Sell more of the products you want to sell, with category page recommendations.

We always suggest that retailers place personalised suggestions and product recommendations at the top of category pages, as not only does it benefit the visitor by showing them relevant products quicker, it allows retailers to consciously achieve their own business goals.

Here’s an example.

• A returning known visitor arrives on the “Sweatshirts and Hoodies” category page.
• The first product in the top row shows the last item in the category browsed by the visitor
• The other three products are the most popular sweatshirts and hoodies that are related to the visitor’s profiled colour and brand preferences, but over £40 to increase revenue

There are two main ways that category page suggestions can be integrated into your site.

Take over the first row of products

This approach involves using personalisation technology to return the first row of products, and styling these to match the normal product listings.

If looking to user this approach, there are two important factors to think about. Firstly, make sure that as users filter further into the category, the product suggestions also adapt. If not, it can disrupt the visitor journey and irritate the user. Secondly, ensure that if users select another sorting scheme (e.g. price, name, etc) the top row is taken over by the platform, as there are all sorts of complications that can arise if not.

This integrated approach means that consumers can skim over the first row as normal, without presenting any disruptions or new concepts for visitors to psychologically process.

Seperate suggestions with a block

Seperating product recommendations from standard category listings allow retailers to make these suggestions stand out more and subtly encourage visitors to consider these products.

This approach has many different uses for each different retailer. For example, one retailer might prefer to call the block Bestsellers, but actually promote “worst sellers”; another retailer might prefer to name the block “We think you might like” and show the most relevant suggestions, regardless of any price or filter.

What algorithms should be used?

There are many different algorithms that can drive the “logic” behind category page product recommendations.

Firstly, it depends on the visitors situation and context – if a visitor is new and unknown, the system won’t have any behavioural information about them and therefore should fallback to crowd-wisdom suggestion. If a visitor is returning, and therefore has a profile built up, more personalised suggestions can be shown.

Secondly, it depends how your visitors shop, and your store is set up. For example, if you have a limited product range, (e.g. you sell computers and laptops and not much more) then profiled behaviour can be taken into account more prominantly, as visitors will likely be shopping for the same or similar reasons.

If you are a department store with many different product categories, then visitors could be shopping for themselves, for family or for friends. In this case, real-time algorithms such as clickstream (people who have had a visit like you bought/browsed) or attribute shadowing (you’re looking at products made from leather in a size 12) can understand the visitor’s intent, and recommend the appropriate relevant products.

Anything else I should know?

Currently, only sites that are deeply integrated with personalisation technology can benefit from category page recommendations. This deep integration is needed because unless the personalisation technology can understand the products already shown in the natural listings, it may show duplicate products that lead to a disrupted visitor experience.

With our deeply integrated Magento extension and deep integration into other playforms, category page suggestions can appear in both PHP and Javascript mode. Javascript-only technologies might find it hard to understand which products from the listings to exclude from the suggestions.

How can I find out more?

We’re glad you asked!

You could take a look at our Technology Guide for more information on the different types of technology used, or alternatively you can:

Call us on +44 (0) 1202 83 20 30 or +1 (877) 298-9380

Email us at hello@predictiveintent.com

Live Chat with us – click the blue box on the bottom-right corner if we’re online.

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1. How hard are your “recommendations” working?