One startup, one story: what if new customer shopping habits could be elicided?

How a product recommendation engine draws its analysis from social media. Let’s meet Guesswork.

In the digital era, product recommendation engines predict the shopping intent of users by sifting through a variety of consumer data, from browsing and digital purchase history to online shopping carts.

This works well for sites such as Amazon, where many shoppers are repeat customers with a data trail. But what happens if you’re launching in an emerging market, where users are buying online for the first time, or launching a new product line of little relevance to existing customers?

CEO and company co-founder Mani Doraisamy launched Guesswork to address the startup e-commerce sites that weren’t being served by existing product recommendation engines.

New e-commerce sites spend millions of dollars in marketing to attract users to their site, but Mani felt their money could be used more effectively. He notes that only around 20% of users typically wooed to a new site will sign in, and of these, only 3% will buy products. Most recommendation engines focus on this small selection of users because they’re the only ones they have data on. Guesswork decided to focus on the other 97%.

The company analyzes users’ social media to make product recommendations, with impressive increases in conversion rates, the key measure of e-commerce success. Within four weeks, Mani predicts a 300% rise in the number of customers completing a transaction as a proportion of the total number of website visitors.

This is based on the experience of customers including Zalora, Asia’s largest fashion e-commerce site; Linio, South America’s top e-commerce site; Babyoye, India’s largest baby products site; Zivame, India’s largest lingerie site; Deerberg, a German online fashion retailer; and Stalkbuylove, India’s top women’s fashion apparel company. Guesswork have designed a system that is easy to set up and requires zero maintenance. Customers just insert a line of code into their website, which shouldn’t take longer than five minutes. That’s when the Guesswork algorithm comes into play, recommending products to users on mobile websites and via email, without requiring any further input from the seller.

It even works without an internet connection. When users go offline, the Guesswork algorithm keeps compiling recommendations that will show when they come back online, thereby adding more sales that might otherwise have been lost.

Guesswork was founded by Indian nationals Mani Doraisamy and college friend Boobesh Ramalingam, both of whom are experienced in building web platforms.

The company is currently focusing on expanding beyond its initial customer base in India and South East Asia. It decided to set up in Europe after a happy customer – Zalora – referred them to other companies also funded by Rocket Internet, a German VC firm. They have now set up in Paris after winning the French Tech Ticket Global Startup competition.

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