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:
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.
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.
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.
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.
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.