Understanding eCommerce product recommendation engines in fashion

The time to ramp up product recommendations is now in fashion, and why AI is at the core of personalised shopping.

Creating a personalised experience for shoppers is a sure way of generating repeat business, building brand loyalty, making better business decisions through product analysis and as a whole, increase business longevity, producing a healthier bottom line.

Melissa loves social media.  Her preferred platforms are Instagram and Pinterest.  She has dozens of Pinterest boards and hops in and out of the app daily, looking for inspiration pinning multiple styles that reflect her personality.  Instagram, for Mel is all about tracking the most current hashtags and following her favourite influencers and celebrities.

90% of Pinterest users say using the platform helps them make a decision to purchase something.

What Pinterest does really well is provide highly relevant content based on user data and search queries.  Continuously learning about a user’s profile and preferences, serving up only the most inspirational content.

Going down the ’More like this’ path in Pinterest is like watching the makeover scene in Pretty Woman.

Reading any current ‘Future of Retail’ report and there are typically three key concepts driving change across the industry – convenience, automation and experience.  These studies are supported by large consumer groups surveyed about their shopping habits. One very common response is that shoppers are absolutely more likely to shop with brands that provide them a genuinely personalised experience, 71% actually, in the recent PSFK Future of Retail report and Shopper Attitudes 2019 Survey.

These statistics are not surprising. And, as everyday people, we can also appreciate what it feels like to receive a personalised shopping experience.  However, the pressure is on those working in the industry to create personalised experiences in their own business. This is where an understanding of empathy, nurture and delight are critical and should be prioritised just the same as resources and budget allocated to acquiring customers.  Why? Because the result of being empathetic, nurturing and delighting customers will organically convert into brand loyalty and repeat shoppers.

Back to Mel for a moment.  She’s aware of the latest trends in fashion, doesn’t purchase unnecessarily and will take some time researching and making a decision to buy something new based on what she currently owns, loves to wear and matches her style.   Mel has interacted with various fashion sites recommending products while she’s browsing the site, however she doesn’t want to see what others bought with the dress she’s looking at. She’s different. Melissa expects, at a very basic level that her individual fashion preferences are taken into consideration when being recommended products to purchase.

Technology has become significantly smarter in the past 10 years with artificial intelligence now the bedrock of many programs.  A number of platforms having already integrated AI to their software without users realising, namely, Pinterest and Instagram.  This is largely due to an exorbitant amount of data generated online.  From clicks, bounces, double-taps, actions and purchases to capturing browser history, downloads, uploads, streaming and geo-tracking.  Big data, our digital footprint, the way we behave online is feeding AI systems all over the world. It’s then translated into insights from patterns and analytics, which can then be used to complement business intelligence and improve the user experience.

When it comes to shopping online, most eCommerce stores will have a product recommendation engine installed.  This software carries out one main task, to recommend additional product to a shopper as they’re browsing a website and product page.  There are three types of product reco engines:

  1. Collaborative filtering – tracks and groups other users actions to predict what another shopper will like.  
    1. Eg: Others also purchased these products with this item.
  2. Content specific – a profile is created by tracking website pages and clicks of that user and the profile is compared to a product catalogue to identify which products to recommend.
    1. Eg: Products similar to this item.
  3. Hybrid – a mix of both collaborative filtering and content specific.

AI driven product reco engines are more sophisticated in that algorithms continuously learn over time users personal preferences and show products based only on similar tag and attribute characteristics.  This form of recommendation outperforms generic engines because it is highly personalised and programmed to adapt to preferences. This is significantly more useful than the logic of comparing a shopper to a group or catalogue in that one online shopping instance.  

One afternoon, Mel is enjoying a coffee and taking a few moments to scroll through Pinterest and casts her eye across an advertisement.  The ad image is a dress similar to a few images she’s pinned to a recently created Pinterest board labeled ‘Special night out’. Mel clicks the ad and is taken directly to the dress listed on a fashion eCommerce site she didn’t know about.  She reads the dress description, and will keep that top of mind while she continues perusing the site. Mel won’t make a purchase just yet, but she really loves this site and starts spending more time on it looking at shoes, accessories and back to dresses.  Over the duration of a few weeks, Mel is ready to buy the dress and clicks back to the site. She notices the recommended products are actually items she likes, and hadn’t noticed the earrings suggested previously. It was without hesitation that Mel could envisage the whole outfit together.  The dress with those gold statement earrings and she would wear her gold strappy heels that she’s had for several years and have never failed her.

The power of a personalised product recommendation through AI in Mel’s experience isn’t the technology, it’s her imagination bringing the outfit to life.  Mel is different, and so is everyone else looking to be inspired to shop and regular product recommendation engines just won’t cut it.

Fashion retailers of all sizes can now access these engines economically and as a standalone software product that isn’t part of a convoluted all-in-one package.  The team at Okkular can look after the implementation, getting you setup with their AI product recommendation engine and get your business on the path to personalisation.