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How to avoid the six most common analytics data visualisation mistakes
Applied Marketing Analytics: The Peer-Reviewed Journal,
1
(4),
320-330
(2015)
Abstract
Digital and marketing analysts spend a considerable amount of time mining data to monitor customer trends and find insights that will propel the business forward. Communicating these insights to stakeholders in a comprehensible and compelling way continues to present a significant challenge, however. This paper examines six common mistakes analytics practitioners make in creating and delivering memorable data visualisations that are acted on by their stakeholders. It covers strategies to avoid common pitfalls such as data hygiene, choosing appropriate chart types, leveraging Gestalt psychology and creating simple visuals that communicate the data story clearly and aid in immediate and accurate cognition.
Keywords: data visualisation; digital storytelling; data presentation; Gestalt psychology; digital analytics; web analytics; analytics; business intelligence
The full article is available to subscribers to the journal.
Author's Biography
Lea Pica is a seasoned digital analytics practitioner, professional speaker, online marketer and presentation consultant with over 11 years’ experience of bringing thoughtfully presented data stories to her organisations. She has managed digital marketing programmes for Scholastic, Victoria’s Secret, Bath & Body Works and Ralph Lauren. Most recently, she built the foundation for digital analytics practices in three business units while at Prudential. Today, Lea offers executive presentation consulting, individual coaching and private corporate training for web analysts and marketers who wish to take their presentation and data visualisation skills to the next level. She also provides digital analytics and marketing expertise to the Robert Wood Johnson Foundation as a Team Demystified consultant.
Citation
Pica, Lea (2015, October 1). How to avoid the six most common analytics data visualisation mistakes. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 1, Issue 4. https://doi.org/10.69554/AAYU6082.
© Henry Stewart
Publications LLP
Publications LLP