Sparkbox builds machine-learning software that helps brand merchandising teams forecast demand, optimise pricing and make inventory decisions to reduce dead stock. The platform aims to improve retailer profitability while supporting sustainability by preventing over-ordering. The company plans to double its team and onboard additional fashion, home and seasonal retailers where dead stock is a recurring challenge. Sparkbox was founded in 2019 by Lindsay Fisher, Matthew Wong and Kevin Blackmore and emphasises its founders' retail experience. The startup positions its product as a data-driven replacement for legacy merchandising decision-making processes. Financial details in the article note a recent funding event to accelerate growth. Sparkbox is a cloud platform that helps retailers sell more inventory more profitably by optimising their pricing. It uses machine learning to understand how customers respond to price changes in real time and makes specific pricing recommendations tailored to business goals. The solution can be set up in just a couple of weeks and the team anticipates it will be especially helpful for fashion and seasonal retailers as the industry emerges from the Covid-19 crisis. Founded in 2018 by four co-founders who are ex-retailers and retail consultants, the team previously delivered custom pricing solutions for major UK brands including The Very Group, Arcadia, JD Williams, and Superdry. In March the company raised an SEIS investment from London-based VC AI Seed, and the deal was supported by Jon Gill and Matthew Howells at TLT. Sparkbox has received recognition including winning Tech Nation’s Rising Stars 2.0, winning Fashion District’s Retail Futures Innovation Challenge, participating in Google for Startups Immersion, and having two founders named to Forbes 30 Under 30 Europe; with AI Seed's support the team plans to grow its team and customer base in 2020.

Total raised

$2M

Funding rounds

2

Latest round

Equity

Latest activity

Mar 2022