Returns Data Optimization
Project: Update information architecture to better support usability and usefulness for Brand Advocates
My Role: UX Research​
Scenario
David, a Home Depot Brand Advocate, helps to manage the digital content and online merchandising for multiple suppliers sold on HD.com. Querying data from the Nuro database allows him to view data such as the total dollars YTD in returns for online orders by the return type. This data helps to establish KPI's, however, the query outputs display null values for return type fields.
Assumption: The Associate/Customer does not always enter a return reason when processing a return and results in null data being pulled
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Objective: Identify current state associate and customer journey to determine issues impacting data pulls using the following methods
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Analytics Review
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Identify User Types
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Identify User Journey
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User Interviews
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Competitive Analysis
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Synthesize Findings
User Research
Examine Returns process
Identified all the users in the return process; in-store and online contact center associate and customer using self-service)
Interviewed 12 cross-functional associates to understand user flow and discover insights that would guide further research
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Conducted a longitudinal observation of an in-store cashier performing a return transaction from start to finish
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Performed a “secret shopper” competitive analysis of the customer return experience in store and online
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Interview Group Job Titles
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Store Associate, Front End Cashier
Analyst, Online Analytics
Sr. Analyst, Operations Business
Product Manager, Availability & Inventory
Product Manager, Contact Center Experience
Manager, Business Process
Manager, Front End Ops
Manager, Operations Process - Store Operations
Manager, Training and Development - Online Contact Centers
Sr. Manager, Front End Operations - Associate Experience
Sr. Manager, Learning
Sr. Director, Brand Advocate
Identify Issues Impacting Data Pulls
I outlined the user flow for each user group, return reason codes used by each user and the data sources each return reason code feeds into. This helped to pinpoint data discrepancies between the in-store, online call center, and Self-Service returns reason codes.
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Benchmark Best Practices
​Performed a “secret shopper” competitive analysis of the customer return experience in store and online to document the process and associated return codes used by comparable retailers.
Synthesized Date
Findings:
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Return reason code is required no matter the user type performing the return
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Return reason codes are not a 1:1 match with codes across in-store associate, online associate or customer access points
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Code discrepancy generates null values in the database
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There is also an 8 week lag within the Tableau data being leveraged
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My secret shopper return experience with THD was effortless and I found that the return reason codes aligned with those of competitors.
Output: Return Process User Flow
Output: Return Reason Code Benchmarking
Recommendations
Prioritize Fixes by Greatest Impact
Align mapping across information architecture (online, in-store, and call center returns). Tie specific Return $, Return Rate %, and Return Units to Reason Codes. Align NURO returns with the Tableau timeframe (8 week lag). This will allow better organization, usability, and find-ability of data.
