Any e-commerce company either mid enterprises or large enterprises vision and goals will be to target more site traffic and increases in revenue through its platform Searchandiser. Recent Search technology leverages machine learning and predictive analytics to deliver a fully-orchestrated experience with optimized content and messaging to increase engagement and conversions across all customer touch points. Intelligent Recommendations are expected before what your customers want to do. By showing customers the right product at the right time in their customer journey you can increase your online conversion and revenue. Recent years, Tremendous changes evolved in search platforms when compare to traditional search. DigiSprint expert team in Searchandiser area will guide and drive the clients to sail through safety in e-commerce Searchandiser platform. Check below recent evolution happened in search platform.
Search Intelligence Spectrum
Any Search platform should support both traditional and latest trends in search query.
Basic Keyword Search which consists of inverted index, TF-IDF, multilingual text analysis, query formulation etc. were used during the early stage.
Next Query Intent Search which consists of query classification, semantic query parsing, personalization etc. came into existents.
Latest Self-Learning and automated Relevancy Turning which consists of signals boosting, Learning to Rank etc. are ruling the e-commerce Searchandiser environment.
Search & Data Connectors
Different type of old and new search capabilities are expected
- Keyword search
- Geo Sparital search
- Faceted search
- Search engine optimization [SEO]
- Search as you type
Large number of data connector types should be supported to supply the data to search engine index.
Business team or Merchandiser should have option to modify the relevancy or score of the documents by adjusting the rules in merchandising tool like visually create rules to pin, boost, bury, block and hide specific products for a search term or category page. Merchandiser should not rely much on IT team for turning the relevancy. Even they should have access to reports or analytics of the searched data like top clicked products, top search terms & trending search, and top no search or empty results
AI-powered search provides the next generation of search result relevance that learns from user behavior in real-time as they’re searching to help bridge the gap between human and computer language.
Learning to rank [Generalized Relevancy]: Teach computers on which product to rank high and which product to rank low by using the user’s signals, content, & other sources.
Signal Boosting [Popularized Relevancy]: Search platform should capture the user’s click on products, product purchased, product rated, etc., and re-use in the query pipeline to yields accurate search results.
Collaborative Filtering [Personalized Relevancy]: Recommendations based on user’s product purchased, user’s location, user’s search history, user’s rating etc.
Highly increase in product count, documents, user signal capture data size, and indexing size of search platform. Need extra care for deployment of such huge environment in the proper deployment environment. The team must have expertise either in a cloud environments or on-premises infra-structure experience to deal with the deployment in container-orchestration.
DigiSprint team assisting the clients on the following search products.
- Fusion Solr
- Elastic Search
- Oracle Endeca
- Oracle Endeca with Oracle Web Commerce