With Amazon cornering Google to emerge as the preferred platform for product discovery, other e-commerce platforms need to diversify their marketing strategy. With Amazon being used by 72% users for Product Discovery, it is now an established fact that “faster product discovery can help e-commerce platforms generate more revenue”, it makes sense to improve customer experience by investing in product discovery.

Studies prove “18% of websites cannot identify existing products when even a single character is inserted wrong”. Such lack of attention to the customer experience is helping competitors gain up to 12% of users just after a failed search.

With over 43% of visitors using search functionality on e-commerce platforms, search accuracy is becoming vital for quality customer experience. While relevant results will help players acquire competitive advantage meanwhile inaccuracies can affect customer churn and loyalty negatively.

Understanding Product Discovery

The event of a customer visiting your site and finding a relevant product he or she wants to buy is known as Product Discovery. The time lapsed in finding a relevant product is called Product Discovery Time. By controlling product discovery time, enterprises can help customers and improve customer experience.

Improving Product Discovery to enhance customer experience will require E-commerce platforms to indulge in the following processes:

  • Navigational & Filtering/Sorting Discovery Analysis
  • Direct Discovery (aka Search) Analysis
  • Recommendations Analysis


A navigational search is designed to help customers find desired products faster. – It happens at different stages of product discovery, on ‘Home Page, Search/Browse Page & even at Product Page’. Designed to assist customers achieve their goals faster, optimization of Navigational Search result is critical.

Search Quality Analysis delivers customer experience enhancement through search optimization and process improvements. By traversing search results incessantly and benchmarking them against competition, our methodology allows the engineering and product teams to make requisite changes towards improving customer experience.

Similar Products and Frequently Bought Together are the most used product recommendation modes by e-commerce players. Similar Products tab on a product page includes suggestions relevant to the item a customer is viewing. Designed to assist buyers find the relevant product, this tab can be further optimized to improve customer experience.

Product Discovery - Metrics in Focus

Opportunities are identified and captured to help e-commerce platforms deliver top-notch customer experience. Either by optimizing existing processes or by revamping operations, a system is built that offers exceptional customer experience every time.

Inspecting following metrics related to Product Search & Discovery can improve customer experience incomparably:

  • Product Ranking Relevance
  • Intent Understanding
  • Query Term/String Analysis
  • Incorrect/Incomplete Spell analysis
  • Natural Language Processing
  • Home/Landing Page Optimization (from external searches)

Such in-depth scrutiny helps e-commerce platforms identify opportunities and build a high performing search function through enhanced machine learning models. We work with Product & Engineering teams to update their algorithms to one which considers these metrics for better usability.

Key Benefits

Improved Cross Selling

Better recommendations can lead to an increase in cross-selling by promoting the right products to the customers.

Increased Conversions

Search results improvement leads to faster decisions by customers increasing conversions.

Reduced Customer Efforts

With improved product discovery, customer efforts are minimized with better results for their searches.

Better Product Placements

Improvements to search and recommendations help platforms with better placements of products for better results.

Client Testimonials