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Category page merchandising
Tailor user experience to improve business metrics by optimizing Category page merchandising strategy
Why should you do it?
Your category pages should reflect your merchandising strategy and offer you full control over the experience you want to create for your customers. This goal can be achieved by a combination of manual and automated merchandising. You will not only increase the business metrics, such as conversions, average order volume and more, but also be able to increase brand loyalty and manage the brand perception among your customer base.
Category Merchandising with Rules
Relevance Rules are used to make precise and (if desired) temporary modifications to your category page display. For example:
- Boost best selling categories
- Boost high inventory collections
- Boost new collections, subcategory
- Add a banner on category/collection page
- Redirect to a dedicated landing page
- Hide or bury out of stock products / shipment delay
- Facet merchandising: order facets, order facet values, sort facets values
For instance, when you have a collaboration with a specific brand, you might boost their products on the relevant category page or pin specific items from this brands’ collection on all the relevant category pages. Assume you need to promote a sports collection designed by an influencer, you can boost this collection on your “Sports” category page, pin shoes from this collection on “Shoes” category page, boost this brand on the “New collections” page and add a banner each time a user lands on one of the relevant category pages to draw their attention to the new campaign.
Category Merchandising with AI Re-Ranking
AI re-ranking algorithm leverages the “wisdom of the crowd”. It is a great way to ensure the most relevant results always appear at the top of each category page, while the least relevant results are pushed to the bottom. Automating the reordering of the product lists helps you manage your category pages at scale and leverage the AI capabilities to ensure efficiency of your merchandising efforts.
For example, an electronics retailer may have a category page dedicated to mobile phones. If newly released “Apple” and “Samsung” phones are more popular than other brands, and generate higher clicks and conversions, those products will be pushed to the top. Alternatively, less popular products will be pushed to the bottom of the search results. Automating reordering of the product lists helps you manage your category pages at scale and leverage the AI capabilities to ensure efficiency of your merchandising efforts.
Category Merchandising with Personalization
Applying Personalization to category pages is used to ensure that each user will always see the most relevant results for their unique preferences. Personalization takes into account user behavior on the platform (click and events) and preferred facets (prefered brands, sizes, colors, product types and more).
For example, a user “A” likes men's t-shirts in size medium and running shoes in size 9. A user “B” prefers women’s t-shirts in size small and soccer shoes in size 7. When user “A” lands on a category page for a Nike brand on sportswear ecommerce store, they will see at the top of the men’s t-shirts in size 9 in stock and running shoes in size 9 in stock at the top. Alternatively, user “B” will have a completely different experience on the same category page, with products reordered based on their personal preferences: women’s t-shirts and soccer shoes in stock in their desired sizes.
Category Merchandising with Recommendations
Recommendations are the smart way to leverage AI capabilities to increase discoverability, upsell related or frequently bought together products and increase average order volume and boost revenues.
There are multiple ways to use recommendations on category pages and beyond:
- Home page - when users land on the home page, they are presented with a dynamic product carousel featuring recommended products, that will adapt the recommendations to the user.
- Category listing page or product listing page - recommendations can be featured on the category pages to increase average order value and items amount per order.
For example, on a category page featuring running shoes, users can see recommendations for products that are frequently bought together with running shoes, such as socks.
- Product description page - once the user landed on a product description page, they might be interested in being offered similar products or frequently bought together products.
For example, a user clicked on a t-shirt in light blue color. They are not sure this product completely matches their style. They notice a similar blur t-shirt under the “similar product” gallery that is exactly what they are looking for and add it to their cart. Additionally, they see a short under “frequently bought together” gallery that is part of a matching set with their t-shirt and decide to add this product to the cart as well.
- Add to cart page - once the user adds a product to cart, an opportunity for upsell opens up. AI generated recommendations can help you offer the right products to match the items your user has already shown intent to purchase.
- Check out page - checkout page is an additional opportunity to upsell similar or related products using AI recommendation engine
For example, a user has added a jean and a blouse to the cart and are now ready to check out. Your store offers free shipping for orders over $99, but this customer’s order falls short of the free shipping minimum. This is an opportunity to suggest additional products, such as matching belts, socks or hats. This is a win-win situation for the company, gaining more revenue and order volume and for the customer, receiving a discount in the form of free shipping.
Email recommendations - Sometimes users abandon virtual carts, show interest in products that are not yet available or temporarily out of stock or just click on items without proceeding to make a purchase. You are able to re-introduce or remind them about the items that caught their attention by sending them an email recommendation for relevant products using the AI recommendations engine.