As part of Solutions Review's Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories, the EMEA VP of Marketing Technology at Valtech offered up some questions businesses can ask when looking for a replacement platform.
Dealing with the occasional platform sunset is both inevitable and troublesome. Right now, Google Optimize—the world’s most used A/B testing tool—is set to sunset later this year. Businesses must find an alternative fast so their experimentation efforts don’t lose momentum or halt entirely. This isn’t an easy task; they’ll need to learn a brand-new platform and migrate over historical data and learnings.
Structured experimentation helps businesses understand customer habits, test their product-market fit, and verify their creativity to drive better business outcomes. It needs to offer a complete view of the customer journey to make sure experiments don't operate in siloes. Through a structured framework of testing, measuring, and learning, experimentation thrives.
What should decision-makers ask when selecting a replacement for a search engine?
1) Can We Scale With the Alternative?
Businesses need to assess how well an A/B testing tool meshes with their digital environment. It isn't IT who will use the platform. Everyone else is more or less true.
The most important characteristic of an experiment is flexibility. What are we talking about by this? Planning based on past learnings and using a data visualization tool is part of experimentation. As their experiments become more complex, teams need an experimentation tool that makes all this seem simple.
Businesses need to be able to adapt to change at a fast pace. Experiments should become more complex over time. A platform that has a wide range of features is a good choice. It isn't just one that helps you identify the best version of the content. If you don't do this, you'll need to migrate again once you hit the ceiling. You will grow into it, like children's clothing.
It is important to consider visual and data when looking at experimentation platforms. Content marketers use many platforms to experiment with images, colors, and copy. It is important for these tools to perform well on data. It's important to make it easy to group target audiences. This must be mapped to tangible changes.
It is easier to get experiments off the ground with some of the tools that are available. They are weak on data, which makes it difficult to scale and effectiveness.
It is important that a platform can integrate with other tools. It's important that your platform is as integrated as possible. Consider how to make your website more attractive to visitors. This is not a full visual tool. This isn't to say that it's perfect, but it's more effective if you combine it with the other tools. It's far from it. Google is not open to the public It isn't easy to get the data back again. It's almost impossible to get data back into a data visualization platform.
What are the alternatives? Data can be integrated directly with the tools like Optimizely's Web Experimentation. Users can choose to work with their existing CDP. This is a lot more advanced than the previous one. Businesses can use these tools to think more ambitiously about integrations after the sunset of the search engine. This is carrying with it a degree of complexity.
2) Which Parts of the Customer Funnel Need Optimizing?
Businesses can use a good experimentation platform. Depending on the needs of your business, each stage needs a different approach.
Top-of-Funnel
It's important to build brand awareness and focus on the problems your business can solve. You can use experimentation to learn more about customers' pain points. It can help make sure you communicate them in a way that meshes with your audience. B2B markets have the average brand serving five to seven industries. Each has different pain points, a different vocabulary, an appetite for detail, and more.
If you don't have the right tool in place, you'll end up with a lot of employee hours needed.
Mid-Funnel
Building relationships with your audience is the key to mid-funnel success. The team has a lot of ideas. Do you know what group it appeals to? It is important to A/B test your tactics before you implement them.
Bottom-of-Funnel
When you map your audience's challenges to specific products and services, you're called bottom-of-funnel. You are showing how you will solve their problems together. Giving too many options can be overwhelming and experimentation is about maximizing product-audience fit. The result isn't an increase in conversions but shorter lead times.
If you want to maximize the customer funnel or target multiple industries, you need to choose an experimentation platform with a high degree of automation. Businesses often try to save money by selecting a tool with less automation capabilities. Increased employee hours create a higher cost of ownership. An impressive level of automation wasn't offered by the search engine. Businesses can try to improve this area.
3) Can We Link Optimizations to KPIs?
You should link the A/B testing results to the metrics. What is the point of learning that the color red is slightly better than the color blue?
It's a big mistake for some businesses to focus on metrics that aren't really important. You don't need to change anything to be effective. A simple option is to make sure that any experiments do well. Experiments may be linked to revenue growth. This is the end result of all the metrics. You need to demonstrate how experimentation has led to increased leads, sales, or deals. You will be able to have more productive discussions with stakeholders.
You will probably struggle to justify the investment if your business isn't ready to link experiments to key performance indicators. Time is of the essence and you don't have the luxury of waiting until you can adopt a more mature approach, so now is the time to decide where you want to be, build a business case around this, and work out tangible steps to get there.
4) Can We Calculate the ROI of Our Experiments?
Yes in a way. The platform you use to experiment should help you calculate the return on your investment. If you are starting A/B testing for the first time, don't try to calculate the return on your investment. It will be wildly incorrect. It's best to run five to ten experiments. The business value of experimentation can be calculated based on this data.
It isn't just about the arrows that point upwards. Sustaining current performance is also part of the equation. Incremental improvements are being made by your competitors. You will eventually see your performance decline if you don't follow their lead.
5) Is My Business Ready for Experimentation?
Any business is prepared for experimentation. Pick option one if there is a choice between investing in experimentation, personalization, or increasing traffic. Experiments should take place before personalization and the same tool should be used. Experiments allow businesses to improve their funnel, which leads to investments in traffic or personalization being more effective. Influencing product design, go-to-market strategy, and more are some of the things that can be learned from experimentation.
If you already started your experiment and are thinking about stopping, don't. When gains become more incremental, many businesses scale down or stop experimenting. Companies that start A/B testing typically see revenue increases. These improvements decrease over time, but they are still important.
Competitors will be able to catch up if you take off your foot. Continuous experimentation is the only way to stay up to date. Prototyping and testing is one of the reasons start-ups and scale-ups focus on this area.
Moving On to Better Things?
It's important to remember that choosing a new experimentation platform isn't just about avoiding instability and disruption. It's an opportunity to assess, evaluate, and implement a more integrated solution. Moving on to better things.
EMEA VP of Marketing Technology at ValtechCarsten Schou Hjæresen is the EMEA VP of Marketing Technology at Valtech, where he specializes in bridging the gap between data, technology, and business decision-makers to drive growth and achieve marketing objectives for clients. He serves as an advisor on the development and implementation of marketing technology strategies, collaborating with cross-functional teams to identify and prioritize new solutions and platforms, managing budgets and resources, overseeing the development and maintenance of martech infrastructure, and leading the training and adoption of tools among clients’ marketing and sales teams. 
