Key takeaways<\/h2>\n\n\n- A\/B testing reworked GoDaddy’s product growth from opinion-based to data-driven decision-making<\/li>\n
- Failed experiments present precious studying alternatives and drive innovation<\/li>\n
- Experimentation tradition promotes cross-functional collaboration and quicker, extra assured decision-making<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
(Editor’s notice: This put up is the primary in a 4 half sequence that discusses experimentation at GoDaddy. Examine again for the following half within the sequence.)<\/em><\/p>\nA\/B testing wasn\u2019t all the time central to product growth at GoDaddy. Like many groups, we relied on buyer suggestions, enterprise priorities, and our greatest judgment. And whereas that labored to some extent, we knew we may very well be extra data-driven.<\/p>\n
Over time,\u00a0we have constructed and embraced a tradition of experimentation<\/a>\u00a0with A\/B testing being central to how we construct merchandise. At any given time,\u00a0GoDaddy groups are working a whole bunch of experiments throughout all buyer touchpoints<\/a>. Nonetheless, it\u2019s greater than only a validation instrument, it serves as a guiding framework for decision-making that shapes our inside product tradition. This strategy permits us to behave quicker, with better confidence, whereas remaining aligned with our clients’ wants.<\/p>\nWhat’s A\/B testing?<\/h2>\nA\/B testing is a technique of evaluating two or extra variations of a product, function, or interface to find out which one performs higher based mostly on particular metrics. In a typical A\/B check, customers are randomly assigned to completely different teams, with one group serving because the management group and others as experimental variants.<\/p>\n
\n<\/figure>\n<\/div>\n\u00a0The diagram reveals an outline of the A\/B testing strategy<\/em><\/p>\nThis kind of experiment is also known as a randomized managed experimentation (RCE). The outcomes are then analyzed utilizing statistical methodologies to find out, with statistical significance, which model results in higher outcomes.<\/p>\n
A\/B testing is a well-liked RCE kind, however not the one one. The next are frequent RCE varieties:<\/p>\n
\n- A\/B<\/strong>: evaluating two variations (A vs. B). That is the commonest kind of check used for easy modifications, reminiscent of a brand new button coloration or headline.<\/li>\n
- Multi-variant<\/strong>\u00a0(or A\/B\/n): that is an extension of A\/B testing the place you evaluate three or extra variations to see which one resonates most with the viewers.<\/li>\n
- Multivariate<\/strong>: as an alternative of testing only one factor (like in A\/B testing), you check combos of a number of modifications without delay to establish the best combine.<\/li>\n
- Champion\/Challenger<\/strong>: the place a “Challenger” variant undergoes a number of refinements earlier than being examined in opposition to the present “Champion”.<\/li>\n<\/ul>\n
Whereas A\/B testing and its variations depend on randomization for exact comparisons, one other strategy is Pre\/Put up\u00a0testing that measures efficiency earlier than and after a change with out a management group. Although extra prone to exterior influences like seasonality, it may nonetheless present precious insights when randomization isn\u2019t doable.<\/p>\n
Past guesswork<\/h2>\nAfter we redesigned\u00a0GoDaddy\u2019s area switch circulation<\/a>, we assumed simplifying the interface would enhance success charges. Whereas this did occur, we quickly realized that exterior elements like seasonality, advertising and marketing campaigns, and macro traits made it troublesome to isolate the true affect of our modifications. Have been the outcomes because of our redesign, or one thing else? The next diagram reveals A\/B testing on a simplified\u00a0hierarchy of proof pyramid<\/a>:<\/p>\n\n![\"\"](\"https:\/\/www.godaddy.com\/resources\/wp-content\/uploads\/2025\/03\/simplified-pyramid.jpg?size=3840x0\"\/)
<\/figure>\n<\/div>\nThat\u2019s the place A\/B testing made a distinction. Not like the pre\/put up evaluation we used earlier than, it allowed us to instantly evaluate two variations below the identical situations. A\/B testing helped:<\/p>\n
\n- give us\u00a0management<\/strong>\u00a0over exterior elements, so we might pinpoint precisely what was driving enhancements.<\/li>\n
- present extra\u00a0confidence<\/strong>\u00a0in our outcomes, so groups iterated quicker, making data-driven changes to attain the specified final result.<\/li>\n
- us\u00a0prioritize<\/strong>\u00a0modifications that delivered the best affect, guaranteeing we centered on probably the most precious enhancements first.<\/li>\n<\/ul>\n
Shifting to data-driven experimentation empowered our product engineering groups to make choices that not solely achieved our targets, but additionally delivered worth to our clients and stakeholders.<\/p>\n
Maximizing studying alternatives<\/h2>\nOne of many largest classes we\u2019ve discovered about A\/B testing is that statistically important losses may be simply as precious as statistically important wins.<\/p>\n
A few of our most beneficial buyer insights got here from failed A\/B experiments. For instance, our first iteration of a brand new\u00a0GoDaddy Auctions bidding circulation<\/a>\u00a0aimed to assist consumers win expired domains however had the other impact. As a substitute of a setback, the staff noticed it as a studying alternative, we:<\/p>\n\n- reviewed outcomes to grasp the frictions throughout the circulation.<\/li>\n
- collected extra suggestions from surveys and analyzed session recordings.<\/li>\n
- introduced collectively the staff to brainstorm options based mostly on the findings.<\/li>\n
- developed a brand new bidding circulation expertise addressing the recognized frictions.<\/li>\n
- launched an improved model, resulting in a successful iteration.<\/li>\n<\/ol>\n
This collaborative, data-driven strategy not solely uncovered the foundation explanation for the difficulty but additionally led to an improved iteration that strengthened buyer success.<\/p>\n
The next picture reveals two A\/B experiment iterations:\u00a0<\/p>\n\n![\"graphical](\"https:\/\/www.godaddy.com\/resources\/wp-content\/uploads\/2025\/03\/experiment-example.jpg?size=3840x0\"\/)
<\/figure>\n<\/div>\nA\/B testing is a robust solution to validate concepts based mostly on buyer actions. Nevertheless it\u2019s only one piece of our buyer empathy toolkit. To grasp each the “what” and the “why”, GoDaddy groups are inspired to mix A\/B testing with a variety of\u00a0instruments that supply deeper insights into buyer habits and suggestions<\/a>. There is not any stronger motivator to make use of these instruments than a shedding experiment. In these moments, we achieve the perception to rethink, regulate, and create one thing higher.<\/p>\nThe Energy of Velocity<\/h2>\nOne of many largest advantages of A\/B testing is that it permits our groups to maneuver quicker with objective. That\u2019s significant progress. Pace is about how briskly you go, velocity is about transferring in the appropriate route. You may be working at full pace, however in the event you\u2019re getting in circles, you\u2019re not getting wherever. In enterprise, progress issues greater than movement.<\/p>\n
The next picture illustrates the distinction between pace and velocity:\u00a0<\/p>\n\n![\"diagram\"](\"https:\/\/www.godaddy.com\/resources\/wp-content\/uploads\/2025\/03\/velocity.jpg?size=3840x0\"\/)
<\/figure>\n<\/div>\nEarlier than adopting A\/B experimentation, our product cycles have been slower and generally disconnected from long-term technique. We\u2019d spend months constructing and launching options, solely to scramble afterward to research metrics. By the point we understood what didn\u2019t work, it was too late to regulate. We have been already centered on the following venture.<\/p>\n
However velocity isn\u2019t nearly transferring rapidly, it\u2019s about studying in a approach that compounds over time. As our CEO Aman famous, a\u00a0wholesome experimentation tradition balances constructive, adverse, and inconclusive outcomes<\/a>. If each experiment wins, we is likely to be enjoying it too protected as an alternative of pushing boundaries. Probably the most impactful experiments don\u2019t simply transfer metrics, they advance our understanding of the client and form product technique. A scattered, one-off experiment may drive a short-term win, however experiments aligned with product themes and clearly outlined buyer issues result in stronger product experiences.<\/p>\nCatalyst for Collaboration<\/h2>\nA\/B testing hasn\u2019t simply helped us construct higher merchandise. It reworked how groups collaborate at GoDaddy. Earlier than we adopted a structured strategy to experimentation, groups like product, design, engineering, enterprise analytics, and buyer success usually labored in silos. The A\/B testing course of itself has naturally develop into a unifying pressure. Every experiment is a cross-functional effort from the beginning, with everybody concerned in defining the speculation, designing the check, monitoring outcomes, discussing, iterating and sharing the learnings.<\/p>\n
GoDaddy aspires to construct nice merchandise and experiences that clear up actual buyer issues. Our gold commonplace are experiments that use randomization and sturdy statistical strategies that empower groups to:<\/p>\n
- \n
- A\/B testing reworked GoDaddy’s product growth from opinion-based to data-driven decision-making<\/li>\n
- Failed experiments present precious studying alternatives and drive innovation<\/li>\n
- Experimentation tradition promotes cross-functional collaboration and quicker, extra assured decision-making<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
(Editor’s notice: This put up is the primary in a 4 half sequence that discusses experimentation at GoDaddy. Examine again for the following half within the sequence.)<\/em><\/p>\n
A\/B testing wasn\u2019t all the time central to product growth at GoDaddy. Like many groups, we relied on buyer suggestions, enterprise priorities, and our greatest judgment. And whereas that labored to some extent, we knew we may very well be extra data-driven.<\/p>\n
Over time,\u00a0we have constructed and embraced a tradition of experimentation<\/a>\u00a0with A\/B testing being central to how we construct merchandise. At any given time,\u00a0GoDaddy groups are working a whole bunch of experiments throughout all buyer touchpoints<\/a>. Nonetheless, it\u2019s greater than only a validation instrument, it serves as a guiding framework for decision-making that shapes our inside product tradition. This strategy permits us to behave quicker, with better confidence, whereas remaining aligned with our clients’ wants.<\/p>\n
What’s A\/B testing?<\/h2>\n
A\/B testing is a technique of evaluating two or extra variations of a product, function, or interface to find out which one performs higher based mostly on particular metrics. In a typical A\/B check, customers are randomly assigned to completely different teams, with one group serving because the management group and others as experimental variants.<\/p>\n
\n<\/figure>\n<\/div>\n \u00a0The diagram reveals an outline of the A\/B testing strategy<\/em><\/p>\n
This kind of experiment is also known as a randomized managed experimentation (RCE). The outcomes are then analyzed utilizing statistical methodologies to find out, with statistical significance, which model results in higher outcomes.<\/p>\n
A\/B testing is a well-liked RCE kind, however not the one one. The next are frequent RCE varieties:<\/p>\n
- \n
- A\/B<\/strong>: evaluating two variations (A vs. B). That is the commonest kind of check used for easy modifications, reminiscent of a brand new button coloration or headline.<\/li>\n
- Multi-variant<\/strong>\u00a0(or A\/B\/n): that is an extension of A\/B testing the place you evaluate three or extra variations to see which one resonates most with the viewers.<\/li>\n
- Multivariate<\/strong>: as an alternative of testing only one factor (like in A\/B testing), you check combos of a number of modifications without delay to establish the best combine.<\/li>\n
- Champion\/Challenger<\/strong>: the place a “Challenger” variant undergoes a number of refinements earlier than being examined in opposition to the present “Champion”.<\/li>\n<\/ul>\n
Whereas A\/B testing and its variations depend on randomization for exact comparisons, one other strategy is Pre\/Put up\u00a0testing that measures efficiency earlier than and after a change with out a management group. Although extra prone to exterior influences like seasonality, it may nonetheless present precious insights when randomization isn\u2019t doable.<\/p>\n
Past guesswork<\/h2>\n
After we redesigned\u00a0GoDaddy\u2019s area switch circulation<\/a>, we assumed simplifying the interface would enhance success charges. Whereas this did occur, we quickly realized that exterior elements like seasonality, advertising and marketing campaigns, and macro traits made it troublesome to isolate the true affect of our modifications. Have been the outcomes because of our redesign, or one thing else? The next diagram reveals A\/B testing on a simplified\u00a0hierarchy of proof pyramid<\/a>:<\/p>\n
\n<\/figure>\n<\/div>\nThat\u2019s the place A\/B testing made a distinction. Not like the pre\/put up evaluation we used earlier than, it allowed us to instantly evaluate two variations below the identical situations. A\/B testing helped:<\/p>\n
- \n
- give us\u00a0management<\/strong>\u00a0over exterior elements, so we might pinpoint precisely what was driving enhancements.<\/li>\n
- present extra\u00a0confidence<\/strong>\u00a0in our outcomes, so groups iterated quicker, making data-driven changes to attain the specified final result.<\/li>\n
- us\u00a0prioritize<\/strong>\u00a0modifications that delivered the best affect, guaranteeing we centered on probably the most precious enhancements first.<\/li>\n<\/ul>\n
Shifting to data-driven experimentation empowered our product engineering groups to make choices that not solely achieved our targets, but additionally delivered worth to our clients and stakeholders.<\/p>\n
Maximizing studying alternatives<\/h2>\n
One of many largest classes we\u2019ve discovered about A\/B testing is that statistically important losses may be simply as precious as statistically important wins.<\/p>\n
A few of our most beneficial buyer insights got here from failed A\/B experiments. For instance, our first iteration of a brand new\u00a0GoDaddy Auctions bidding circulation<\/a>\u00a0aimed to assist consumers win expired domains however had the other impact. As a substitute of a setback, the staff noticed it as a studying alternative, we:<\/p>\n
- \n
- reviewed outcomes to grasp the frictions throughout the circulation.<\/li>\n
- collected extra suggestions from surveys and analyzed session recordings.<\/li>\n
- introduced collectively the staff to brainstorm options based mostly on the findings.<\/li>\n
- developed a brand new bidding circulation expertise addressing the recognized frictions.<\/li>\n
- launched an improved model, resulting in a successful iteration.<\/li>\n<\/ol>\n
This collaborative, data-driven strategy not solely uncovered the foundation explanation for the difficulty but additionally led to an improved iteration that strengthened buyer success.<\/p>\n
The next picture reveals two A\/B experiment iterations:\u00a0<\/p>\n
\n<\/figure>\n<\/div>\nA\/B testing is a robust solution to validate concepts based mostly on buyer actions. Nevertheless it\u2019s only one piece of our buyer empathy toolkit. To grasp each the “what” and the “why”, GoDaddy groups are inspired to mix A\/B testing with a variety of\u00a0instruments that supply deeper insights into buyer habits and suggestions<\/a>. There is not any stronger motivator to make use of these instruments than a shedding experiment. In these moments, we achieve the perception to rethink, regulate, and create one thing higher.<\/p>\n
The Energy of Velocity<\/h2>\n
One of many largest advantages of A\/B testing is that it permits our groups to maneuver quicker with objective. That\u2019s significant progress. Pace is about how briskly you go, velocity is about transferring in the appropriate route. You may be working at full pace, however in the event you\u2019re getting in circles, you\u2019re not getting wherever. In enterprise, progress issues greater than movement.<\/p>\n
The next picture illustrates the distinction between pace and velocity:\u00a0<\/p>\n
\n<\/figure>\n<\/div>\nEarlier than adopting A\/B experimentation, our product cycles have been slower and generally disconnected from long-term technique. We\u2019d spend months constructing and launching options, solely to scramble afterward to research metrics. By the point we understood what didn\u2019t work, it was too late to regulate. We have been already centered on the following venture.<\/p>\n
However velocity isn\u2019t nearly transferring rapidly, it\u2019s about studying in a approach that compounds over time. As our CEO Aman famous, a\u00a0wholesome experimentation tradition balances constructive, adverse, and inconclusive outcomes<\/a>. If each experiment wins, we is likely to be enjoying it too protected as an alternative of pushing boundaries. Probably the most impactful experiments don\u2019t simply transfer metrics, they advance our understanding of the client and form product technique. A scattered, one-off experiment may drive a short-term win, however experiments aligned with product themes and clearly outlined buyer issues result in stronger product experiences.<\/p>\n
Catalyst for Collaboration<\/h2>\n
A\/B testing hasn\u2019t simply helped us construct higher merchandise. It reworked how groups collaborate at GoDaddy. Earlier than we adopted a structured strategy to experimentation, groups like product, design, engineering, enterprise analytics, and buyer success usually labored in silos. The A\/B testing course of itself has naturally develop into a unifying pressure. Every experiment is a cross-functional effort from the beginning, with everybody concerned in defining the speculation, designing the check, monitoring outcomes, discussing, iterating and sharing the learnings.<\/p>\n
GoDaddy aspires to construct nice merchandise and experiences that clear up actual buyer issues. Our gold commonplace are experiments that use randomization and sturdy statistical strategies that empower groups to:<\/p>\n
- present extra\u00a0confidence<\/strong>\u00a0in our outcomes, so groups iterated quicker, making data-driven changes to attain the specified final result.<\/li>\n
- Multi-variant<\/strong>\u00a0(or A\/B\/n): that is an extension of A\/B testing the place you evaluate three or extra variations to see which one resonates most with the viewers.<\/li>\n
- A\/B<\/strong>: evaluating two variations (A vs. B). That is the commonest kind of check used for easy modifications, reminiscent of a brand new button coloration or headline.<\/li>\n