Forrester Research notes the number of net new shoppers online is slowing to a less than 20% annual growth rate after reaching highs of as much as 50%. Competition for shoppers is intense. Curated product recommendations present a competitive edge to convert larger numbers shoppers and boost revenue per customer. Personalized product recommendations give shoppers the pleasure of browsing and discovering new products they may be interested in while saving them time and money.
THE ‘CHANGING ROOM’ – PERSONALIZED SHOPPING
Every line of code that drives FashioningChange was built by the team. The company was founded to answer one question… “I like XYZ brand what’s the sustainable and fairly made alternative that saves me money?” We built our Changing Room to associate every FashioningChange approved brand with mainstream brands they had a similar style to (i.e. H&M, Zara, Ralph Lauren, etc.). We later evolved the Changing Room to include the ability to shop by occasions (i.e. Date Night, School, Tea, Sport Event, etc.), personalities (i.e. Outgoing, Nautical, Runway Ready, etc.), and causes (i.e. Children, Human, Rights, Job Creation, etc.).
PRODUCT RECOMMENDATIONS USING FACEBOOK INTERESTS AND LIKES
When we realized we could integrate Facebook Interests and Likes and correlate them to products we sought out to show consumers that regardless of their personal interests and likes sustainable, fairly made, and accessibly priced items to enhance those interests exist. The way that we did this was by:
- Aggregating a huge list of Facebook Interests and Likes held by our users
- We then asked the question “What Interests and Likes represent at least 80% of our users?”
- We then used those Interests and Likes and weighted them against brands, products, personalities, occasions, and causes (a super fun experience that had the team laughing for days)
- Once Facebook Interests and Likes were weighted against established Changing Room attributes we were able to dynamically select appropriate products for Facebook Interests and Like and render them in the Changing Room
This process resulted in our ability to make recommendations to someone who liked ’Dennis Rodman’ on Facebook. If someone liked ‘Dennis Rodman’ they would receive products that were associated with a ‘Funky’ personality, ‘Funk’, and ‘Funk Music.’
When a customer receives a recommendation from the FashioningChange Changing Room they convert into a customer 288X more than they would if they did not receive a product recommendation. This is something the founding team is super proud of. It’s a huge lift on conversion when compared to the baseline for e-commerce customer activation. Mores so the curated product recommendation via Facbeook Interests and Likes add a fun layer to the shopping experience.
Personalized e-commerce and dynamic product curation is an interesting niche to follow. I see the leaders in the area to be Netflix and Hulu. I haven’t seen anything too amazing in apparel e-commerce. Have you?