If you want to improve your business operations, you need to experiment with new ideas and solutions. But how do you design, test, and evaluate your experiments without wasting time, money, and resources? Here are some best practices for experimenting with new operational ideas and solutions.
Before you start experimenting, you need to clearly define the problem you want to solve and the goal you want to achieve. What is the current situation, what are the pain points, and what are the desired outcomes? How will you measure your success and failure? Having a clear problem statement and a SMART goal (specific, measurable, achievable, relevant, and time-bound) will help you focus your experiment and avoid scope creep.
It is vital to ensure that any project or initiative begins with a thorough verification process involving the team, confirming that a genuine problem exists. Without a problem, there is no need for a solution, and building something without a genuine purpose will likely lead to poor adoption rates from the team. Therefore, it is essential to establish a clearly defined problem and goal before embarking on any efforts. Understanding the current situation, identifying pain points, and defining desired outcomes are critical steps in this process. Additionally, it is crucial to establish metrics to measure both success and failure. Creating a clear problem statement and ensure that the experiment remains focused.
Next, you need to generate possible solutions or hypotheses for your problem. A hypothesis is a testable statement that predicts how a change in your operation will affect your goal. For example, if your problem is low customer retention, your hypothesis might be: "If we offer a loyalty program, then we will increase customer retention by 10% in six months." You can use brainstorming, research, or data analysis techniques to generate hypotheses. Then, you need to prioritize them based on their potential impact, feasibility, and alignment with your strategy.
At @sortlist we do use the Impact/Effort matrix which is a tool used to prioritize solutions or hypotheses based on their potential impact and effort. It allows you to assess the impact of each solution and the effort required to implement it. By placing each solution on the matrix, you can easily identify the high-impact, low-effort solutions that should be prioritized. This matrix helps in making decisions and allocating resources effectively.
Once you have your hypotheses, you need to design and run experiments to test them. An experiment is a controlled way of testing a hypothesis under specific conditions and measuring the results. You can use different methods of experimentation, such as A/B testing, pilot testing, or prototyping, depending on your context and resources. The key is to design your experiment with a clear and consistent methodology, a valid and reliable measurement system, and a representative and sufficient sample size.
In revenue operations (RevOps), it's important to avoid overengineering and focus on prototyping or creating a minimum viable product (MVP) first. This allows you to collect feedback, make improvements, and measure the results at each step. It's crucial to ensure that your experiments/projects are designed with a clear methodology, utilize a valid and reliable measurement system, and have a representative and sufficient sample size. This approach enables data-driven decision-making and optimization in RevOps.
After you run your experiments, you need to analyze and learn from the results. You need to compare the performance of your experimental group (the one that received the change) and your control group (the one that did not) and see if there is a significant difference in your goal metric. You also need to look for any unexpected outcomes, confounding factors, or biases that might affect your results. Based on your analysis, you need to draw conclusions and recommendations for your next steps.
Make sure your tool has analytics of adoption. It's the only way to check if your program/solution has been adopted by the team or not. Some examples? - Tango: a step-by-step guide tool for your team. They have analytics embedded so you can check your enablement program is adopted - Hubspot: A CRM that gives you a sense of adoption, for example with Play Books. It tells you how many times it has been seen and used.
Finally, you need to implement and scale the solutions that worked in your experiments. You need to plan how to roll out the changes to your entire operation, communicate them to your stakeholders, and monitor their impact. You also need to document your learnings and best practices for future reference and improvement. Remember that experimentation is an ongoing process, not a one-time event. You should always look for new ways to optimize your operation and solve new problems.
Implementing and scaling solutions can be the MOST challenging part, especially in a dynamic work environment like revenue operations. It requires careful planning, effective communication, and continuous improvement. It's easy to add new solutions. Less to optimize or kill existing ones...