Figleaves started life as one of the original internet fashion businesses. Back in 1998 when the company was founded, there was little choice of lingerie styles and sizes on the high street, and even less knowledge of bra fitting. Figleaves was determined to change that.
The company knew the range of women’s actual sizes was painfully underserved, and that poor fitting was impacting both posture and health. Figleaves revolutionised the range of choice by offering over 130 sizes.
To provide the breadth of choice in size, silhouette and price, Figleaves offered a carefully curated selection of the best brands; from its own brands to big name designers, as well as niche and specialist collections. Stocking such a diverse range in stores would be impossible, which is why the company remains exclusively online.
Adapting and Modernising for Today’s Retail Challenges
“Today, we collect a lot of marketing data from external sources, such as Google Ad Words, Salesforce and Facebook, which is held in one system, and we need to compare it with cost and margin data held in a separate system. Our challenge was that these disparate systems didn’t talk to each other,” said Graham Smith, Head of Technology at Figleaves. “For example, looking at gross profit against marketing spend was difficult because we weren’t able to easily pull product cost data into a report from a marketing perspective. We needed to adapt and modernise our systems for the pace and demands of today’s retail environment.”
With dynamic pricing models, such as Google Shopping, pricing can be very fluid for retailers. Figleaves needed a system that could easily aggregate reports on margins, limitations and traffic around its commodity products – as well as the ability to effectively and quickly respond to competitor price scraping as part of its advertising strategy.
“The days of one weekly rolled up metric don’t exist in eCommerce anymore,” said Angus Jenkins, Head of eCommerce at Figleaves. “Today, retailers must be able to read and cut data in different ways several times a day as needed, as well as drill down in a timely fashion, and know with certainty whether they need to change and react, and spend more or spend less with online promotions.”
The need for real time marketing optimisation lead Figleaves to the Avora platform. Avora’s augmented analytics platform enables Figleaves to easily and effectively marry together its merchandising and marketing metrics data sets giving the trading team better insight in real time.
“Our size and colour variants are hugely complex,” added Jenkins. Our average bra has 36 different sizes, five colour ways resulting in more than 200 SKUs per product. Avora enables us to pull together customer data and segmentation, as well as different customer behaviour across brands, categories and regions – giving us huge opportunities for richer, deeper insights. Figleaves was founded with a forward-thinking data driven approach, and we see Avora as an evolution of that original concept.”
Avora’s next generation of augmented analytics uses Machine Learning and patent-pending technology to make data and business analytics insight so fast, reliable and easy to use that every business user becomes their own empowered data analyst. Using anomaly detection and root cause analysis, Avora reveals why things are happening, not just what has happened. Avora’s unique and powerful approach rapidly ingests data in the cloud from any source and connects it on any device across the organisation into a single trusted view without limiting usage, scale or collaboration.
“A few months ago, we had a dip in trade, which after three weeks of investigation we found was the result of a very specific price change on Google Shopping by a competitor,” said Smith. “We had to put data sets together and have conversations internally to find this out. Moving forward, scenarios like this will be automatically flagged with Avora’s anomaly detection and root cause analysis to help us to get to that type of discovery quicker.”
The Right Analytics Fit For All
Figleaves’ trading team is self-organised and self-managing and is tasked with finding correlations in data and making good decisions on those insights. Avora enables Figleaves users to build their own self-service dashboards and report in a timely manner.
“Lots of other suppliers will do the ETL piece, but their reporting is very static, and you can’t easily drill down without a lot of upskilling,” said Smith. “Avora lets our trading team go on exploration journeys with the data. It gives us the flexibility to tie data together and easily democratise it across teams in the business.
“We looked at other cloud-based analytics and technologies and we realised that the complexity of developing and maintaining all the ETL connectors to all the various data sources is a significant overhead and development commitment – and not one we were looking to take on in-house. Avora gives us the economies of scale managing hundreds of connectors so we can pull it into our data warehouse – and we still retain and own the data. Knowing we have a scalable performant platform underpinning our systems means we have a distinct data asset moving forward.
“Previously, it was very difficult for us to put our data into the hands of super smart data scientists and ask them to get value from it. Now we can easily present it in an easy to interrogate format so data can be democratised, and data scientists can find new meanings and make great decisions, rather than assemble data sets.”
Thanks to Avora’s Machine Learning and algorithmic processing, Figleaves will also have access to Avora’s powerful AI functionality moving forward.
“Avora’s Machine Learning will enable us to perform deeper queries at a much faster and granular level than a human being can,” concluded Jenkins. “We can filter by different sizes, colours, regions, behaviours and demographics, and look at the data through lots of different lenses on all the different propensities to purchase, including external factors such as YouTube influencer posts in real time – things humans just can’t do at speed, looking at questions we may not have even considered and capitalising on them. While it’s still relatively early days for Machine Learning in retail, it will become part of the standard toolkit for ecommerce and retail so it’s important for our teams to understand how to master and maximise its value. As a business, it’s our intention to become fluent in harnessing its power and potential.”