Best Buy Product Search with Algolia

Project Overview

This project is part of a take-home assignment for a Solutions Engineer role at Algolia. The objective was to create a search interface using Algolia's search engine, showcasing my ability to index data, configure relevance settings, and build an intuitive user interface. For this project, I utilized Best Buy's Product Catalog dataset and implemented several features to enhance the search experience.

Dataset

The dataset used is the Best Buy Product Catalog, which contains detailed information about various products sold by Best Buy, including product names, brands, descriptions, prices, and popularity rankings.

Steps Taken

1. Indexing the Data

• Algolia Account: Created an Algolia account to access the indexing and search capabilities.
• Data Indexing: Indexed the Best Buy Product Catalog data using Algolia's dashboard. The data was uploaded and structured to ensure efficient searching and retrieval.

2. Configuring Relevance Settings

• Searchable Attributes: Configured the search to prioritize the following fields:
  • Name: Highest priority to ensure that products with the search term in their name appear first.
  • Brand: Secondary priority to allow users to find products by brand.
  • Description: Lower priority but included to enhance the search results by considering product descriptions.
• Unsearchable Attributes: Deliberately excluded fields like image links to avoid irrelevant matches.

3. Ranking and Sorting

• Custom Ranking: Set up a ranking and sorting system to prioritize search results based on:
  • Popularity: Most popular items appear first.
  • Rank: Items with higher customer ratings are prioritized.
  • Price: Higher-priced items are given a slight preference to promote products with a potentially higher profit margins.

4. Faceting and Filtering

• Facets: Created facets for price, rating, and brand.
  • Price: Added a range slider to filter products by price range.
  • Rating: Implemented a rating menu to filter products based on customer ratings.
  • Brand: Added a refinement list to filter products by brand.

5. Building the User Interface

• InstantSearch Library: Utilized Algolia's InstantSearch library to build the search interface.
• UI Components: Integrated various UI components to enhance user experience, including search box, filters, and result display.

6. Publishing the Project

• GitHub Repository: The project code is available in a publicly accessible GitHub repository.
• GitHub Pages: Published the project using GitHub Pages for easy interaction and demonstration.

7. Admin Access

• Admin Access: Granted Algolia admins access to the indexed data and relevance settings to facilitate review and feedback.

Project Goals

The primary goal of this project was to demonstrate my ability to work with Algolia's search engine and create a user-friendly search interface. I aimed to provide a seamless and efficient search experience for users, ensuring that they can easily find products based on relevance, popularity, and other important criteria.

Feedback for Algolia

Working on this project provided valuable insights into the capabilities and flexibility of Algolia's search platform. Here are some key takeaways and feedback:

• User-Friendly Dashboard: The Algolia dashboard is intuitive and easy to navigate, making the process of indexing data and configuring settings straightforward.
• Documentation: The comprehensive documentation and examples provided by Algolia were instrumental in understanding and implementing various features.
• Relevance Tuning: The ability to fine-tune relevance settings and ranking criteria allowed for a highly customizable search experience.
• Faceting and Filtering: The faceting and filtering options were powerful and enabled the creation of a dynamic and interactive search interface.

Conclusion

This project showcases a robust and user-centric search interface built using Algolia's search engine. By focusing on relevance, ranking, and filtering, I aimed to provide an optimal search experience for users interacting with Best Buy's product catalog.

You can interact with the project here.

ais-ecommerce-demo-app

This project was generated with create-instantsearch-app by Algolia.

Run this project locally

To run this project locally, install the dependencies and run the local server:

npm install
npm start

Alternatively, you may use Yarn:

yarn
yarn start

Open http://localhost:3000 to see your app.