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Make Your A/B Testing More Effective and Efficient

Anjali Mehra, Senior Director of Product Analytics at DocuSign, discusses the role of A/B testing in data experimentation and how it can impact an organization. They cover DocuSign's analytics goals, A/B testing mech

feb 2023

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Guest
Anjali Mehra

Anjali Mehra is the Senior Director of Product Analytics, Data Science, Experimentation and Instrumentation at DocuSign. Throughout her career, she has also worked in marketing analytics and customer analytics, giving her significant insight into the impact of A/B testing and experimentation across all multiple parts of an organization.


Photo of Richie Cotton
Host
Richie Cotton

Richie helps individuals and organizations get better at using data and AI. He's been a data scientist since before it was called data science, and has written two books and created many DataCamp courses on the subject. He is a host of the DataFramed podcast, and runs DataCamp's webinar program.

Key quotes

Organizations should keep very close eyes on A/B tests while they are running. There are a lot of things that can go wrong, and teams make mistakes. It’s possible that you did something that's now introduced an error in your product or feature, but also it's possible that you influence a user behavior in a way that's really, really hurting you. For example, let’s say your test period is four weeks. Within just the first few days, if you aren’t monitoring your test, you may not notice that your order volume is going down significantly, and it will stay that way for the full duration of the test unless you catch it early. So, it's critical to monitor any test while it's running.

Whenever someone has a new idea, there is always a lot of excitement and urgency to jump into testing it right away, so spending some time in the planning phase may seem a little bit like a slowing you down, but it really does pay back dividends in the end. It’s important to invest time into  building a strong hypothesis because not having one just makes everything take longer and risks more since you can’t fully trust the integrity of the results. Both rushing the process and trying to measure against too many KPIs can derail any testing process and hurt overall business value.

Key takeaways

1

You need a strong hypothesis and a strong primary KPI to make A/B testing an effective and efficient use of time and resources.

2

A/B Testing is a team effort that requires thorough planning, active monitoring, and constant tweaking to get good results.

3

A cost-benefit analysis is a great way to identify if what you want to test whether or not the results you are aiming for are going to drive more value than the resources it would require to test and implement anything new.

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