WEEK-1 | 24-07-25

Hey everyone! Yesterday marked my first full day on this Amazon externship through Extern, and wow - what a day it was. Let me share what I learned and the exciting challenge I've been given.

Understanding Amazon FC’s Challenges

Yesterday, I got my first real look at what Amazon Fulfillment Center is dealing with. It's not just about selling products online - there are some serious problems they need to solve, and that's where I come in as a business analyst.

I was introduced to Prezi, which is this amazing presentation tool with really cool animations. It's going to help me present my findings in a way that actually engages people instead of boring them with plain slides.

More Complex Than You Think

I watched fascinating video showing how Amazon handles orders from the moment you click "buy" on Amazon.com until it reaches your doorstep.

Here's what blew my mind:

• It looks simple on the surface, but there's so much happening behind the scenes

• A few years ago, when Amazon got an order, workers in the Fulfillment Center had to walk around and find each product on the shelves

• Now? The shelves come to them! They use robotic movable racks that bring the products directly to the employees

• This saves tons of time and makes the whole process much more efficient

People Are Leaving

Despite all these advanced features and technology, Amazon faces a serious issue: employee turnover.

Here's the situation:

• Many new employees are leaving the company

• Most quit before they even complete three months

• This is costing Amazon a lot of money in hiring and training new people over and over again

Find Out Why

As a business analyst (or as I like to call it, a data investigator), my job is to figure out why this is happening. I'll be working with real employee feedback data from people who work in Amazon's Fulfillment Centers.

The main problems I need to solve:

• High turnover - too many people quitting

• Employee burnout - people getting exhausted and stressed

• Lack of clarity - employees not understanding their roles or expectations

Continues…

WEEK-1 | 27-07-25

They Quit Silently. I Learned to Catch It Early.

This week, we moved from understanding Amazon Fulfillment Center operations to identifying one of its most critical problems - “attrition”.

The challenge wasn’t just understanding why people leave, but learning to spot the signals before they do.

The Real Problem

Attrition at Amazon Fulfillment Centers is not a hidden issue. In past reports, Amazon’s FCs have seen an annual turnover rate of up to 150% among hourly workers. Many employees leave within months of joining, resulting in rising hiring costs and declining operational efficiency.

But the departure doesn’t happen overnight. The signs are visible, if you know where to look.

What I Learned

In this module, we explored the early warning signs of attrition through three lenses:

1. Productivity shifts
   Delayed tasks, more frequent errors, and withdrawal from high-impact responsibilities

2. Burnout indicators
   Emotional fatigue, increased absenteeism, and visible frustration

3. Lack of belonging
   Silence in meetings, disengagement from team life, and unclear future vision

4. Other red flags
   Avoiding feedback, skipping team events, reluctance to take on growth roles

These aren’t just behavioral shifts—they are data points in predictive attrition models used by companies like Experian and Netflix.

Applying It to Real Reviews

We analyzed three real employee reviews and used people analytics frameworks to determine which employee was most at risk of leaving.

My assessment pointed to Employee 2.

The reasoning was clear:

• Constant pressure, with no space for recovery

• Lack of trust in HR and disorganized management

• Extremely low scores on job satisfaction metrics

• A final note that directly hinted they wouldn’t stay long

This is a textbook example of disengagement that precedes physical exit. The employee had already mentally disconnected from the role.

Business Impact

If such signals go unnoticed, companies face:

• Operational delays, especially when problem solvers exit

• Morale dips, as attrition often spreads through teams

• Increased rehiring costs, along with productivity loss during onboarding

My Takeaways

Attrition is not just an HR problem. It’s an operational risk. Managers who pay attention to behavior patterns, especially around stress, silence, and stagnation, can intervene before performance drops or people leave.

This week’s challenge taught me how to apply people analytics to real-world decisions. It also made me realize that listening to employees isn’t just about what they say, but what they show, often quietly.

Continues…

WEEK-2 | 30-07-2025

What a Negative Review Taught Me About Amazon Fulfillment Center Culture: A STAR + IA Analysis

As part of the Externship, I’m exploring how employee experience, feedback, shape workplace culture.

I want to use a real-world negative review from a Fulfillment Center (FC) employee and analyze it using the STAR + IA framework—a method we’ve been applying during this externship to derive patterns, insights, and recommendations from employee sentiment.

STAR + IA: Framework for Thinking Like a Business Analyst Here’s the breakdown:

S – Situation: What’s going on?

T – Task: What’s expected from the associate or company?

A – Action/Issue: What action was taken or not taken?

R – Result: What happened as a result?

I – Insight: What bigger understanding did we uncover?

A – Actionable suggestion: What should the company do next?

The Review in Focus

Title: Avoid working here at all cost

Role: Picker

Location: Saint Peters, MO Experience: Less than 1 year

Pros: Always hours available, anytime pay

Cons: Long hours, minimal light, will write you up for bathroom breaks, mandatory overtime with little to no heads-up. This place drains you emotionally and physically.

STAR + IA Breakdown:

S – Situation

A Picker at an Amazon Fulfillment Center expressed frustration and burnout after less than one year on the job.

T – Task

The associate expected fair working conditions: reasonable hours, safe facilities, and humane treatment (like being able to take bathroom breaks).

A – Action

They worked through mandatory overtime under poor lighting, were penalized for basic needs like restroom use, and felt blindsided by unpredictable scheduling.

R – Result

The associate is emotionally and physically exhausted - and explicitly advises others to avoid the job entirely. This is not just disengagement - it’s damage to the brand’s reputation.

I – Insight

While “hours available” and “anytime pay” seem like benefits, the conditions behind them are harming employee well-being. This is a clear indicator of systemic burnout, not isolated dissatisfaction.

Such reviews are hard to act on because they point toward cultural and operational gaps, not just isolated issues. You can’t fix this with a one-off policy.

A – Actionable Suggestion Amazon could:

• Audit environmental conditions (e.g., lighting, fatigue, hydration access)

• Implement protected break periods

• Introduce predictable scheduling windows (at least 24–48 hrs ahead)

• Provide emotional wellness touchpoints for shift workers

This kind of structured response doesn’t just describe the problem — it proposes a realistic pathway forward.

And the best part?

I learned how to do this using Claude as a practice partner.

Want to Practice This Yourself?

Here’s the step-by-step method I used to improve:

Step 1: Try:

Write about a workplace experience where someone felt unheard or undervalued. Use STAR + IA to explain the situation and suggest change.

Step 2: Ask Claude for a Practice Scenario Prompt:

Give me a realistic workplace scenario where an employee feels drained or unheard. Then help me apply the STAR + IA framework to it.

Step 3: Rewrite in Your Words Use this to get better with analysis and expression:

Here’s my version of the STAR + IA response. Can you suggest improvements and make it more impactful for a workplace leadership panel?

Step 4: Reflect Ask yourself:

• Is my Insight deep enough?

• Is the Action realistic for the company?

• Would this make someone feel heard?

This exercise reinforced for me how critical feedback analysis is, especially in large-scale operations.

Continues..

WEEK 2 & WEEK 3 | 11-08-2025

Slow progress, steady learning, and a reality check from my SME

This week, I worked mainly on Project 2, which involved several steps:

• Step 1: Understanding what Employee Voice Data is - learning how Amazon Fulfillment Centers work, why employee feedback matters, and how different roles contribute to operations.

• Step 2: Learning the basics of Python - I spent quite some time practicing Python in Google Colab. I learned about data structures, control flow, functions, and running code on Colab.

• Step 3: Cleaning up Glassdoor reviews using Python - applying my Python skills to organize messy employee feedback into structured data using Pandas.

  

• Step 4: Collecting and cleaning YouTube reviews - finding real videos from Amazon workers, ethically extracting transcripts, and turning those into usable data.

I initially tried to learn Python fully before moving ahead, but my SME told me, “You’ll just go into tutorial hell. Rather learn as you go and while working on the project.”

That advice really made sense. I realized that trying to finish all tutorials first was slowing me down. So now, I’m focusing on doing the tasks, learning Python only when I need to.

Even though I took more time than planned, I’m happy with the progress. Slow moving is better than no moving. Tomorrow, I’ll start Project 3: finding sentiment and keyword patterns in the employee voice data.

Excited to keep learning and improving!

Continues..