Standard product recommendation engines can solve this problem to some extent. Based on user behavior analysis in real-time, they suggest products that drive engagement. However, businesses require a more advanced solution to provide customers with a holistic shopping experience and drive higher average order value (AOV).
AI stylists can help to create this experience. They go beyond suggesting standalone items and curate a “complete the look” bundle, which helps to maximize order values. Both product recommendation engines and AI-powered stylists are distinct and can benefit a business in different ways. Recognition of their distinctions can assist retailers in making an informed decision to use one or the other in preference to their business needs.
Understanding Product Recommendation Engines and AI Stylists
A standard product recommendation engine functions on real-time analysis of user browsing history, purchase preferences, and similarities between products. These data are used by algorithms such as collaborative filtering or content-based filtering, and patterns are found between them to give recommendations of products that would resonate with a customer's interest.
Recommendation engines focus on individual product relevance and are useful for enhancing product discovery and customer interaction.
On the other hand, AI-powered personal stylists bundle together a variety of products that enhance and support each other. Using ML-based algorithms, they take into account color matching, style matching, and the latest trends to create visually attractive ensembles.
Through a focus on well-coordinated outfits, AI stylists promote the purchase of multiple products, which leads to increased conversion rate and average order value.
The Key Differences
Businesses need to understand the difference between traditional product recommendation engines and AI-based stylists to offer appropriate personalized customer experiences. Although both lead the customer to the appropriate product, their approach, usefulness, and effect differ greatly.
1. Focus
Product recommendation engines aim to recommend specific products from various categories that the user may be interested in. Recommendation engines, using a user's previous buying history, interactions on the website, and browsing history, can suggest a product to the user. Recommendation engines do not consider product combinations or their underlying workings when making suggestions.
AI-powered stylists work on delivering multiple products that go together. Instead of suggesting a single shirt, it recommends an entire bundle containing pants, shoes, and accessories that complement the shirt. As ensembles work as a single unit, the chances of buying multiple products are higher compared to individual product recommendations.
2. Customization
Recommendation engines personalize the products being suggested based on user browsing behavior, preferences, and purchase history. Businesses can provide personalized product recommendations, such as frequently bought together and trending products related to customer preferences. However, they are limited to product similarity or popularity. These engines cannot deliver recommendations based on occasion or fulfill customers' visual requirements.
Personalization by AI-powered personal stylists follows a similar analysis by recommendation engines but at a deeper level. They take into account users' styles, body shapes, and occasions to recommend appropriate products. NLP and visual AI are used by AI stylists to recognize complementary colors, meet consumer needs, and adhere to fashion trends. As a result, customers can get context-aware recommendations.
3. Application
The applications of product recommendation engines are versatile and can be used on various pages based on customers’ current positions in the buying journey. Product recommendations can be placed on the homepage, on the product page, or during the checkout process. They help promote specific products and suggest alternatives for abandoned carts.
Bundles created using AI-powered stylists are effective when used on product pages and during the checkout process. It can capitalize on the potential customers who are ready to convert by suggesting complementary products and increasing their cart value. Curated bundles, for instance, can be deployed in marketing channels such as email, social media, and targeted display ads to continue to engage customers.
4. Visual Curation
Recommendation engines, at times, fail to deliver visually appealing products. They present product suggestions either as a list or in a carousel format, along with images and product names. This makes it harder for customers to visualize how the suggestions will look combined and hinders them from making proper decisions.
A significant advantage of AI-powered personal stylists is their ability to show how different product combinations look together. This complete look of product suggestions can captivate customers and increase their purchasing confidence. AI stylists further allow users to personalize their look through a mix and match of suggested items.
5. Benefits
Product Recommendation Engines
- Engaging Customers at Multiple Phases: Retailers can implement this at various points in the customer buying journey, beginning with the homepage until checkout, as well as the awareness stage through to post-purchase emails.
- Improving Product Discovery: As customers are provided with products of their interest in the first place, recommendation engines ensure that the customers can find relevant items quickly.
- Exposing Product Line: Recommendation engines can suggest products from different categories that may not complement each other but are relevant to the user. This allows customers to consider items beyond their intended product search.
- Targeted Promotions: Recommendation engines are designed to promote particular products based on the interests of the users.
AI Stylists
- Increases AOV: A complete outfit bundle can encourage customers to buy multiple products, leading to increased cart value.
- Boosts Cross-selling and Upselling: Promoting complementary products or higher-end products that further enhance buyers’ primary purchase can help maximize revenue per customer.
- Delivers 1:1 Personalization: AI-powered stylists customize looks to each customer, simulating a personal interaction. This helps to improve brand loyalty and customer base.
- Reduces Search Time: Customers are provided with ready-to-buy ensembles using AI-powered personal stylists, which reduces their search time. Moreover, the “complete the look” bundle can reduce decision fatigue and ensure that the customer completes multiple purchases in a single visit.
Bottom Line
Both product recommendation engines and AI-powered stylists with different applications provide different values to the customer and company. Businesses must always take into account different aspects when deciding between either of the two solutions or to combine them. A thorough analysis should be done based on their product line, business needs, and goals to be achieved to make an informed decision.
To increase product visibility or introduce customers to a wider product catalog, a recommendation engine is an ideal choice. AI-powered personal stylists can help if the goal is to increase AOV and provide hyper-personalized experiences. Businesses with clear objectives can leverage them both appropriately to encourage customers to explore, engage, and spend more on their websites.
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