Real time out of stock substitute finder for online shopping
Lead Participant:
GATI AUTOMATA LIMITED
Abstract
Originally a qualified accountant, I started my career with Citi Bank and later moved to Financial Advisory at KPMG before gaining 10+ years of experience in retail marketing platforms. I am passionate about women entrepreneurship and innovation.
Online shopping is growing at an unprecedented rate, with the number of digital buyers continuing to climb up every year. In 2019, an estimated 1.92 billion people purchased goods or services online across the globe. According to Adobe's analysis COVID 19 accelerated this behaviour and now shoppers buy for even essential supplies online. Online purchases of common staples increased upto 60%; the virus protection category of products such as hand sanitisers, gloves, masks and antibacterial sprays have surged by 817%. The typical out of stock level averages around 10% for online retailers, but during the pandemic the unprecedented demand of goods, travel restrictions and closure of factories led to supply chain disruptions and out of stock levels were at historic high of upto 40%.
According to eConsultancy, although intense stockpiling has reduced in recent months, logistical and fulfilment issues led to shortages in stock for all major retailers. This shortage resulted in consumers visiting multiple stores both online and offline to find all the products they were looking for.
[][0] Shopper.com aims to enhance its back in stock notification feature with a machine learning classifier that gives a list of available substitute products. Out of stock notifications and related products information is available with few major retailers including Amazon. However, the proposed system can ensure the discovery of same or substitute products from the same retailer or other online retailers ensuring shoppers complete the shopping online.
[][0] Shopper.com is a fast-growing startup based in Birmingham. Shopper.com[][0] is already helping thousands of shoppers in discovering products and deals thus improving their online shopping experiences. Shopper.com[][0] is developing an innovative system by proving key technologies and incorporating them to its current platform. Areas of research and development include use of machine learning, advanced data processing and event processing which is at the leading edge of R&D in eCommerce systems. Shopper.com[][0]'s project aims to develop state-of-the-art machine learning processes to support eCommerce infrastructure. It will allow online shoppers to have a better shopping experience encouraging them to shop from home thus providing a safer sail through the crisis.
[0]: http://Shopper.com
Online shopping is growing at an unprecedented rate, with the number of digital buyers continuing to climb up every year. In 2019, an estimated 1.92 billion people purchased goods or services online across the globe. According to Adobe's analysis COVID 19 accelerated this behaviour and now shoppers buy for even essential supplies online. Online purchases of common staples increased upto 60%; the virus protection category of products such as hand sanitisers, gloves, masks and antibacterial sprays have surged by 817%. The typical out of stock level averages around 10% for online retailers, but during the pandemic the unprecedented demand of goods, travel restrictions and closure of factories led to supply chain disruptions and out of stock levels were at historic high of upto 40%.
According to eConsultancy, although intense stockpiling has reduced in recent months, logistical and fulfilment issues led to shortages in stock for all major retailers. This shortage resulted in consumers visiting multiple stores both online and offline to find all the products they were looking for.
[][0] Shopper.com aims to enhance its back in stock notification feature with a machine learning classifier that gives a list of available substitute products. Out of stock notifications and related products information is available with few major retailers including Amazon. However, the proposed system can ensure the discovery of same or substitute products from the same retailer or other online retailers ensuring shoppers complete the shopping online.
[][0] Shopper.com is a fast-growing startup based in Birmingham. Shopper.com[][0] is already helping thousands of shoppers in discovering products and deals thus improving their online shopping experiences. Shopper.com[][0] is developing an innovative system by proving key technologies and incorporating them to its current platform. Areas of research and development include use of machine learning, advanced data processing and event processing which is at the leading edge of R&D in eCommerce systems. Shopper.com[][0]'s project aims to develop state-of-the-art machine learning processes to support eCommerce infrastructure. It will allow online shoppers to have a better shopping experience encouraging them to shop from home thus providing a safer sail through the crisis.
[0]: http://Shopper.com
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| GATI AUTOMATA LIMITED | £50,000 | £ 50,000 |
People |
ORCID iD |
| Sonu Bubna (Project Manager) |