Helping online stores reduce returns and carbon emissions
Houston Analytics uses SAS® Viya® to help online stores reduce returns and carbon emissions
Reducing carbon footprint
Houston Analytics achieved this using • SAS® Viya® • Microsoft Azure
The sportswear chain Stadium were looking to limit the number of returns from online purchases and thus reduce their carbon footprint, so Houston Analytics developed a real-time size recommendation engine.
Shopping for shoes online can be a challenge, it takes time for delivery and you are not 100 percent certain that they will fit properly when they arrive. There are lots of brands and sizes to choose from but there isn’t a way to try them on virtually. This leads to customers purchasing more than one size at a time and returning the ones that don’t fit, adding to transportation costs and CO2 emissions.
Stadium would like to limit the number of returns on shoes and become more sustainable in the future. For the SAS® 2020 Hackathon, a team from Houston Analytics partnered with the sportswear chain to analyze their online store data and create tailored size recommendations for their customers.
How can we have an impact on returns and assist Stadium with their sustainability goals? For this Hackathon, we analyzed online shopping data from Stadium to create a holistic understanding of the reasons why some purchases are returned and provide analytical insight to avoid unnecessary return Antti Merilehto Head of growth Houston Analytics
Using SAS® Viya® and Jupiter Notebook, the team was able to assess that many customers purchased more than one pair of shoes in a similar size, and then returned all of them except for the one that fits.
Especially when a customer purchases shoes from a brand they have not tried before, they are often unsure of their size and therefore, take home several pairs to make sure they get one that fits. This leads to a lot of additional administration and transportation including unnecessary carbon emissions.
With this project, each time a customer is purchasing from a new brand, their purchase history will be available. This information is then compared to other customers with similar profiles and the system gives a prediction of which size is correct for them.
The project enables real-time recommendations and limits the number of unnecessary returns.
It is expected that Stadium will improve their customer support and satisfaction ratings. At the same time, overall CO2 emissions through unnecessary returns are reduced.
Houston Analytics – Facts & Figures
2
sizes of same shoe is ordered online
1
real-time size recommendation engine
2020
Participant in the 2020 SAS® Hackathon
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