Google is dropping its A/B testing product, Optimize, in September, and if you don’t have a plan to replace it yet, you might be left in the dark.
- The stakes: Budgets for innovation are lean, this is a hard time to get buy-in for a big tech investment with a recurring cost. You could lose your whole program budget if you can’t find a replacement, but the hard part is selecting a tool that ensures the future of the program and getting the budget for it.
- The pitch: You have a plan that will align your optimization strategy with revenue and cost savings and show positive ROI on the program quickly. You just need to get the budget approved to get this tool.
- The choice: Now you have to make sure you choose the right tool that will future-proof your program and activate your plan. You need to get it up and running quickly so you can start gaining momentum and getting the return.
- The promise: You need to get test velocity and wins, so that you are making good on the promise you have in this investment.
- The proof: You need to show the ROI in a short period of time and get the program in the black, this requires a measurement model and reporting that you can share with leadership that makes certain your program is seen as profit-center.
In today’s digital landscape, brands rely heavily on data-driven decision making and a carefully selected suite of technology to manage and optimize their websites, enhance user experience, and drive revenue growth. A critical tool in AB testing programs has been the free version of Google Optimize, which allows businesses to experiment with different variations of their website and measure the impact on user behavior and conversions. In September 2023, Google is sunsetting this product, and referring customers to adopt other products, most of which are not free.
What’s at Stake
With the discontinuation of Google Optimize, many brands are being forced to rethink their suite of technology and make new plans for the future of optimization. This shift will require more investment into infrastructure, training, and staff. In the current economic climate, where organizations are actively seeking cost-cutting measures amid declining innovation investments, it becomes difficult to get a budget for recurring costs in software licenses, and training staff to use them. In order for brands to keep their testing programs, they are going to have to find the budget to replace Google Optimize, but there are ways to recoup these costs and add momentum to the program by strategically selecting the right A/B testing platform.
If you are about to embark on finding a Google Optimize replacement and the budget to pay for it, first make sure you have a good read on where your optimization program is today, where it could be with the additional power of a new platform, and also what it would take to grow your practice to where you want it to be. Consider this migration from Google Optimize as a critical piece of your future growth strategy and this may be your big chance to ask for the budget you really need to secure the future of the program.
Selecting a reliable A/B testing platform is crucial as it directly impacts the accuracy, scalability, and flexibility of experiments. By choosing the right platform, brands can gather actionable insights to inform their optimization efforts and achieve long-term success. A/B testing platforms all have different strengths and weaknesses, and a wide variety of costs.
When selecting an A/B testing platform to replace Google Optimize, brands should evaluate several factors. These include ease of use, scalability, integration capabilities, statistical rigor, support for personalization, and advanced targeting options. By carefully assessing the features and functionalities offered by different platforms, brands can make an informed decision that aligns with their optimization goals and long-term vision.
Key features that really make the difference
When considering each of these features as they relate to choosing the best A/B testing tool for your company, here’s what you should keep in mind:
- WYSIWYG (What You See Is What You Get) refers to an interface that allows non-technical users to make changes visually without writing code, while the code-based approach requires coding knowledge to make modifications. Consider the technical expertise of your team. If you have non-technical members who need to make changes, a WYSIWYG editor would be more user-friendly. If your team is comfortable with coding, a code-based tool may provide more flexibility and customization options.
- Statistics Engine: The statistics engine of an A/B testing tool calculates the statistical significance and confidence levels of the experiment results. Look for a tool that uses robust statistical methods to ensure accurate and reliable results. It should provide features like p-values, confidence intervals, and sample size calculations to help you interpret the data effectively.
- Targeting and Personalization Rules: Targeting and personalization rules allow you to define specific audience segments for your experiments and deliver tailored experiences. Consider the sophistication of targeting options. Look for a tool that offers flexible targeting criteria based on user attributes, behavior, demographics, or any other relevant data. Advanced personalization capabilities can help you create more targeted and impactful experiments.
- Martech and Analytics Integration: Martech (Marketing Technology) and analytics integration enable seamless data exchange between your A/B testing tool and other marketing or analytics platforms. Check if the A/B testing tool integrates with your existing marketing technology stack, such as customer relationship management (CRM) systems, analytics tools, email marketing platforms, etc. Integration allows you to leverage existing data and streamline your workflows.
- Experiment Management: Experiment management features help you organize and track your A/B tests efficiently. Look for a tool that offers a user-friendly interface to create, schedule, and monitor experiments. It should provide options for segmenting experiments, setting experiment goals, tracking progress, and generating reports. Collaboration features like role-based access control and annotations can be beneficial for team collaboration. Remember that the specific requirements of your company might vary based on your team’s skill set, goals, and budget. It’s important to assess these features in the context of your unique needs and select a tool that aligns with your business objectives.
Developing a strategic optimization plan is key to maximizing the benefits of A/B testing, and a vital step to get a high return on testing. This roadmap should highlight clear optimization objectives around unlocking trapped revenue. This is revenue that is being lost by abandonment, inefficient ad spending, or areas to reduce cost by increasing self-service or other online behavior. By aligning tests with revenue and cost goals, brands can prioritize high-impact experiments, streamline the testing process, and ensure a return on their software investment.
An efficient A/B testing platform enables e-commerce brands to achieve high test velocity, allowing for rapid iteration and optimization. Test velocity refers to the speed at which experiments can be conceived, executed, and analyzed. With faster experiments, brands can uncover winning variations sooner, optimize conversion funnels, and continuously improve their website to drive revenue growth.
By identifying and implementing winning variations quickly, brands can maximize the time they are seeing an increase in conversion rates, average order values, and customer lifetime value, resulting in a net revenue gain for the organization. The effect of a winning test doesn’t last forever, come back to your winners regularly and see if they need a fresh experiment.
Investing in an A/B testing platform is a strategic decision that requires careful consideration of cost-effectiveness and return on investment (ROI). While there may be upfront costs associated with the software, a well-executed optimization plan can help recoup the investment within a short period. Be upfront with budget owners about the cost of ownership of an A/B testing tool, but also show them the ROI model you use to make your business case for testing.
Consider that testing not only helps you incrementally increase the revenue rate of your site, it also helps stop you from making costly unvalidated changes. There are both cost savings, and revenue generation values in testing. CRO is one specific strategy that works well for same-visit purchases, but also think about the value you get from a “do no harm” test, and calculate that into your program ROI.
In conclusion, having a clear vision for how A/B testing and optimization will impact your bottom line, and a strategy for turning your product investments into actualized business value is the key to moving forward with a Google Optimization replacement that will set you up for the future.
With these tips, we hope you are able to create that vision, put together a solid case, and find the A/B testing tool that works best for your business. If you are looking for a partner to help you build your case, create your strategy, or implement your new A/B testing platform, Roboboogie is here to be your guide.