Discovery Intelligence v1.0

Customer Discovery
Intelligence Toolkit

A structured framework to run productive, non-leading conversations with EV ecosystem stakeholders. Validate actual battery diagnostics pain patterns, establish warranty/resale trust parameters, and load dynamic JSON questionnaires.

Back to 12-Week RoadmapSample Discovery POC ↗
Introduction

Customer Discovery Overview

Under Week 2 of our Battery Diagnostics Roadmap, our singular goal is to validate market pain before writing core software. We bypass theoretical assumptions by listening directly to operators, technicians, dealers, and owners.

  • Target: Conduct at least 10 high-quality interviews.
  • Dynamic Execution: Load survey questions dynamically from central configuration files.
  • No bias: Let stakeholders explain operational difficulties in their own words.
Strategic Logic

Why Customer Discovery Matters

Building battery intelligence algorithms requires a deep understanding of operational bottlenecks. Without customer validation, we risk overengineering features nobody wants or neglecting critical friction points like used EV financing or warranty claim lags.

The Core Philosophy: “A week of coding can easily save you an hour of customer discovery — but it will cost you months of misaligned efforts.” We align our engineering sprints directly with documented industry urgency.
Methodology

The Structured Interview Process

Follow this three-phase pipeline for every discovery discussion to ensure clean, structured data collection.

Phase 01

Setup & Context Capture

Establish trust immediately. Clearly state that you are there purely to learn, not to sell any product or platform. Request permission to take notes, photos, or audio recordings.

Key Task:
Record name, phone number, location, vehicle brand/model, battery type (LFP / NMC), and ecosystem category before starting the questionnaire.
Phase 02

Active Listening Questionnaire

Switch to the corresponding category tab below. Read questions slowly, using simple language. Avoid offering examples too quickly; let the participant think and respond in their own words.

Key Task:
Take detailed notes on exact vocabulary used (e.g. range anxiety, battery calibration, drop in pickup). Record native language choice.
Phase 03

Closing & Synthesis

Ask the common final questions regarding pain scales (1-10) and pilot interest. Express gratitude using the thank-you script and schedule potential follow-ups once a diagnostics prototype is prepared.

Key Task:
Transcribe notes into the central dashboard immediately post-interview to prevent cognitive decay.
Core Commandment

Interviewer Rules & Best Practices

Discipline during interviews is non-negotiable. Maintain high objectivity to avoid false positive validation.

Rule Checklist

What to AVOID

  • Do NOT sell a product or pitch an idea. Pitching shifts the client to "feedback mode" rather than "pain disclosure mode."
  • Do NOT ask leading questions like “Wouldn't it be great if you had a diagnostics report?”
  • Do NOT interrupt the participant when they are describing a workflow or complaint.
  • Do NOT argue or attempt to correct the participant if they misunderstand battery physics.
Rule Checklist

What to DO

  • Do not sell any product during the discussion.
  • Ask questions slowly and in simple language.
  • Let the customer explain in their own words.
  • Record language used: English / Kannada / Hindi / Tamil / Mixed / Other.
  • Take photo/audio/video only with permission.
  • At the end, thank the participant politely.
Intake Template

Contact & Context Information Template

Capture these parameters systematically before launching into the core questions.

Participant NameJohn Doee.g. Fleet Operations Manager
Contact Number+91 98765 43210Keep secure and separate
Ecosystem RoleUsed EV DealerEV Owner / Fleet Operator / Refurbisher etc.
Vehicle Brand / ModelAther 450X / Ola S1 ProRecord brand, variant and vehicle age
Battery ChemistryNMC / LFP / UnknownIdentify if possible
Primary LocationBengaluru, KarnatakaRecord city & neighborhood
Questionnaires

Dynamic Role-Specific Questionnaires

Questions are loaded dynamically from the central JSON configuration file. Wording is preserved exactly.

EV OWNER / RIDER Questionnaire
1
Which EV do you use? Please mention brand, model, and vehicle age.
2
How many kilometers do you usually drive per day?
3
Have you faced any battery-related problem so far? Examples: range drop, slow charging, battery not holding charge, overheating, sudden shutdown.
4
What is the biggest battery problem you have faced?
5
When did you first notice this problem?
6
How do you currently know whether your battery is healthy or weak?
7
Did you visit a service center for this battery issue? If yes, what did they check and tell you?
8
How much money or time did this battery issue cost you?
9
Before buying or selling a used EV, would you want a trusted battery health report? Why?
10
How much would you be willing to pay for a trusted battery health check/report?
Consolidation

Common Final Questions

Ask these closing questions to quantify pain priority and identify early adopters for pilot testing.

F1
What is the single biggest battery problem we should solve first?
F2
How urgent is this problem for you? Low / Medium / High / Critical
F3
On a scale of 1 to 10, how painful is this problem?
F4
Are you willing to discuss again after we prepare a solution or prototype?
F5
Can we contact you later for follow-up?
Closing Script

Thank You Message

“Thank you for sharing your experience with us. Your feedback will help EV.ENGINEER understand real EV battery problems and build practical solutions for battery diagnostics, health intelligence, safety, and customer trust.”
Analytics Mockup

Discovery Intelligence Dashboard

A simulated look at the future platform showing aggregated insights from completed discovery surveys.

Active Discovery Intel
Week 2 Sprints
12
Interviews Held
120%
Target Achievement
8.4 / 10
Avg Pain Score
92%
Follow-up Rate

Primary Documented Pain Distributions

Range Drop / Sudden Shutdown85% Urgency
Used EV Resale Trust deficit78% Urgency
BMS Diagnostics Deficiencies64% Urgency
Second-Life Battery Grading Lags58% Urgency

Completed Cohort Mix

EV Owner / Rider3 Surveys
EV Service Center2 Surveys
Used EV Dealer2 Surveys
Fleet Operator2 Surveys
Battery Refurbisher2 Surveys
EV Startup / OEM1 Survey
Machine Learning

Simulated AI Insights

Examples of automated text processing extracting structural pain points from unstructured interview audio/text notes.

Used EV Resale bottleneckHigh

"Dealers are unable to close used EV sales due to battery health trust issues. Buyers are afraid of buying a dead battery. A certified SOH grading report is critical for financing approval."

👥 2 Dealers
🎯 Resale Price impact: 30%
Unplanned Fleet DowntimeCritical

"Fleet operators lost over ₹12,000 per vehicle last month due to sudden battery shutdowns. They require 30-90 day predictive failure warning systems to optimize battery cycles."

👥 2 Fleet Ops
🎯 Downtime loss: ₹24,000
Technician Diagnostics GapHigh

"EV service centers lack reliable tools to diagnose actual cell health. They rely on basic BMS error logs which fail to catch localized micro-short thermal runaways before they develop."

👥 2 Techs
🎯 Tool Spend: Willing to pay ₹2k/mo
Circular Economy Vision

Future Discovery Platform Vision

Our ultimate objective is to construct a scalable **Circular Discovery Intelligence Hub**. The insights gathered from our manual Week 2 interviews will serve as foundational rules to train natural language models. In future stages, when developers upload unstructured service center logs or used EV dealer transaction records, our automated pipeline will parse and isolate telemetry risk scores, mapping resale and SOH reliability metrics instantly.