Ultimate 5-Step Guide to Transitioning from Google Optimize / Blogs / Perficient

The upcoming Google Optimize sunset has made it crucial for businesses to find reliable replacements. In today’s data-driven environment, the functionality of data experimentation tools is paramount. Relying on A/B testing tools for measuring and enhancing website optimization is key to increasing user satisfaction and conversion rates. By September 30, 2023, with Google Optimize’s discontinuation, businesses using this tool need to transition swiftly to ensure uninterrupted metric tracking, efficient advertising CPA, and better website metrics.

Watch our video guide on the Google Optimize sunset replacement and transitioning tips:



This video presents a structured strategy for businesses transitioning from Google Optimize to more robust tools. Among the alternatives, Optimizely stands out for its capabilities in experimentation, user experience personalization, and seamless integration with Google Analytics 4.

1. Preserving your Google Optimize History:

A thorough understanding of what works and doesn’t work for your audience can be gained by tracking the effectiveness of various strategies and elements over time and keeping track of the history of your website experiment results. This data-driven methodology aids in the optimization of your website’s overall performance and future experiments.

Document all actions taken in Google Optimize. A data-driven approach, backed by consistent analysis of past experimentation results, provides insights into what resonates with your audience. From color choices to layout designs, understanding these data trends can guide future decisions and cultivate a culture centered on continuous improvement.

Documentation Checklist should include:

1. A/B testing results
2. Personalization campaigns
3. Goals and Metrics setup
4. Google Analytics audience setup
5. Development workflows and troubleshooting documentation

2. Choosing a Google Optimize Sunset Replacement:

As Google Optimize phases out, finding a replacement becomes vital. Your next tool should not only mirror Google Optimize’s capabilities but also cater to your evolving needs, whether that’s in terms of functionality, cost, or compatibility with existing systems.

• Factors to consider:
  1. Price
  2. Capabilities and features
  3. Access level collaborators
  4. Easy of use and flexibility:
  5. Easy to understand analytics and data
  6. Compatibility with Google Analytics 4
  7. Future proof

Start with a basic package from a tool like Optimizely Web Experimentation and grow as needed. Here are several reasons why Optimizely is an excellent upgrade from Google Optimize.

Remember, the best tool for you depends on your specific needs and context. It’s a good idea to take advantage of free trials when they’re available to ensure the tool is a good fit before making a commitment.

3. Implementation of the A/B testing and personalization tools

Transitioning to a new tool, such as Optimizely, should be straightforward—often as simple as integrating a JavaScript line into your website’s header.

4. Recreating System for Accelerated Experimentation:

Accelerated experimentation is a process that allows businesses to rapidly test, iterate, and implement changes to their products, services, or processes based on real-time data. This approach leverages advanced technology, data analytics, and a culture of continuous learning to facilitate quick decision-making and improve business outcomes.

1. Improved workflow
2. A collaborative team working together in experimentation workflow
3. Introduction of GA4 audiences, events and integrations
4. Post analysis directly in tool and GA4 analysis

By improving these areas, companies can speed up their experimentation processes, allowing them to quickly identify what works and what doesn’t, and implement changes that drive better business outcomes.

5. Experimentation System Established

For a company to enhance its experimentation maturity and amplify results using an experimentation system, it needs to instill a culture where decisions are grounded in data, permeating every level of the organization. This involves adopting advanced experimentation and analytics tools capable of supporting growth and facilitating intricate experiments while producing actionable insights. Success should be articulated through clear KPIs, tailored to business objectives, with an established systematic experimentation process encompassing everything from hypothesis generation to results application. To manage the complexity, multiple teams should leverage the same system, driving organized projects, while isolating testing tracks for mutually exclusive experiments. Integration with external systems like Snowflake, Customer Data Platforms (CDPs), and real-time segments becomes imperative. Furthermore, fostering cross-functional collaboration and sharing of insights, along with cross-project events and comprehensive website optimization, empowers the experimentation process. Finally, a cycle of incessant iteration and optimization based on experiment outcomes can lead to informed decisions, minimized risk, continuous enhancement, and innovation, yielding improved offerings, efficient marketing strategies, and increased revenue.

• Multiple teams leveraging the same system with organized projects
• Isolated testing tracks for mutually exclusive experiments
• Integration of external systems like Snowflake, CDP and real time segments
• Cross project events and full website optimization

In conclusion, as businesses in our data-driven era pivot to new tools for optimization and experimentation due to the termination of Google Optimize, it becomes vital to make a smooth transition. This guide aimed to equip you with an understanding of the importance of documenting your website experimentation results history, assessing new A/B testing tools, and streamlining the implementation of a new tool like Optimizely. As your business scales, so should your experimentation maturity. The integration of multiple teams, adoption of isolated testing tracks for mutually exclusive experiments, and integration with external systems like Snowflake and CDP are keys to a more sophisticated experimentation system. Furthermore, the continual iteration and optimization based on experiment outcomes allow for data-driven decision-making, risk reduction, and innovation. Ultimately, this culture of continuous improvement can lead to improved offerings, more efficient marketing strategies, and, most importantly, increased revenue and growth. As Google Optimize sunsets, it’s not the end but a new opportunity to further streamline your data experimentation efforts and drive growth.

Please get in touch with us if you have questions or want to learn more about how we can help you build a world-class strategy to keep your platforms up-to-**** and grow the results of your current platforms.