Coveo offers a bunch of cool ML functionality. As some of you may already know, growing and providing ML relevance in various streams of website journey is one the main goals of Coveo and where it is heading as an Enterprise product. Coveo has some very good documentation on what their ML components are and how to get started with these here
But, given all that, it is still hard to put a finger on to whether a specific ask or requirement could fit in and benefit with the use of Coveo ML component. So, I decided to blog on our journey beginning from a thought to reality. It was a lot of fun and some additional twists due to lot of context specific filters that were needed to ensure we only show what is truly relevant to the context to top the recommendations that Coveo thinks are apt based on user journey.
So, in this blog I would like to focus on when to know and understand that the current requirement on hand could be a good candidate to implement Coveo solution instead to get the powerhouse technical bits that Coveo is real proud of. Now, it all started when for the very first time I heard a high level requirement around showing recommended products on a e-commerce platform based on user history in combination with couple of Sitecore based fields. My first immediate thoughts and concerns when I hear lot of content management effort would be :
- Data Integrity – When lot of manual steps are involved, it is usually hard to have a tap on data integrity even with proper workflows in place
- Tedious and frustrates Content Authors – The more there are to steps, the more it keeps getting on the pile of TODO for the team of CA. Probably the team might never get to it if the priorities change
Now, lets focus for a second on first requirement, so, it is based on user browsing history. If we are confident on the patterns and can learn from how user navigates between products, do we then have a better way of projecting what the user may be interested in rather than making our CA team work hard? Yes, it is an absolute Yes!. Coveo Recommendations component does exactly that by tracking and learning constantly as user explores the site, the model is then capable of returning recommendations.
So, say, there is a scenario where user interactions should not matter and probably the relation ship data between products is coming from a ERP or CRM based system? In this case, since we do not need relevance power of Coveo and is just mostly business logic driving a finite result set, this would be a perfect opposite use case where you could pull it off with few simple SOLR queries to get what you need.
It is very important to get the decision finalized before you dive deep and actually use a cool component Coveo has handy. So, if you are going forward with implementing Coveo Recommendations component, this is a good place to start and understand the steps needed to build one. In my next blog as part of this series, I will explain each one in detail, later will move on to context based filters, hive components to use, sample results to test some HTML with.