MakerSights, pioneer of the product decision engine for retail, today announced its $8.5 million Series A round led by new investors Forerunner Ventures and joined by Brett Hurt and Brant Barton, the founders of BazaarVoice. Brian O'Malley of Forerunner Ventures will also join the MakerSights Board of Directors. Following a $3.1 million seed funding round, the additional capital infusion will enable key leadership hires, accelerate the growth of in-house engineering and data teams, fuel international expansion and scale its go-to-market operations. MakerSights has grown its customer base by 5x and significantly expanded its footprint of enterprise brands—including Calvin Klein, Levi's, Tommy Hilfiger, Shinola, Taylor Stitch, HOKA ONE ONE, Teva and Allbirds.
MakerSights Raises $3.1M in Funding
MakerSights, a San Francisco, CA-based product decision platform for retail, raised $3.1m in funding. Backers included: – Steve Anderson, founder of Baseline Ventures; – Hayley Barna, partner at First Round Capital; – Bill McComb, former CEO of Kate Spade and Lucky Brand; – Elizabeth Spaulding, senior partner at Bain & Company; and – Jeff Epstein, operating partner at Bessemer Venture Partners. Led by Dan Leahy, co-founder and CEO, MakerSights provides a product decision platform for retail. Its AI-driven technology partners with product teams to support informed decision-making at every stage of the creation and go-to-market process, from ideation to line planning and sell-in.
This Company Is Helping Fashion Brands Make Smarter Product Decisions Via Predictive Analytics
Brands including Ralph Lauren, Sperry, Lucky Brand, MM LaFleur and True Religion are turning to a predictive analytics platform called Makersights to help inform their product design and development. Think of a traditional focus group where learnings are high, but costly and slow, and then tip it into the digital, mobile or indeed machine learning age and you’re on the right page. This is a business that pulls information at scale from customer insights, then applies actual sales data and machine learning to that feedback. They call it "actionable product intelligence". According to the team, the aim is to help brand partners develop more accurate sales plans, de-risk new product introductions and measure how customers respond to product attributes like fabrics, colors and price. It’s already seeing a 2-4% gross margin lift for its partners based on minimizing markdowns and doubling down on big winners.