How Forever New boosted online sales

Forever New tested the growth through personalisation theory in online retail with a new visual recommendations tool and enjoyed an uptick in sales

Grabbing a precious few minutes for a spot of online retail therapy has become commonplace and few would give up this convenience and pleasure. Yet sometimes the retail experience can seem a little one-sided, clicking through clothing categories and navigating menus to find desired items.

Online, there’s no human to pick up a top similar to one a customer has and suggest ‘maybe you’d like this too?’ Enter technology, which has a habit of creating unforeseen problems, and then promptly designing solutions to turn them into opportunities.

Helping customers with like-for-like recommendations while shopping online was the very thing Australian fashion retailer Forever New wanted to develop and, in doing so, enjoy the benefits of growth through personalisation. 

Helping customers find similar clothing

Forever New launched in 2006 in Melbourne and was created out of the need for affordable, elegant, feminine designs for women and girls. The label has gone from strength to strength, expanding into new global markets through its own channels, as well as through wholesale, marketplaces and concession stores, and it now trades in over 300 retail and concession stores globally.

“We have a truly unique product offering, we’re known for our effortlessly wearable and timeless collections that celebrate modern femininity,” says Carolyn Mackenzie, managing director of Forever New, who has been with the company for four years after an already long career in FMCG.

Online shopping is a visual medium, so it makes sense to present clothing, shoes, bags and accessories by their attributes, be it colour, style, shape and so on. However, while these systems typically follow certain merchandising rules, it isn’t always done in the most intuitive way to offer like-for-like suggestions when customers are shopping online. Forever New knew customers were often buying similar styles from the same category and wanted to make it easier for them to discover more of the styles and silhouettes that would appeal to them. 

“Through business intelligence analysis, we saw patterns in customer behaviour indicating customers who had multi-item transactions tend to buy items from the same category, in similar styles,” Mackenzie said.

The business if focused on its online platforms and, in particular, honing its attention on its customers and having something to meet their tastes and styles while shopping on its site.

“Driving growth on our digital channels is a priority for the business. Our digital and ecommerce teams are constantly working on developing new personalisation technologies to make our online customer experience best-in-class.”

The solution: Image recognition technology like a virtual sales assistant

In traditional bricks and mortar retail, in-store shop assistants can help do the thinking for customers, suggesting outfits they might like based on their preferred styles; but in the rush to online shopping it’s somewhat fallen by the wayside.

And yet here’s where technology can help: making the all-important link between image recognition technology that can match similar-looking things, such as items of clothing, with a recommendations engine harnessing the power of search and merchandising strategies. Attraqt, which specialises in merchandising, online retail and e-commerce solutions, has a visual recommendations tool to let shoppers easily find similar items.

Forever New knew customers often chose from the same category and adopted Attraqt, which it was already using for other parts of its online platform, because it’s the first provider to combine these two powerful tools.

“The Attraqt visually similar technology was the solution for reinforcing this behaviour. From an integration perspective, it made sense to leverage off our existing Attraqt network and add a layer of artificial intelligence (AI) image tagging over our already powerful merchandising rules for exponential sales,” Mackenzie told CMO.

“When the customer is on a product page they can scroll down and view the ‘You May Also Like’ section. This section shows styles similar in colour, print, style, or shape to the initial style that attracted the customer in the first place.”  

Attraqt uses SaaS technology to process image data and automatically show the customer visually similar styles based on the customer's product preferences.

“It takes the unique characteristics of each item in your catalogue and recommends products that have similar patterns, style, colour, or silhouette.”

Attraqt is soon to introduce a visual search function, similar to Google image search, allowing shoppers to upload an image and search for similar items; and visual tagging allowing shoppers to look for items based on particular attributes, such as design feature, style, or occasion.

The result: Shoppers like what they see, when they see what they like

Forever New has found personalisation is powerful and has enjoyed an uptick in sales because it makes online retail more intuitive. Customers get a personalised touch, which is more traditionally a part of the store experience, with the ease and speed of online shopping, and it’s had an impact on growth.

“When shoppers are shown visually similar items, we see an increase in conversion for those products, as well as a higher AOV,” Mackenzie said.

Forever New plans to continue to use the power of the entire Attraqt platform, and it's already optimised merchandising logic to enhance what it shows to the customer based on previous buying habits. It also plans to hone its understanding of exactly what attributes in the recommended items appeal to its customers to further drive add-on sales.

“We also are looking to A/B test how we weight recommendations; testing which attribute is the most powerful conversion tool. For example, do customers want something visually similar from a colour perspective first, or is the conversion driver the style of the product and the colour is secondary?

“Testing will allow us to harness these insights regularly in order to continually improve the customer experience. We also hope to leverage this technology on our category pages, tailoring the browsing experience with visually similar recommendations included in the journey.”

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