Trace every finding
Connect a brief to eligible record IDs and inspect exclusions.
Business and customer analytics · EDITION 1.0.0
Turn Customer Feedback into an Evidence-Based Brief
Build a defensible feedback brief with evidence IDs, selection limits, competing explanations and a repeatable Python audit.
By Sanjaay Gowda T S · For customer experience analysts, product managers and service owners.
You need: Basic spreadsheet arithmetic and familiarity with the topic; Python 3.10+ and a terminal for the optional companion project
Runtime and hardware details
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Basic spreadsheet arithmetic and familiarity with the topic; Python 3.10+ and a terminal for the optional companion project.
Full setup and scope ↓$2.99USD · one-time purchase
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review remains pending. Automated checks and agent review are not certification.
PDF + EPUB + companion project. Any applicable tax is shown at checkout. Single-reader ebook terms; companion code is MIT licensed.
TRY THE FREE SAMPLE
See how the guide teaches.
Read 6 sample pages to explore the introduction, first chapter and teaching approach before you choose the complete edition.
Read free sample PDF · No checkout required
WHAT YOU WILL LEARN
Understand each part.
Inspect every boundary.
Count honestly
Distinguish selected-record shares from customer prevalence.
Plan the next test
Convert a bounded finding into an owned investigation.
INSIDE THE BOOK
9 focused chapters.
- Define a decision before collecting comments
- Build an evidence register
- Separate observations from interpretations
- Count records without inventing a population
- Compare channels and competing explanations
- Write a brief with explicit limits
- Turn findings into a small test
- Run the companion evidence audit
- Maintain the brief and close the learning loop
Your download includes
- 21-page tagged PDF with linked contents
- Reflowable EPUB for ebook readers
- Local Python project, fictional input, recorded expected JSON output and 10 companion tests
- 3 explanatory diagrams
- 9 chapters with exercises and worked answers
Before you begin
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Learning prerequisites: Basic spreadsheet arithmetic and familiarity with the topic; Python 3.10+ and a terminal for the optional companion project.
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review is pending.
The included examples are synthetic teaching material. Model access, software subscriptions, hosting, ongoing updates and consulting are not included.
CLEAR EXPECTATIONS
Before you buy.
What will I build?
Build a defensible feedback brief with evidence IDs, selection limits, competing explanations and a repeatable Python audit. The local companion uses fictional teaching fixtures. Project limits: Selected feedback is not a representative customer sample. Manual theme assignments are inputs, not verified machine classifications. No causal inference or sentiment prediction is performed.
What was tested?
The 10 companion tests passed on the packaged project. The supplied fictional fixture produced its recorded expected JSON output. EPUBCheck reported no errors or warnings; DAISY Ace and PDF/UA automated checks passed. These local teaching checks are not production accuracy claims or certification. Independent human technical, editorial and assistive-technology reader review remains pending.
How do I get the files?
Complete the secure Lemon Squeezy checkout to access the PDF, EPUB and companion ZIP. Keep your receipt and download access private. This is a one-time purchase of edition 1.0.0; ongoing updates and consulting are not included.
Can I share the book or the companion code?
The full ebook is licensed to one reader. The labelled free sample may be shared unchanged. The companion project is MIT licensed and may be adapted or shared with its licence notice. See the exact ebook terms.
Trace observations, count selected records, and turn themes into testable decisions
One book. A complete learning project. Evidence you can inspect.
AI-assisted · Independent human review pending
$2.99USDBuy ebook