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In the Frosmo Platform, a recommendation is a piece of dynamically generated content predicted to appeal to visitors and delivered through a modification.

Recommendation slider for the most popular products on a site

Figure: Recommendation slider for the most popular products on a site

To learn more about recommendations, see:

Recommendation use cases

Here are a few example use cases for recommendations:

For practical examples of working with recommendations, see Recommendation examples.

Driving discovery and selection in the retail visitor journey

Recommending the right products at the right time is key to effectively engaging and converting visitors. A retail visitor's journey from discovery to checkout is rarely linear and may even take multiple sessions to complete. However, the visitor is bound to land on the following types of pages again and again:

  • Home page
  • Category page
  • Product page

You can drive product discovery and selection by providing tailored recommendations for each page type and varying the recommendations for new and returning visitors.

Home page recommendations

The home page is the virtual front window of your webshop. Both new and returning visitors often start their journeys here, so engagement is key.

For new visitors, recommend products that have been popular with other visitors:

  • Bestsellers
  • Currently trending products
  • Most viewed products
  • New products
  • Products on sale

For returning visitors, recommend products with which they have history from their previous visits:

  • Products they have recently viewed
  • Products that complement their recent purchases
  • New or popular products that are related to their previously viewed or purchased products

Home page recommendation for popular products displayed to a new visitor

Figure: Home page recommendation for popular products displayed to a new visitor (click to enlarge)

Home page recommendation for recently viewed products displayed to a returning visitor

Figure: Home page recommendation for recently viewed products displayed to a returning visitor (click to enlarge)

Category page recommendations

Product category pages drive discovery. Since new visitors often land on category pages, these pages must also engage.

For new visitors, recommend products that you know to be popular with other visitors in the current category:

  • Bestsellers in the category
  • Products currently trending in the category
  • Most viewed products in the category
  • New products in the category
  • Products on sale in the category

For returning visitors, recommend products based on their history and interests:

  • Products they have recently viewed in the category
  • Products that complement their recent purchases in the category
  • Products in the category that other visitors have viewed or purchased together with a product or category in which the current visitor has an interest

Category page recommendation for trending products displayed to a new visitor

Figure: Category page recommendation for trending products displayed to a new visitor (click to enlarge)

Category page recommendation displayed to a returning visitor based on their interests

Figure: Category page recommendation displayed to a returning visitor based on their interests (click to enlarge)

Product page recommendations

Visitors typically spend most of their time on product pages (also known as product detail pages and product information pages). Whether or not a visitor finds what they're looking for on a product page, make sure that they have more to explore by recommending other relevant products. Product pages are a great opportunity to increase average order value by prompting visitors to discover and select additional products.

You can recommend mostly the same sets of products to both new and returning visitors, such as:

  • Alternatives to the current product, including ones that are of a higher quality (and price)
  • Complementary or associated products, such as lenses for a camera or batteries for a toy
  • Most viewed or purchased products from the same category
  • Other relevant products based a visitor's recent views and purchases
  • Products in the same category that other visitors have viewed in addition to the current product
  • Promotional products

Figure: Product page recommendation for products other visitors have viewed in the category (click to enlarge)

Cross-selling and upselling products at checkout

Shopping cart recommendations are arguably one of the most-used product recommendation types. Shopping cart recommendations are effective in increasing average order value by cross-selling and upselling complimentary products.

The simplest way to benefit from shopping cart recommendations is to show generic, inexpensive products that most people know and use, such as socks, screen wipes, or pillowcases. You can also recommend complimentary products and highly specialized accessories to the original product. However, it's important to understand when to display recommendations. First, let the visitor proceed far enough in the shopping funnel, and show complimentary products only when the purchase decision has been made. Secondly, do not disturb the purchase flow. Show products that the visitor can add to the shopping cart without having to view details about colors, sizes, or compatibility with other products.

Recommending products in the shopping funnel

Figure: Recommending products on the shopping cart page

Recommending content instead of products

Recommending content, such as articles and stories, instead of purchasable products, is relevant to both media sites and ecommerce sites. Many retail sites nowadays include a blog or user-generated content to provide more value to visitors. Content recommendations increase the time visitors spend on the site and improve visitor engagement. The revenue for the content comes from ads and, for many media sites, from the increased number of subscriptions.

To implement content recommendations, you can rely on most viewed articles or articles viewed by other visitors with a similar profile. You can also employ natural language processing (NLP) algorithms that recommend content based on similar words or word combinations.

Recommending trending articles

Figure: Recommending trending articles

Building your entire webshop with recommendations

The most effective recommendations often combine different data sources and recommendation algorithms. Many retail businesses already build their entire webshops with recommendations, that is, grouping and ranking products based on different criteria.

This approach lets you both personalize content for individual visitors and highlight products you want to promote. You can combine, for example, the visitor's purchase history, default products, most sold products, and products related to the ones the visitor has recently viewed.

For the best effect, in addition to recommendations, let your visitors take control by providing them with filters they can use to customize the product catalog even further.

Building a store front with recommendations

Figure: Building a store front with recommendations

Frosmo Recommendations system

The Frosmo Recommendations system is an end-to-end solution for generating recommendations in the Frosmo Platform. You first define what kind of recommendation you want to generate, and then retrieve and display the generated recommendation data in a modification on your site.

The Frosmo Recommendations system generates recommendations by feeding usage data collected from the site to one or more algorithms that produce relevant results from that data.

Recommendations in the Frosmo Platform

Figure: Recommendations in the Frosmo Platform

Building blocks of a recommendation

A recommendation is the end product of multiple Frosmo components working together. The following figure shows three components coming together on a web page to form a recommendation slider.

Components of a recommendation coming together on the page

Figure: Components of a recommendation coming together on the page

A recommendation is the product of the following components:

  • Recommendation data generated from a recommendation strategy or recommendation configuration. The data consists of the details of one or more recommended items, while the strategy or configuration defines the logic and settings for generating the data. By default, the items are in descending order of rank, with the most recommended item (as defined by the algorithm) ranked highest.

    Strategies are the default and preferred way of generating recommendation data. If possible, use a strategy instead of a configuration when creating a new recommendation.

  • Template for creating the web page element for the recommendation. The template defines how the recommendation is displayed on the page. The template must:

    • Define the framing element for the recommendation. This is the static part of the recommendation: the HTML, CSS, and JavaScript that together define how the recommendation looks and behaves on the page.
    • Fetch the recommendation data, and populate the element with that data. This is the dynamically generated content of the recommendation: the details of the recommended items.

    The static and dynamic parts together create the final element as displayed on the page.

  • Modification for displaying the recommendation on the page. The modification must:
    • Use the template for its content.
    • Set the content options, such as which recommendation strategy to use, defined in the template, if any.
  • Placement for determining where the modification is placed on the site: on which web pages and where on those pages.
  • Segments (optional) for displaying the modification only to a specific subset of visitors. If you do not want to display the recommendation to all visitors, define one or more segments for the modification.

Where to go next