and more precise website optimization compared to traditional methods.
Data-driven decision-making: ML empowers data-driven decisions by uncovering hidden patterns and customer insights that might be missed by manual analysis.
Effortless 24/7 optimization: Machine learning algorithms can continuously analyze and optimize your ec-ommerce experience, working tirelessly to improve conversion rates.
Boosts efficiency for human expertise: ML frees up human experts to focus on strategic tasks by automating repetitive processes like testing variations and analyzing data.
Experience is a feeling, not a habit: Focus on creating positive emotions throughout the customer journey, not just building routine purchases.
Tech is a tool, not a solution: Don\'t prioritize technology over the customer. Ensure it enhances, not hinders, the shopping experience.
Transparency builds trust: Be open and honest about your brand\'s practices and values. This fosters trust and strengthens customer relationships.
Community fosters loyalty: Build a community around your brand. Engage with customers, listen to their feedback, and create a sense of belonging.
Data informs decisions, empathy refines them: Use data to understand customer behavior, but prioritize empathy to create experiences that resonate on an emotional level.
GA4 focuses on events, not hits: Unlike Universal Analytics, GA4 prioritizes tracking user actions (events) over website interactions (hits). This allows for a more comprehensive understanding of the customer journey.
Cookie-less future-proofing: GA4 is designed to function without cookies, which are becoming less reliable due to privacy regulations. This ensures your data collection remains robust.
Data streams for multi-source analysis: GA4 uses data streams to combine data from websites and apps, providing a unified view of user behavior across platforms.
Drag-and-drop reporting customization: The GA4 interface allows for flexible report creation through drag-and-drop functionality. Build reports tailored to your specific needs.
Integration with VWO for A/B testing: GA4 doesn\'t have built-in A/B testing, but VWO\'s two-way integration bridges this gap. You can leverage GA4 data for experiment setup and analyze results within VWO.
Beyond basic changes: Move past simply tweaking colors or copy. A/B testing should explore significant variations with the potential for larger conversion rate gains.
Segmentation is key: Don\'t treat all visitors the same. Segment your audience based on demographics, behavior, or other factors to personalize your A/B tests and get more relevant results.
Data-driven hypotheses, not copycat tactics: Don\'t blindly copy what worked for others. Base your A/B test hypotheses on user data and research to identify areas for improvement specific to your audience.
Look beyond conversion rate: While conversion rate is important, consider other metrics like customer satisfaction or time spent on page to paint a more complete picture of a variation\'s success.
Continuous learning and iteration: A/B testing is an ongoing process. Analyze results, learn from each test, and use those insights to continuously refine your website and user experience.
Leverage patriotic spirit: Indians\' enthusiasm for Independence Day translates to purchase decisions. Design marketing campaigns that resonate with this national pride.
Exclusive product launches: Create excitement with exclusive product offers or launches timed for Independence Day sales. This can attract new customers and boost sales.
Mobile-first approach: Since India is a mobile-dominant market, optimize your website and marketing efforts for smartphones to capture customer attention.
Highlight value and savings: Independence Day sales are a time for deals. Emphasize discounts and special offers to convince customers they\'re getting a good value.
Build anticipation: Don\'t wait until the last minute. Tease upcoming sales and exclusive offers in advance to build anticipation and excitement among potential customers.
Trustworthy brand image: A strong, trustworthy brand image builds confidence in visitors, making them more likely to convert. Ensure your website design and messaging reflect your brand\'s values and personality.
Clear value proposition: Communicate your brand\'s unique selling proposition (USP) clearly and concisely. Help visitors understand why your product or service is the best choice for them.
Emotional connection: Forge an emotional connection with your audience. Use storytelling, visuals, and customer testimonials to tap into their desires and needs.
Frictionless user experience: Make it easy for visitors to find what they need and complete desired actions. Streamline your website navigation, checkout process, and overall user experience for optimal conversion.
Highlight social proof: Showcase social proof elements like customer reviews, awards, and logos from trusted brands. This builds trust and reassures visitors they\'re making a good decision.
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Key Learnings:
Focus on customer needs, not features: Don\'t just list features; explain how your product or service solves a specific problem or fulfills a need for your target customer.
Clarity and conciseness are king: Craft a clear and concise value proposition that can be understood instantly. Avoid jargon and technical terms; focus on the benefits that resonate with your audience.
Highlight differentiation: Explain what makes your offering unique compared to competitors. This could be a specific feature, a superior customer experience, or a focus on a particular niche.
Quantify value whenever possible: Use data or statistics to back up your claims. For example, showcase how much time or money your product saves customers.
Test and refine your value proposition: Your value proposition is a living document. Test different versions with your target audience to see what resonates best and continuously refine it for maximum impact.
Simplicity for clarity: Flat design prioritizes clean lines and minimal elements, making it easier for users to focus on website content and complete desired actions.
Faster loading times: Flat design\'s streamlined aesthetics often lead to faster website loading times, especially on mobile devices, which can improve user experience and potentially boost conversions.
Focus on user experience: By minimizing clutter and distractions, flat design can enhance user experience by making it easier for visitors to find what they need and navigate the website efficiently.
Modern and approachable feel: Flat design offers a modern and approachable aesthetic that can resonate with a broad audience, potentially increasing brand appeal and website engagement.
A/B test for optimal results: Remember, what works for one website might not work for another. Consider A/B testing flat design elements against your current design to see which approach leads to higher conversion rates for your specific audience.
The Chance to Beat All (CTBA) represents the probability that a variation will perform better than the control version of your website or mobile app. For example, a chance of 60% means that the variation is likely to perform better than the control 60% of the time. This value also means a 40% chance that your results are completely due to a random chance.
Currently, this method of calculating the CTBA is only used for Multivariate tests because they still follow the Frequentist method of calculating a winner. To determine the winning variation in A/B, Split URL tests, and Personalize, we follow the SmartStats (Bayesian) method of calculating the chance to beat all versions.
The success or failure of a variation is calculated by comparing the results against a base version (by default, the control page). You can change the base for your test at any time.
Please note that VWO calculates the chance to beat based on the test threshold settings you have defined in Settings> Campaign Settings.
To determine the chance to beat a variation, VWO calculates the:
Conversion rates of the control and variation
The standard error for the control and variation
Chance to beat for a variation
Calculating Conversion Rates of the Control and Variations
Mathematically, the conversion rate is a binomial random variable, i.e., it can have two possible values. It is represented as the variable p. In A/B testing, you can calculate the value of p by observing n visitors on the webpage that is being tested. From those n visitors, you can calculate the number of conversions. The percentage value from the result is the conversion rate of your website or app.
Conversion rate (p) = Actual conversions/Visits to the website
The percentage value is the conversion rate of your website.
For example, consider that you are running a test to review the new design of your landing page. The control receives 894 visits, of which 423 are converted. On the other hand, the variation receives 863 visits, of which 458 are converted. These values imply that the conversion percentage value of the control is 47%, and that of the variation is 53%.
Calculating the Standard Error for the Control and Variations
If you repeat the same test multiple times, you will likely get a different value of p (or conversion rate of a variation) every time. This happens because, in statistical terms, we’re sampling, and like every sample, there’s a range of errors associated with it (because it does not cover the entire population). To avoid repeated tests, we can use the already established statistical formulas to calculate the standard error to determine how much deviation from the average conversion rate (p) can be expected from the test results. The deviation is the range within which the conversion rate is usually found. Smaller the deviation, the more confident you can be about estimating the true conversion rate. For a given conversion rate (p) and the number of trials (n), the standard error is calculated as:
Standard Error (SE) = Square root of (p * (1-p) / n)
In the example, the SE for control = Square root of (0.4732*(1-0.473/894) = 1.67 x 10-2. And, the SE for variation = Square root of (0.5307*(1-0.5307/863) =1.70 x 10-2. (Please note that the percentage values are converted to decimal in the above calculations).
To get a 95% range for the conversion rate, multiply the standard error value by 2 (or 1.96 to be precise). You can be sure with 95% confidence that your true conversion rate lies within this range: p% ± 2 * SE.
In VWO, we calculate the conversion rate range for 80%, not 95%. Hence, we multiply the SE value by 1.28.
In the example discussed above, the standard error for the control is 1.67, while that of the variation is 1.70. Next, multiply the values by 1.28. Thus, the conversion rate range is ± 2.14% for control and ± 2.17% for the variation.
Calculating the CTBA for a Variation
After the Z-score is calculated, VWO calculates standard normal distribution to derive the chance percentage for the variation. You can refer to the following normal distribution table to understand how CTBA is derived for a variation:
To determine the chance to beat for a variation, we calculate the Z-score for control and variation. A Z-score is used in statistics to model any normal distribution as a standard normal distribution.
Where,
Pv = Conversion rate of variation
Pc = Conversion rate of control
SEv = Standard error of variation
SEc = Standard error of control
For our example discussed above, the Z-score can be derived as follows:
As we see, for our z-score of 2.41, the normal distribution value is approximately 99%. To make the calculation easier for you, VWO provides a test significance calculator. Click here to download an Excel spreadsheet to help you determine the statistical significance of your test.
VWO does not calculate the CTBA when:
The conversion rate of control is 0 or 1.
The conversion rate of a variation is 0 or 1.
The number of visitors in the variation is less than the set threshold value (sample size).
The standard Error of either control or variation is 0.
You can edit the CTBA threshold values and sample size using the VWO dashboard under Settings > Campaign Settings.