Laptop and product recommendation engine start-up wins crowd-sourced funding, European accelerator support and retail interest
A fledgling enterprise aimed at taking the pain out of complex shopping decisions is rapidly attracting support and attention from consumers and retailers alike.
First launched in early December 2012, Swogo
was created in response to the fact that it can take an average shopper up to three weeks to research, compare and then buy a laptop.
New breed of decision engine
In response, Swogo aims to make anyone taking on this buying decision an expert. The brainchild of its founder, Anthony Ng Monica, the website aims to condense the decision-making process down to seconds and convert a committed shopper to a retailer’s site based on six simple questions.
“It is essentially trying to do what calculators do for maths, but with purchases, prioritising the recommendations using machine learning techniques,” said Ng Monica, speaking exclusively to Retail Technology. “We’ve started with laptops in the UK and then branched out to the US. And we aim to offer the service for tablets, cameras, TVs and smartphones as well.”
The site has already generated 15,000 successful conversions, which Ng Monica confirms as users who clicked through to a retailer’s site. “One in four click off to a retailer,” he said. And, in turn, the service gathers data on consumer purchases, from the products they are looking at to what different demographics are searching for.
Swogo recently raised £60,000 on the online crowd-sourced funding network Seedrs
and has also made it into Europe's leading seed accelerator programme, Startupbootcamp
. And the company currently runs on cloud infrastructure services from Rackspace
Taking aim at retail market
Flushed with initial success, Ng Monica said the company is looking scale across markets quickly. It plans to ‘white label’ the technology for retailers. “Swogo can help increase conversion rates, customer satisfaction, and reduce return rates,” he explained.
He added that the company was just in the process of adding affiliate marketing capabilities. “At the moment we’re using solely Amazon and approaching retailers direct,” Ng Monica said. “We find that we tend to get much better data that way. And we are 100% impartial, we have to stand by the user.”
The direct route into the retail market will eliminate added affiliate costs from the service and, after a set-up fee for implementation, retailers will pay a monthly subscription to use the technology and maintain their product database. “Ultimately, it’s not a set fee, as our prices differ to cope with varying volumes of customers,” he added. “We could hook into their APIs [application programming interfaces], or we could even code snip into their site.”
While, the company is also exploring the idea of developing the service for customer-facing tablets or kiosks for use in a store, Ng Monica is hoping the strong start made by the recommendation engine will win over consumers and retailers alike in the coming months.