Discovering the Fusion between Subscription Services and AI Discovery

We’ve come a long way from traditional shopping. The increase in online orders and reliance on technology have drastically changed how we approach buying products. Due to consumers’ hectic schedules, convenience and speed are now significant differentiating factors in the retail landscape. To accommodate these evolving needs, retailers are pivoting. For example, Amazon’s ‘Dash Buttons’ for Prime Members allows consumers to reorder their favorite products with the click of a (now digital) button. The idea is similar to subscription models which take quick replenishment a step further. Subscription services often prove nontraditional because subscribers aren’t always in control of which items they receive. This is countered by the added convenience of never having to spend time picking out new products.

Traditional shopping ignores convenience, but subscription services tend to push the extreme too far in the other direction by removing control. Achieving a balance between shopper autonomy and receiving adequate support to make confident decisions is the sweet spot retailers are after. At Shoptelligence, we use AI-based style discovery to attain this balance. With a customized view of products, you can pick what you want out of relevant choices without being overwhelmed by a seemingly endless catalog.

Rise of Subscription Services

One-time sign-ups that guarantee a routine delivery of desired products are attractive to many customers, often regardless of the product category. In 2018, 15% of online shoppers were reported to have signed up for one or more subscriptions to receive products on a recurring basis. Rather than frequent trips to the grocery store, meal kits provide the ingredients and recipes needed to make dinner every night. Other direct to consumer companies like Birchbox and Dollar Shave Club allow for dependable monthly deliveries and product discovery.

Similarly, the apparel industry is starting to experience the entrance of subscription service models. Companies like Stich Fix, Le Tote, and Dia & Co. provide the benefit of new clothing based on your personal style. Unwanted items can then be returned, all without the pressure of selecting the items yourself. Though this model provides freedom from navigating the abundance of options in physical stores or online retailers, shoppers are at risk of dissatisfaction with the products picked for them, resulting in high return rates.

Understanding Customer Intent

Traditionally, shoppers approached a store already knowing what they intended to purchase. Today, consumer data and evolving shopper expectations now suggest retailers should be helping shoppers discover new products regardless of original purchase intent. Acting on recorded customer data allows a retailer to better serve their shoppers by providing relevant product recommendations and timely offers. Even more so, understanding customer intent through machine learning assisted data analysis allows retailers to provide unmatched personalized service. Data analysis can answer the questions: “What is the shopper looking for? Why are they shopping today? Why are they shopping at my company?”

How Merchant Assist Can Help

For brands striving to deliver on personalization but don’t see subscription services or their accompanying personalization strategies fitting into their business model – Shoptelligence’s Merchant Assist platform is an alternative solution. Merchant Assist eases the discovery process while providing autonomy to shoppers and retailers. On the consumer side, shoppers interact with our recommendations to create the perfect room. On the retailer side, retailers can customize the ensembles presented to shoppers. For example, if a retailer has a sofa in the same collection as a specific ottoman and side table, the retailer can manually pair those items together to present in an ensemble to the shopper. The non-collection items in that ensemble are then generated based on the style attributes of the sofa. The ensemble creating algorithm automatically accounts for inventory changes and learns from inputs leading to better and better ensemble recommendations with each use.

A deeper understanding of customer intent, fueled by a data-driven platform, allows retailers to guide shoppers through the shopping journey. Ultimately, reducing the shopper’s discovery burden without sacrificing their control or autonomy.


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