Experimentation is essential because it enables organisations to make decisions based on evidence rather than assumptions. Instead of relying solely on opinions, teams can test ideas with real users to understand what actually drives engagement, satisfaction, and business performance. An effective experimentation programme allows organisations to validate new features, optimise customer journeys, improve conversion rates, and reduce the risk associated with product development. As customer expectations continue to evolve, experimentation provides a structured way to learn quickly and adapt to changing behaviours. Businesses that embed testing into their culture are often better equipped to innovate, respond to market shifts, and deliver experiences that meet both customer and business needs.
Don't let the sun go down on your experimentation programme
What to consider now Google Optimize and Optimize 360 are no longer available.
Google Optimize and Optimize 360 are no longer available
This has left many Marketers and Product Managers scratching their heads and faced with a need to think of alternatives to support their experimentation programme.
Google have hinted at enabling people to continue experimenting with GA4 but it seems unlikely a direct replacement is coming in, since they haven’t explicitly mentioned it.
The news provides a good opportunity for all companies to pause and take stock of their experimentation programme, whether they are Google Optimize users or not. The time is now to invest in evaluating current and future tools, as a sign of intent, and a commitment to the future of experimentation.
The advantages of moving to a more mature tool
On the heels of the sunsetting news have come countless posts singing the praises of Google Optimize with many also pointing out its limitations. Optimize has always been a starter tool since its inception, able to fly under the radar despite only allowing programmes to mature so far.
Given this, now is the perfect time to consider acquiring a more advanced tool for experimentation, for which there are many advantages.
Removing limitations on what can be tested and embedding experimentation within product teams
Tools such as Google Optimize, which are implemented as a tag on the client-side, have long been lamented by engineering teams for the potential impacts on page performance and site resilience. Experiments implemented in this way are also limited in terms of what you can test.
Choosing a tool that offers both client-side and server-side capabilities brings experimentation to more teams, allowing product teams to manage experiments, with developers building tests in the native codebase. This vastly increases the scope of what can be tested, where you can test and how rapidly; you are no longer limited to design changes on the website, instead almost anything is possible across any digital touchpoint with your customers.
It is important to have both client and server-side capability available. It’s not a question of client vs server side testing. Being able to deploy experiments both client and server side is the ideal scenario, providing the most flexibility and agility to organisations and teams. Only having one method will limit your test velocity and the types of experiments you are able to run, ultimately frustrating everyone involved.
Closer integration with your existing technology stack
Google Optimize and other client-side tools are implemented onto websites with minimal fuss and often without developer involvement. But this implementation is typically shallow and limits how integrated your tool is.
Your next tool could allow you to embed testing within the release pipeline for your developers, allowing teams to release with the confidence that changes will not have a negative impact and will promote innovation. It may also allow you to experiment directly within your content-management system, with content editors able to discover which content draws the most interest or which headlines to use.
Integration with your data architecture could allow for a richer exchange of information with your Central Data Platform (CDP) or Digital Marketing Platform (DMP), and better targeting/personalisation with both on-site and off-site communication, e.g. ads, emails, push notifications.
Support teams from the tool supplier
Part and parcel of most platforms is a broad catalogue of articles around not only the usage of the tool but the practice of experimentation. Signing on and investing with a optimisation product normally comes with some form of client success manager who ensures that you’re getting the most out of it.
This support and guidance can help those scaling or just getting started with experimentation to think beyond the tooling and into deeper aspects such as experimentation strategy and how to drive growth, the skills you want to bring into teams working on experimentation, and further integrations you can make with other technology providers.
It’s worth remembering not all paid-for tools will offer the same level of support but investing in one can mean a supplier becoming invested in your experimentation programme. This can drive adoptions and growth while helping you achieve a greater return on your investment.
Going, going, gone
Ultimately, Google Optimize is gone. But, there are hundreds of testing suppliers available and anyone looking to move should take their time to carefully consider which one is right for them. However, treat this inflection point as a chance to take your experimentation to the next stage in your journey.
If you want to chat through any of this, feel free to reach out to me or any of the team here at Foolproof.
Experimentation - FAQs
The retirement of Google Optimize provided many organisations with an opportunity to reassess the maturity of their experimentation programmes. Rather than simply replacing one tool with another, businesses should evaluate their broader experimentation strategy, including processes, governance, technology, and team capabilities. This is an ideal time to identify gaps, establish testing roadmaps, and determine how experimentation can support long-term digital transformation goals. By taking a strategic approach, organisations can move beyond basic A/B testing and create a more sophisticated framework that delivers continuous learning and optimisation across products, services, and customer experiences.
A mature experimentation platform enables organisations to test a wider variety of experiences, gather more reliable insights, and scale experimentation across multiple channels. Advanced platforms often support both client-side and server-side testing, making it possible to experiment with user interfaces, business logic, personalisation strategies, and product features. These capabilities help organisations accelerate learning cycles, increase testing velocity, and improve collaboration between product, engineering, data, and marketing teams. Over time, a mature experimentation platform becomes a strategic asset that supports innovation and continuous optimisation.
Client-side experimentation primarily focuses on visual and user interface changes that are executed within the browser, making it useful for testing content, layouts, and design elements. Server-side experimentation, on the other hand, occurs within the application's backend and allows teams to test deeper functionality, algorithms, and product features. Both approaches offer unique advantages, and organisations that utilise both methods gain greater flexibility in what they can test. Combining client-side and server-side experimentation creates more opportunities to improve customer experiences while supporting broader digital transformation initiatives.
Experimentation provides valuable insights that help teams make better decisions and prioritise initiatives with the highest potential impact. Product teams can validate features before investing in development, marketing teams can optimise messaging and campaigns, and customer experience teams can identify improvements that increase usability and engagement. Because experimentation generates measurable results, it helps organisations align stakeholders around evidence rather than opinion. This improves efficiency, reduces risk, and increases the likelihood of delivering successful customer-focused outcomes.
A successful experimentation programme is embedded within organisational culture and supported by clear processes, leadership commitment, and cross-functional collaboration. It involves continuously generating hypotheses, running tests, analysing results, and applying learnings across the organisation. Rather than viewing experimentation as an occasional activity, leading organisations treat it as an ongoing capability that drives innovation and optimisation. The most effective programmes create a culture of learning where evidence informs decision-making and continuous improvement becomes a core business practice.