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A highly innovative size recommendation engine, disrupting the existing online apparel market by using big data and machine-learning algorithms.

Periodic Reporting for period 2 - FITA2.0 (A highly innovative size recommendation engine, disrupting the existing online apparel market by using big data and machine-learning algorithms.)

Reporting period: 2017-02-01 to 2017-10-31

Garment sizes are not the same across different brands, which means you can be size S in one brand and size L in another. When shopping online, you can’t try on different sizes to find out which one fits best, so you have to guess a size and hope it will fit. This approach results in a relatively low proportion of garments sold online, and high return rates of up to 70% in fashion e-commerce. Our company, Fit Analytics GmbH, an ICT-SME based in Berlin, is the proud developer of the world’s most widely-used online apparel size advisor. By integrating our software-as-a-service product into their online shops, major online fashion retailers around the world have increased their sales, and reduced their costs, by helping their online customers purchase their right clothing and footwear size. Through the SME Instrument Phase 2 project, and supplemented by private funding, we are developing our next generation size and product recommendation technology, which will disrupt the fashion industry by using big data to generate even more accurate and size recommendations and to deliver big-data-driven personalised product recommendations for the first time.

Studies show that each returned item accounts for more than €20 of operational costs (excluding the refund) due to increased costs for inventory, warehousing, and logistics. These aspects can cause a 10% loss of revenue for the vendor. Due to these problems, online fashion retailers are seeking effec-tive services to help customers find the right size, allowing them to order with certainty. Furthermore, a decrease in e-commerce returns would also lead to less emissions of CO2– several academic studies have calculated the emissions caused by e-commerce. They have shown that each (returned) package produces 500 gram CO2 on average.

Our company, Fit Analytics, helps etailers to rapidly overcome the problems described above by integrat-ing our software-as-a-service product into their online shops. Our Size Advisor helps shoppers find the right size when shopping for clothes and shoes online. This enables our clients to deliver a more enjoyable, personalised shopping experience to their customers, leading to greater customer loyalty and lifetime value, and provide even greater certainty about size and fit. Our objectives for the project are as follows:

Commercial Objectives
• To perform a market demonstration of FITA2.0 with clients, to assess performance, client and end-user satisfaction and to further validate our pricing model.
• To implement a knowledge-management and IPR protection strategy, for the expansion in Europe and the USA.
• To develop a business innovation plan, containing a detailed commercialisation strategy, market uptake and replication, and a financing plan.
• To develop and implement a communication plan to promote the project and its findings during the period of the grant.

Technological Objectives
• To specify the system specifications and implement an API for the collection of data; to develop new size recommendation algorithms for analysing the data and define the improvements that need be added to refine its operability and increase its performance to work with large sets of data.
• To carry out the platform implementations specified before, to develop the FITA2.0 Big Data Recommendation Engine with hands-free recommendation and target group analysis features.
• To demonstrate the solution to clients, and then perform pilots by integrating our product into their websites, for use by end-users and to assess client business and end-user satisfaction with FITA2.0 measuring its technical and economic performance.
The achievement of both our commercial and technology objectives for the second reporting period (February 2017 - October 2017) are all either within or even ahead of the initially proposed schedule, and we were able to further develop our product and introduce it to both new and existing clients. We were also able to develop and provide additional features and services such as sophisticated BI and demographic reports. We were able to succeed in several A/B tests that proved the added value of our FITA2.0 solution due to the more robust infrastructure, optimized implementation processes and enhanced algorithms for size recommendations.

We were able to prove the added value of our solution by conducting several A/B tests that showed a positive impact on return rate and conversion rate.
We are able to carry out most the integration work with new clients on our own to save resources for our clients.
We can provide target group analysis and demographic reports helping our clients to better understand their customers.
We are measuring high interaction rates from end-users with our solution and low bounce rates, and the end-users are comfortable using our solution
We were able to offer new features such as hands-free recommendations