Experimentation and Testing Programs acknowledge that the future is uncertain. These programs focus on getting better data to product and marketing teams to make better decisions.
We believe that research is an integral part of experimentation. Our research projects aim to identify optimization opportunities by uncovering what really matters to your website users and customers.
90% of the analytics setups we’ve seen are critically flawed. Our data analytics audit services give you the confidence to make better decisions with data you can trust.
Laura Borghesi, Senior Director of Growth Marketing at MongoDB, had painted a picture for her executive team of the opportunities experimentation offered. She convinced the business that experimentation would allow MongoDB to grow faster than it would do organically. But to do so, they needed to run a high velocity of experiments and use the data to inform decisions across the business. The problem was Laura had a small growth team and knew she needed help.
Laura put out an RFP and reviewed three experimentation agencies, choosing to partner with Speero.
“The reason why Speero won the RFP was that I loved your approach. I loved the fact that it was a straightforward, no-BS approach, and it was very clear at a high level and a deeper level.”
Laura Borghesi, Senior Director of Growth Marketing at MongoDB
The Solution
Working alongside Laura and the growth team, we delivered our full-service experimentation program. This involved everything you’d need to run an experimentation program; initial assessments, a suite of user research and data analysis, test prioritization, design and execution, and program management.
An example of what happens in the first six months of our full-service experimentation program
MongoDB wanted to experiment at a high velocity but was starting from zero. So, we began running experiments on the website where there was ample opportunity and a nimble infrastructure, making it easy to get things going quickly. This helped build confidence in experimentation and establish the necessary processes to execute tests.
We were mindful that test experience and understanding varied a lot across the business. So, we would adapt our communication style and content to suit the audience. This helped to bring everyone along for the ride without alienating anyone.
“You translate what experimentation means for different people in a very clear, digestible way. There's a lot of good statistical knowledge built into how Speero prioritizes things, and you are able to translate that to different people with different levels of knowledge.”
Laura Borghesi, Senior Director of Growth Marketing at MongoDB
As experimentation maturity increased, we helped establish a center of excellence team that sat across the growth function. The job of the CoE team was not to own any part of the user experience but enable others to run experiments.
Throughout the program, we took the opportunity to embed experimentation at MongoDB by sharing our processes and documentation, running internal workshops, and providing access to CXL training.
“In the beginning, people were scared of changing things so quickly. But then it became a mantra. It's like, no, we're not changing everything or disassembling the user experience. We’re collecting data to help you make better decisions. Everybody got so excited in the end, and now people are coming to the team to ask, can we do more experiments?”
Laura Borghesi, Senior Director of Growth Marketing at MongoDB
As confidence grew across the teams, the CoE role shifted to oversee the integrity of experiments, setting the overarching roadmap and helping to increase velocity by implementing different testing swim lanes.
The Results
Moving from zero to 100 tests a year pace within six months of working together.
Helping to fulfill the growth team's vision of building a center of excellence so people across the business could use experiments to make business decisions and drive growth.
Helped transform internal culture from being scared of experimentation to teams leading the way.
Key Takeaways
1
Building a self-service CoE model helps increase test velocity.
You need to monitor other metrics alongside velocity to develop a successful experimentation program.
While increasing velocity might be your goal, you need to balance it with aspects such as test complexity. Use our velocity vs. complexity graph as a conversation starter on this topic.
Let’s kick things off. Tell us what you’re looking for
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