Site search engines in the e-commerce world have a tendency to operate in isolation. They receive a query and return results. This works well till the site reaches a couple of thresholds. These are:
- when you cannot improve the accuracy or quality of search results anymore through rule changes.
- when the human effort required to manage promotions and merchandizing is enormous.
- when the buzz around certain products is so high and variable from day to day and week to week, that by the time business rules change, the popularity has ebbed or the pattern has changed.
Search working in isolation is a problemWhen site search works without feedback from the website, we are basically assuming a static product catalog and an unchanging buyer behavior. This is certainly not the case in most real-world stores. Merchandisers work around this by tracking stats such as:
- Top selling products
- Latest deals
- Highest margin products
- Highest discounted products, etc.
- Track and classify the products based on the above criteria.
- Create and manage such business rules for each of the above.
- And if you’re lucky, provide a management dashboard that enhances merchandiser productivity, accuracy and turn-around time for such requests.
Suggested Read: – Why Site Search is Important for Your eCommerce Business?
Achieving accuracy at scale is better off automatedLets say you put in the human effort needed to build the merchandising rules for promotions, cross-selling and up-selling based on the data you’re collecting. At the scale I am talking about, the risk of a human creating an incorrect rule is pretty high. Furthermore, with the variable patterns in buying and browsing behavior, keeping the rules updated is an even tougher task. This is where e-commerce search needs to automate:
- the collection of behavioral data
- quantifying the data into metrics
- then, feeding the metrics into the search system for creating merchandizing rules, on-the-fly.