Clear Returns is a software-as-a-service (SaaS) solution that provides online
retailers with real-time information on their products and customers. It
analyses data to pinpoint problems with the returns process to minimise spend
and improve customer service.
CEO of the startup, Vicky Brock, discovered that it was normal for fashion retailers to have return rates as high as 40%. “It’s clearly a massive problem to business growth, and it is not sustainable long term,” she said.
Clear Returns' technology is split into two solutions: Eco, which looks at
products; and Seer, which analyses customers.
Eco provides a view of the business profitability. Brock said that 50% of returns are driven by certain key items, so Eco shows the retailer patterns in the items and returns. The retailer can set alerts to know if a certain item is being returned more than another, which can help it spot problems and reduce costs.
If a customer adds a problem item to their online basket, the retailer can trigger helpful services, such as the virtual dressing room, allowing customers to make sure the product is right for them.
This article is part of a series where Computer Weekly aims to connect CIOs with technology startups.
If you are thinking of looking for technology solutions from small innovative companies, but are not sure where to look or how to approach them, you may be interested in our UK startups articles.
This guide provides you with everything you need to know about startups in the UK, with news, business profiles and advice on starting relationships with UK startups.
“It helps buying decisions, quality control and stock ordering,” she said. “Our data is coming in near real time. Without [Clear Returns], retailers usually get that information eight to 10 weeks after. It would come from e-commerce and the warehouse, then to an excel spreadsheet, and then be worked through at the end of the month and reported into the organisation.”
Seer, on the other hand, analyses customer returns behaviour.
“Most customers are good customers who buy because they want the product. If they have a bad returns experience, you kill off that relationship,” said Brock
But Seer can also analyse the “bad customers” who return at a higher than normal rate. This includes customers who wear an item once before returning it to gain a refund. The analytics can pinpoint customers who regularly buy the same type of product but always return, and then it is up to the retailer to decide on a course of action, such as stopping the suggestion pop-up adverts going to that particular customer to reduce the chance of them buying something they’ve bought before.
Brock explained that in a low-stock environment, where there might be only a couple of pieces left, people who have no intention of keeping it can be identified by the retailer, which can then make sure that a loyal customer gets the first pick.
“That’s a negative scenario – the majority of scenarios are positive, because the majority of customers are good positive loyal customers, and returns is a negative customer experience,” said Brock. “In a low-stock environment, retailers have to buy 20% more stock to keep an item in stock because of the high number of returns.”
Clear Returns uses data mining and predictive technology to analyse stock and customers.
Brock chose IBM’s predictive analytics software SPSS, which runs on the Amazon cloud. It takes information from the e-commerce platform, warehouse stock numbers and operation systems, and then predicts which product will be returned based on sales and return patterns.
Brock said that if its SaaS product helps to reduce returns by two percentage points, that will equate to a 1% increase in profit.
“We’ve just done a trial with a relatively small but international fashion brand, and we found that £1m in transactions were coming from returners buying the same items every couple of weeks – wear and returners – and another £400,000 of returns was simply down to mismatch of description,” she said.
As well as cost savings, the platform can provide an insight into the returns process for customers and help retailers improve customer satisfaction.
“If you delight a few more customers and turn them into loyal shoppers by making the process right and giving a really high-value service, you’re actually going to have an impact on revenue as well as savings."
A retailer can choose either Eco, Seer or both solutions as a SaaS model.
Clear Returns initially takes a few months of extractive data from the retailer to gain an understanding of its taxonomies and various classifications. It then takes feeds from all the different systems to analyse and predict product returns.
The retailer can then access the analytics via a browser login.
While Brock has designed the platform with fashion retailers in mind, the algorithms also work for other retailers, such as those selling sporting goods, jewellery and DIY.
“I started with fashion first because the return rates are truly eye-watering,” she said.
Clear Returns has just secured the clothing and household store M&Co as a customer. M&Co has more than 300 stores in the UK and a growing e-commerce platform which also sells internationally.
Brock set up Clear Returns in May 2012, and the company was enrolled in Entrepreneurial Spark, a Glasgow-based incubator. Entirely self-funded, it was also supported by the Scottish government, which helped Brock attend the MIT Global Entrepreneur Development Program – a bootcamp for high-growth tech entrepreneurs.
She then took part in IBM’s SmartCamp in Dublin, which supports startups with ongoing strategic development and mentoring.
The next step for Clear Returns is to scale its business into other geographies, and it is currently raising £1m in investment.
CEO of the startup, Vicky Brock, discovered that it was normal for fashion retailers to have return rates as high as 40%. “It’s clearly a massive problem to business growth, and it is not sustainable long term,” she said.
Eco provides a view of the business profitability. Brock said that 50% of returns are driven by certain key items, so Eco shows the retailer patterns in the items and returns. The retailer can set alerts to know if a certain item is being returned more than another, which can help it spot problems and reduce costs.
If a customer adds a problem item to their online basket, the retailer can trigger helpful services, such as the virtual dressing room, allowing customers to make sure the product is right for them.
This article is part of a series where Computer Weekly aims to connect CIOs with technology startups.
If you are thinking of looking for technology solutions from small innovative companies, but are not sure where to look or how to approach them, you may be interested in our UK startups articles.
This guide provides you with everything you need to know about startups in the UK, with news, business profiles and advice on starting relationships with UK startups.
“It helps buying decisions, quality control and stock ordering,” she said. “Our data is coming in near real time. Without [Clear Returns], retailers usually get that information eight to 10 weeks after. It would come from e-commerce and the warehouse, then to an excel spreadsheet, and then be worked through at the end of the month and reported into the organisation.”
Seer, on the other hand, analyses customer returns behaviour.
“Most customers are good customers who buy because they want the product. If they have a bad returns experience, you kill off that relationship,” said Brock
But Seer can also analyse the “bad customers” who return at a higher than normal rate. This includes customers who wear an item once before returning it to gain a refund. The analytics can pinpoint customers who regularly buy the same type of product but always return, and then it is up to the retailer to decide on a course of action, such as stopping the suggestion pop-up adverts going to that particular customer to reduce the chance of them buying something they’ve bought before.
Brock explained that in a low-stock environment, where there might be only a couple of pieces left, people who have no intention of keeping it can be identified by the retailer, which can then make sure that a loyal customer gets the first pick.
“That’s a negative scenario – the majority of scenarios are positive, because the majority of customers are good positive loyal customers, and returns is a negative customer experience,” said Brock. “In a low-stock environment, retailers have to buy 20% more stock to keep an item in stock because of the high number of returns.”
Clear Returns uses data mining and predictive technology to analyse stock and customers.
Brock chose IBM’s predictive analytics software SPSS, which runs on the Amazon cloud. It takes information from the e-commerce platform, warehouse stock numbers and operation systems, and then predicts which product will be returned based on sales and return patterns.
Brock said that if its SaaS product helps to reduce returns by two percentage points, that will equate to a 1% increase in profit.
“We’ve just done a trial with a relatively small but international fashion brand, and we found that £1m in transactions were coming from returners buying the same items every couple of weeks – wear and returners – and another £400,000 of returns was simply down to mismatch of description,” she said.
As well as cost savings, the platform can provide an insight into the returns process for customers and help retailers improve customer satisfaction.
“If you delight a few more customers and turn them into loyal shoppers by making the process right and giving a really high-value service, you’re actually going to have an impact on revenue as well as savings."
A retailer can choose either Eco, Seer or both solutions as a SaaS model.
Clear Returns initially takes a few months of extractive data from the retailer to gain an understanding of its taxonomies and various classifications. It then takes feeds from all the different systems to analyse and predict product returns.
The retailer can then access the analytics via a browser login.
While Brock has designed the platform with fashion retailers in mind, the algorithms also work for other retailers, such as those selling sporting goods, jewellery and DIY.
“I started with fashion first because the return rates are truly eye-watering,” she said.
Clear Returns has just secured the clothing and household store M&Co as a customer. M&Co has more than 300 stores in the UK and a growing e-commerce platform which also sells internationally.
Brock set up Clear Returns in May 2012, and the company was enrolled in Entrepreneurial Spark, a Glasgow-based incubator. Entirely self-funded, it was also supported by the Scottish government, which helped Brock attend the MIT Global Entrepreneur Development Program – a bootcamp for high-growth tech entrepreneurs.
She then took part in IBM’s SmartCamp in Dublin, which supports startups with ongoing strategic development and mentoring.
The next step for Clear Returns is to scale its business into other geographies, and it is currently raising £1m in investment.
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