Today, it’s not enough to be versed in analytics. You need to understand how to use analytics to solve the specific business problems you own. Those of us in the digital world may all be using the same tools—Google Analytics, or Adobe, or any other stack—but we’re using them in very specific ways for very different purposes. The nuances are critical.
If you are leading experimentation on your organization’s site or in your product, you need to adopt a particular lens for data analysis to answer the types of questions that an experimentation role will ask of you.
In this post, our friends at Widerfunnel walk through the three stages of experimentation from ideation through execution and into analysis, and highlight how analytics and data can support you at every turn.
- How to use analytics data to generate and validate an evidence-backed hypothesis,
- How to measure experiments to get insights in addition to wins and losses, and
- Ultimately, how to push your experimentation program forward.
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