BestBuy x NPS Analysis for Voice Of Customer
UX Research for Customer Navigation Experience
This project is part of the Voice of Customer (VoC) program, an initiative led by the Best Buy Canada eCommerce Operations Community. The VoC program aims to capture and amplify customer insights across the organization to enhance the end-to-end digital shopping experience.
By analyzing NPS feedback, the research uncovered key friction points in product discovery, search, and navigation that hinder customers from finding what they need efficiently. These insights helped the Product and Design teams prioritize opportunities to improve findability and enhance the overall shopping experience.
ROLE
User Experience Researcher
TIMELINE
4 Weeks | Feb 2020
Industry
eCommerce
METHODOLOGY
NPS Analysis
Text Analytic
Affinity Diagram
STUDY OVERVIEW.
The goal of this project was to analyze NPS feedback to identify recurring themes related to product discovery, search relevance, navigation flow, and filtering experience, in order to uncover friction points that impact customers’ ability to find what they need efficiently.
IMPACT.
Acted as the voice of the customer, serving as an information hub to facilitate collaboration across multiple business teams.
Analyzed NPS customer feedback and provided actionable recommendations to enhance the website’s digital experience.
Developed a scalable strategy for the eCommerce Operations Community through the VoC program’s discovery phase, enabling ongoing insight sharing and process improvement.
Research Method.
My analysis followed four key steps: collect, review, categorize, and analyze.
Data Source: NPS Customer Experience Portal (Best Buy internal database)
Time Range: FY2020 Q4 (Nov 3 – Feb 4)
Sample Size: 1,660 responses
PROCESS.
Keywords Spotting
Data Collection: Raw data were extracted from NPS Non-Purchaser Verbatim responses.
Data Cleaning: To refine the dataset, I applied a keyword spotting technique in Excel using a predefined list of terms related to the Find product team’s focus areas (e.g., search, filter, navigation).
Filtering Relevant Feedback: This process helped narrow down valid and relevant customer feedback, ensuring that only insights tied to product findability and navigation challenges were included in the analysis.
Keyword Framework
To identify relevant customer feedback, I developed a three-tier keyword structure aligned with the Find Product Family focus areas. This framework ensured consistency and precision during the data cleaning and analysis process.
Primary Keywords (Product Family): Filter, Facet, Search, Navigate, Find
Secondary Keywords (Generic Navigation): Categories, Current Offer, Brand, Color Family, Customer Rating, Sellers, Status
Tertiary Keywords (Facet – Current Offers): On Sale, On Clearance, Best Buy Exclusive, Online Only, As Advertised
Thematic Categorization
After reviewing the customer feedback, I categorized valid comments into themes and subthemes using Excel and an affinity diagramming approach.
The affinity diagram helped cluster similar topics, reveal patterns, and visualize relationships between different pain points. This method allowed me to uncover deeper insights into how customers experience challenges related to product discovery and navigation, forming the foundation for actionable recommendations.
Data Visualization
I conducted an in-depth analysis of “Findability” themes and visualized the most frequently mentioned keywords from NPS feedback using Power BI.
These visualizations highlighted key pain points and emerging patterns, enabling stakeholders to quickly grasp customer concerns and prioritize areas for improvement within the product findability journey.
RESULTS.
Pain Points from Non-Purchaser Verbatim
Through the analysis of NPS Non-Purchaser Verbatim feedback, several recurring pain points emerged that directly impacted customers’ ability to find products efficiently. These insights revealed key usability and navigation challenges within the eCommerce experience, including:
Filter – Available In Store
Finding:
Customers expressed a strong need to view in-store product availability directly on the Product Listing Page (PLP). Many wanted the ability to check nearby store inventory without navigating away from the search results.
Insight:
Improving visibility of local store availability on the PLP can help customers make faster, more confident purchase decisions and reduce friction in the online-to-offline shopping journey.
Filter – Marketplace & Refurbished
Finding:
Customers wanted an easy way to filter out Marketplace sellers and refurbished products directly on the Product Listing Page (PLP). Many found it difficult to distinguish between new, refurbished, and third-party listings.
Insight:
Introducing clear filters for seller type and product condition would help customers refine their searches more efficiently, build trust in product quality, and reduce confusion during the browsing process.
Facet – Specific Categories
Finding:
Customers expressed a desire for more specific and intuitive category options to guide their online shopping experience — for example, Gift Ideas, Gifts for Her/Him, or Gifts by Price Range.
Insight:
Introducing contextual and occasion-based facets can enhance product discoverability, inspire purchase intent, and support customers who are browsing without a specific product in mind.
Search – Availability of Product
Finding:
Customers expressed frustration when encountering products marked as “Sold Out Online” during their search experience. This often led to confusion and a sense of wasted effort when products appeared in search results but were unavailable for purchase.
Insight:
Improving filter functionality and labeling for out-of-stock items can reduce customer frustration, streamline the path to available products, and ultimately enhance overall satisfaction and conversion rates.
NEXT STEP.
Implementation
The Product Team adopted the recommendation and launched an “In Stock” filter option on the Product Listing Page (PLP), allowing customers to easily view only available products and reducing frustration during browsing.
Additional recommendations identified through this research were added to the product backlog for future iterations, ensuring continuous improvement of the product findability experience.
