High-impact testing that inspires action
Abstract
This paper outlines a simple framework for data storytelling that communicates experimentation programme test results focused on stakeholder actionability rather than test performance. This framework can be applied both as a narrative and a visualisation guide within a company’s existing analytics tool. Its benefits for a business mirror the true returns from investing in an experimentation programme — not the percentage lift of a single test treatment, but the application of test results to how the business might de-risk decision-making, optimise marketing efforts, or prioritise its product roadmap. This approach supports experimentation maturity by (1) giving stakeholders access to test learnings, (2) sharing how stakeholders can use learnings, (3) building institutional intuition of how to talk to different customer audiences or develop new products/features, (4) enabling insights to guide next right actions, and (5) helping others understand the potential application of experimentation and the benefits of testing.
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Author's Biography
Valerie Kroll is an Optimisation Director at Search Discovery, where she spends her days crafting evidence-based hypothesis libraries, utilising AI features in testing platforms, and creatively solving tough problems to ensure long-lasting business results. Prior to her current role, Valerie established and led the first digital analytics and optimisation programmes at UBS and the American Medical Association. She currently serves as the Chairwoman of the Digital Analytics Association.
Julie Shallman Hoyer is a Senior Analytics Associate at Search Discovery, where she uses her knowledge of analytics and optimisation to help clients find actionable solutions to complex business questions. She holds bachelor’s and master’s degrees in applied mathematics and has worked as a senior data analyst for clients across various industries.
Citation
Kroll, Valerie and Hoyer, Julie Shallman (2022, February 1). High-impact testing that inspires action. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 7, Issue 3. https://doi.org/10.69554/XLRG3742.
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