Recommender Systems

Personalised search experience

Personalised search experience

Recommender systems allow you to offer a more personalised and powerful search experience on your website. 35% of consumer purchases on Amazon come directly from product recommendations. Some eCommerce companies report a double-digit lift in conversion rates after using them.
 
But higher sales are not the only benefit. Content recommender systems can boost customer engagement and retention. You can serve your audience with dynamic content recommendations based on their intent. This brings them deeper into your sales funnel until they are ready to convert.
SEO team

How we can help you

Our data scientists can help you choose and set up the best recommendation engine solution for your business. Or we can set up a custom-built algorithm to suit your marketing needs, including:

  • Cross-sell/up-sell eCommerce
  • After-sales
  • Proximity marketing
  • Out-of-stock products
  • Content recommendations

Our recommender systems service

Analysis

First, we’ll analyse your current business model and product range. We will need to understand your customer journey scenarios and your marketing goals. We can then provide you with a list of solutions to meet your business needs and budgets.

Data preparation

Our data scientists will help you determine which data matters the most for recommendation delivery. We’ll also advise how to merge and cleanse it for further analysis. At the same time, our development team will start to build the engine.

Roll-out & support

Our team will help you set up and integrate the new engine into your marketing setup. We can provide in-house training if needed. Once your new system is up and running, we’ll stay in touch and provide you with extra insights on how to improve your system’s performance. This will ensure you deliver the highest levels of personalisation.

Our clients

P&O
Help for Heroes
Hillier
Mitchells & butlers
Fatface
KFC
Keytek
Sarah Raven
Stagecoach
Ontrack
Waste Group
PODFather
View case studies

Contact us

If you would like to provide a more personalised experience to your website visitors, submit your details below.

Frequently Asked Questions

A recommender system uses customer data and machine learning to recommend products or content. This is based on their purchase history and browsing habits. The technology allows you to show relevant, targeted listings to users who are most likely to take further action. You can show related accessories, relevant content or items other users have shown an interest in.
 
Recommender systems are used by some of the biggest names in retail and consumer software. Think Amazon, eBay and Netflix. They are able to maximise sales and engagement through relevant recommendations.

eCommerce websites are the obvious candidates for recommender systems. They help you increase sales by showing relevant product listings whilst users browse. Content platforms like Netflix and Spotify use recommender systems. Also social networks use their own systems to maximise engagement with individual users.

Not if you install the technology properly and follow privacy guidelines, GDPR included. It’s worth noting there are several ways to install recommendation systems and make use of data. Not all of them rely on personal data.
 
Even if you’re using personal data in your recommendation system, the technology itself is relatively GDPR-friendly as it legitimately enhances the customer experience. As long as your consent system is GDPR compliant and you take reasonable steps to protect user and customer data, you shouldn’t have any problems.

A recommender system doesn’t directly affect your search ranking. But the improved experience and engagement they provide will have a positive impact on eCommerce SEO. They encourage users to click through to more pages and spend more time on your website. They help users interact with more content and can increase conversions per session. A recommendation system can boost a lot of the engagement and UX signals Google pays attention to.

These two terms are often used to reference the same thing. A recommendation engine has the algorithms and AI technology that power a recommender system. The system itself is the complete product or implementation on your website.