Tight margins are normal for many online apparel merchants. Pressure to discount, rising returns, and all kinds of unforeseen costs can cut into profit and slow down growth. He also knew increasing AOV is all about making better connections with current customers and cross selling to them, a strategy used by some of the biggest players in e-commerce.
The key to this solution was right under his nose, in his customer data. But putting his data into an actionable business plan was challenging. Mr. Ahua concedes, “we had a lot of difficulty exporting our website data and analyzing it by ourselves.” It’s not an uncommon problem. Marketers all over the world have tons of data on hand, but find it difficult to manage.
The Rosetta AI personalized marketing solution collects customer/product data for Mr. Ahua and machine learning figures out which other products individual shoppers want to see.
With recommendation engines installed on the landing page, category page and product details page, shoppers visiting Ahuaruok may come for custom socks, but they may add something else to their shopping cart now, perhaps a dress or a jumpsuit that goes with the socks.
With the customer data insights from the Rosetta AI in hand, Mr. Ahua was soon able to see what was selling and what was not on the level of product attributes. To Mr. Ahua, customer insights are the most valuable thing, he even added “it’s not all about the money.” Now he views growing his business via cross selling much more favorably than through discounts alone.
With an understanding of the shared attributes of the things that get purchased most often Ahuaruok has a deeper understanding of its customers and products which lets him make more truly data-driven decisions.
While still in the 14-day trial period, Mr. Ahua noticed that average order value had jumped from 70 to 100 NT dollars per customer so he signed up for a full year subscription to the service.
Just 3 months later (by August of 2020) personalized marketing provided by Rosetta AI was driving 20% of total purchases and 20% of total revenue per month.
By the end the year, AOV was 19.6% higher than before having the recommendation engine. The extra revenue translated into a 5x return on investment!