Betty Schirrmeister

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Royal Mail’s Estimated Delivery Window – Another Successful Data Science Story

One of Royal Mail’s latest initiatives to improve customer experience and convenience was launched in April this year. Customers are now receiving information about their parcel deliveries a day in advance and also get shorter estimated delivery windows, down to a time frame of two hours. I will present some of the data science behind the project, as well as how we managed to make this project another data science success story for Royal Mail.

Following an Academic career in Evolutionary Genetics and Bioinformatics (University of Zurich - PhD; University of Bristol - Senior research scientist), I have switched to professional Data Science. Since 2016, I have been working on several projects for Royal Mail, supporting the business in making smarter, data-driven decisions. I have supported or lead several ambitious predictive analytics projects for Royal Mail, such as:

(I) Estimated parcel delivery times: Lead a team of 7 (data scientists and data engineers) to successfully implement the technical part for one of Royal Mails capital projects regarding predicting delivery windows. Resposible for all technical data-sciencerelated aspects of the project, interacting with multiple teams within the business and communicating with major stakeholders. (Talk) (II) Developed and implemented Royal Mails traffic forcast. Finding an end-to-end solution that is running live for two years, supporting Royal Mail's daily resource planning. (III) Lead, developed and implemented a predictive project with a savings potential in the millions for Royal Mail. Responsible for end-to-end solution, including deployment via webapp and usage evaluation.

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