Goodyear TRE — Case Study

01 — Context & Executive Intro

How X Was Solved
with Y Outcome

Goodyear dealerships struggled with manual tire recommendation and inventory verification, which slowed down customer interactions and led to inconsistent suggestions.

We solved this by building an AI-powered Tire Recommendation Chatbot (TRE) that automates tire selection based on vehicle details and provides real-time inventory visibility across dealerships.

👉 Outcome: Reduced response time from minutes to seconds, improved recommendation accuracy, and increased sales efficiency.

Goodyear dealerships and associated distributors previously lacked a centralized system capable of providing accurate tire recommendations based on customers’ specific vehicle details. With a growing number of car brands, models, and trim levels — each varying by year, climate suitability, and performance requirements — sales associates found it difficult to suggest the right tire quickly and confidently.

Additionally, dealership teams relied on manual methods (phone calls, emails, spreadsheets) to verify inventory levels at their own or nearby dealerships. This fragmented process led to slow response times, inaccurate information, and missed sales opportunities.

Solution Overview & Key Features

1. AI Vehicle Mapping

Accurately identifies compatible Goodyear tires based on Brand, Model, and Year of manufacture.

2. Conversational Query Flow

Accepts natural language inputs like “BMW 5 Series 2017” and prompts for additional info such as ZIP code.

3. Real-Time Stock Visibility

Displays available stock quantity, warehouse details, and alternative dealerships nearby.

4. Recommendation Cards

Contains Brand, Size, Facility ID, City, Distance, OEM/Replacement Type, Speed Rating, Warranty, and Margin.

5. Nearby Dealer Locator

Identifies alternate facilities with available inventory within a set geographic radius.

6. Seamless ERP Integration

Connects directly with Goodyear’s internal ERP systems for live stock access and updates.

North Star Goal

The core objective of the Goodyear Tire Recommendation Engine (TRE) is to automate tire recommendations and improve dealership efficiency by reducing human dependency and manual intervention. The tool focuses on speed, consistency, and intelligent data integration.

02 — Problem & The Ask

Two-Fold Challenge
& Project Ask

The Two-Fold Problem Breakdown

No standardized system to recommend tires based on Brand, Model, Year, and usage conditions.
No visibility into nearby dealership inventory, forcing manual phone calls and emails to check stock availability.

Resulted in: Slow customer service, high human dependency, inconsistent recommendations, and missed sales opportunities.

👉 The Design Ask

Design a scalable, intelligent B2B solution that recommends accurate tires instantly, shows real-time stock across warehouses, suggests nearby dealerships when primary stock is unavailable, and reduces manual effort.

Objectives & Strategic Value

01

Automate Fitment

Eliminate manual lookup tables and improve accuracy via a centralized AI database mapping.

02

Enhance Efficiency

Minimize customer response time to under 3 seconds and streamline inter-dealership coordination.

03

Enable Scalability

Integrate real-time data from all outlets and standardize workflows for rapid regional expansion.

Legacy Manual Process vs. Goodyear TRE

Workflow Area	Legacy Manual Process	Goodyear TRE AI Solution
Fitment Verification	Catalog Reference (Manual books)	AI Mapping Engine (Instant lookup)
Stock Check	Phone Calls / Emails (Manual)	Live ERP Feed (Instant stock ID)
Nearby Outlet Locator	Trial & Error (Uncertainty)	ZIP Geolocation Radius (Exact distance)
Response Time	5 – 15 Minutes per customer	< 3 Seconds end-to-end completion
Recommendation Logic	Inconsistent (Associate dependent)	100% Standardized (OEM logic)

User Pain Points Matrix

Manual tire lookup through catalogs

High Freq

High Sev

"Caused slow recommendations and high dependency on individual associate experience."

No visibility of nearby dealer stock

High Freq

High Sev

"Dealers made phone calls to verify stock, drastically increasing customer wait times."

Lack of standardized recommendation logic

High Freq

Med Sev

"Inconsistent tire suggestions across dealerships for identical vehicle trim levels."

Time-consuming customer interaction

Med Freq

High Sev

"Long waiting periods during consultation heavily impacted customer satisfaction."

Incomplete vehicle-to-tire mapping

Med Freq

Med Sev

"Limited catalog data directly led to incorrect product recommendations."

03 — Process & Artifacts

System Design
Approach

We followed a problem-first, system design approach to create a scalable conversational interface.

A. Discovery & Understanding

Analyzed workflows, identified friction points (manual lookups), and defined core inputs: Brand, Model, Year, Zip Code.

B. Information Architecture

Designed a chat-first interaction model structured into: Input capture, Recommendation engine, Inventory lookup, Nearby mapping.

C. UX Design

Built conversational flows for progressive data collection and designed recommendation cards for fast scanning under pressure.

D. Prototyping

Low-fi chat logic, Mid-fi interaction states, and Hi-fi production-ready UI aligned with Goodyear's system.

04 — Core Journey

Step-by-Step
Experience Flow

👉 Key Flow Highlight: Input → AI Mapping → Inventory Check → Recommendation Cards → Nearby Alternatives

1

Input Capture Natural Language

Sales rep enters “BMW 5 Series 2017”. The Chatbot instantly identifies missing parameters and proactively prompts for the customer's ZIP code to proceed.

2

AI Processing Background Task

The AI Engine simultaneously maps the Vehicle to compatible tire sizes/specs and maps the Location to nearby Goodyear warehouses within the optimal radius.

3

Output Generation Recommendation Cards

Displays multiple tire options in a highly scannable card format containing: Tire Name, Size, Stock quantity, Facility details, Speed rating, Warranty, and Margin %.

4

Alternative Suggestions Stock Failsafe

If the primary stock is unavailable at the current location, the system automatically suggests the nearest alternate Goodyear dealership, showing exact distance and available stock.

5

Decision & Checkout Conversion

The sales associate instantly recommends the optimal tire or redirects the customer with reserved inventory, closing the deal smoothly without any wait time.

05 — Live Prototype

Interactive AI
Recommendation Simulator

Test the recommendation engine below. Input vehicle parameters to see how the system queries the mock database and generates a standardized, high-density Recommendation Card.

Goodyear TRE Engine — Configurator

ENGINE ONLINE

Brand

Model

Year

Customer ZIP

Goodyear

Eagle F1 Asymmetric 6

In Stock (12)

Size245/45R18 100Y XL

Facility IDGY-8821

LocationAkron, OH (3.2 mi)

TypeOEM Replacement

Speed RatingY (186 mph)

Est. Margin34%

Awaiting parameters...
Click "Run AI Recommendation"

06 — Design System

Brand Guidelines &
Component Specs

Goodyear Design Standards

Design System: Built following Goodyear’s Digital System.
Frontend Library: React-based framework for scalability.
Accessibility: WCAG-compliant design with high contrast.
Identity: Goodyear Yellow and Corporate Navy equivalents.

Recommendation Card Attributes

10 required fields for immediate associate scanning:

Brand & Tire Name
Tire Size String
Quantity Available
Facility Name & ID
Zipcode & Distance
OEM / Replacement
Max Speed Rating
Warranty & Margin

07 — Research & User Needs

Discovery Insights
& Personas

Translating empirical dealership research into actionable design decisions, priority user needs, and targeted user personas.

Sales Executive

Mark

Goals

Provide accurate recommendations quickly and close sales without wait times.

Needs

Automated mapping using basic car details; instant stock insights.

Inventory Manager

Sarah

Goals

Ensure balanced stock levels and coordinate inter-dealer transfers.

Needs

Unified dashboard with real-time sync and automated facility locator.

Regional Sales Lead

David

Goals

Maintain sales consistency and standard across all regional outlets.

Needs

Consistent, data-backed recommendations across every dealership.

08 — Impact & Decisions

Business Outcomes
& Platform Value

Response Time

< 3s

Reduced customer wait from manual minutes to instant AI seconds.

Manual Effort

0%

Reduction in catalog lookups and stock phone calls.

Rec Accuracy

>95%

Correct tire fitment mapping eliminating associate guesswork.

Usability

0%

Adherence to Goodyear's digital component specifications.

Business Impact Summary

Automated Validation: Completely eliminated manual dependency for fitment and inventory.
Enhanced Experience: Drastically improved speed, accuracy, and transparency during consultations.
Operational Scalability: Built on a standard backend ERP integration framework.

09 — Technical Appendices

Data Integration
Schemas

JSON Feed Schema

{
  "query": {
    "brand": "BMW",
    "model": "5 Series",
    "year": 2017,
    "zip": "44313"
  },
  "results": [
    {
      "tire_name": "Eagle F1 Asym 6",
      "size": "245/45R18 100Y XL",
      "facility_id": "GY-8821",
      "stock": 12,
      "dist_miles": 3.2
    }
  ]
}
          

Sequence Diagram Flow

[User Chatbot UI]
   │
   ├── (Brand, Model, Year, ZIP)
   ▼
[Recommendation Engine]
   │
   ├── 1. Fitment Query ──► [Vehicle DB]
   ├── 2. Radius Query  ──► [Maps API]
   └── 3. Stock Feed    ──► [GY ERP]
   │
   ▼
[Render Scannable Cards]
          

How X Was Solvedwith Y Outcome

Solution Overview & Key Features

Two-Fold Challenge& Project Ask

The Two-Fold Problem Breakdown

👉 The Design Ask

Objectives & Strategic Value

Legacy Manual Process vs. Goodyear TRE

User Pain Points Matrix

System DesignApproach

Step-by-StepExperience Flow

Interactive AIRecommendation Simulator

Brand Guidelines &Component Specs

Discovery Insights& Personas

Business Outcomes& Platform Value

Data IntegrationSchemas

How X Was Solved
with Y Outcome

Two-Fold Challenge
& Project Ask

System Design
Approach

Step-by-Step
Experience Flow

Interactive AI
Recommendation Simulator

Brand Guidelines &
Component Specs

Discovery Insights
& Personas

Business Outcomes
& Platform Value

Data Integration
Schemas