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E-commerce Performance & Customer Experience Analysis

Executive Summary

This report provides a comprehensive analysis of the company's Q4 performance, revealing a critical disconnect between sales acquisition and post-purchase customer experience. While lead conversion and top-performer sales show strength, these gains are being nullified by systemic issues in logistics and fulfillment.

  • The Core Problem: A significant 21.4% decline in Customer Lifetime Value (CLV) and a 21.38% drop in total sales are directly linked to a deteriorating delivery experience. Despite an improvement in average delivery speed, a decrease in on-time reliability and a spike in delays in key regions have led to a 4.72% drop in average review scores and a catastrophic 95.6% churn rate.

  • The Path Forward: Our strategy must pivot from aggressive acquisition to aggressive retention. This report outlines pointed recommendations focused on three pillars:

  • Fixing the Fulfillment Engine: Overhauling logistics to prioritize reliability over raw average speed.

  • Activating Customer Retention: Implementing targeted programs to re-engage at-risk customers and reward loyalty.

  • Capitalizing on Bright Spots: Replicating the success of top sellers and products to de-risk the business.

Key Performance Indicators (KPIs) - The Story in Numbers

Customer & Retention Metrics

  • Customer Lifetime Value (CLV): $191.74 (🔻 -21.4% vs. last quarter). A major decline, indicating we are extracting significantly less value from each customer.

  • Customer Retention Rate: 4.4% (🔼 +6.9% vs. last quarter). While showing a slight improvement, a rate this low is unsustainable and indicates a fundamental "leaky bucket" problem.

  • Customer Churn Rate: 95.6% (🔽 -0.3% vs. last quarter). The marginal improvement is statistically insignificant. A 95.6% churn rate is an existential threat to the business model.

Sales & Revenue Metrics

  • Total Sales: $1.61M (🔻 -21.38% vs. last quarter). Aligns with the drop in CLV, confirming a broad-based revenue decline.

  • Top 5 Products (by Avg. Revenue): Generated $187.07k in revenue (🔼 +145.33% vs. last quarter). This highlights a heavy reliance on a few high-ticket categories (Computers, Agro, Small Appliances).

  • Top 5 Sellers: Generated $151.85k in revenue (🔼 +4891% vs. last quarter). This astronomical growth points to a few hyper-performing sellers carrying a disproportionate amount of the business.

Funnel & Operations Metrics

  • Lead Conversion Rate: 100%. This metric is likely flawed or mis-defined. A 100% conversion rate is improbable and suggests leads are only being tracked after the point of conversion. This needs investigation.

  • Average Time to Close: 20 days. A baseline metric to monitor as we adjust marketing and sales strategies.

  • Average Delivery Time: 12 days (Improved by 4 days). A positive "vanity metric" that masks the real problem.

  • On-Time Delivery (OTD) Rate: 92.27% (🔻 -2.66% vs. last quarter). This is a critical failure. We are faster on average but less reliable, which breaks customer trust.

  • Average Delivery Delay: 0.6 days (Worsened by 0.4 days). This increase, combined with the OTD drop, is the root of the satisfaction problem.

  • Top 5 Cities Delivery Delay: Averaged 8 days (🔼 +202.5% vs. last quarter). A massive operational failure in specific geographies is severely damaging our brand reputation there.

Customer Satisfaction Metrics

  • Average Review Score: 4.1 (🔻 -4.72% vs. last quarter). A significant drop, moving from a "Good" to a "Mediocre" rating in the eyes of the customer.

  • Correlation (Delivery Delay vs. Review Score): -0.29 (Strengthened negativity by -119%). This is the smoking gun. The statistical link between late deliveries and poor reviews has more than doubled in strength this quarter.

Recommendations Based on Findings

1. Declare War on Delivery Delays (Highest Priority)

  • The data is unequivocal: logistics failures are the primary driver of customer dissatisfaction and churn.
  • Action: Immediately segment logistics partners by performance in the top 5 worst-performing cities (starting with Formosa). Renegotiate SLAs to include penalties for missing OTD targets, not just average speed. Explore partnerships with local, more reliable couriers in these problem areas.
  • Action: Implement a proactive communication system. If a delivery is projected to be late, automatically notify the customer with a sincere apology and a small store credit for their next purchase. This turns a negative experience into a retention opportunity.
  • Action: Publicly feature an "On-Time & Reliable" badge on product pages for items fulfilled through our top-performing logistics partners. Use this as a selling point.

2. Launch a Targeted Customer Retention & Win-Back Program

  • With 95.6% churn, acquisition is inefficient. We must plug the leak.

  • Action: Use the predictive model to identify customers who have received a delayed order but have not yet churned. Target them immediately with a "We're Sorry & We're Improving" campaign, offering a compelling discount to encourage a second purchase under our improved logistics.

  • Action: Create a loyalty program for customers who purchase from our high-satisfaction product categories (Fashion, Flowers, Books), as they are our happiest cohort. Encourage them to become brand advocates.

  • Action: For high-CLV customers who have churned, initiate a personal outreach from a customer success manager to understand their issues and offer a significant incentive to return.

3. Diversify and Replicate Success to De-risk the Business

  • Over-reliance on a few sellers and products is a major risk.
  • Action: Conduct a qualitative analysis of the top 5 sellers. What are they doing differently? (Product selection, customer communication, marketing). Codify their successful strategies into a training program for all other sellers.
  • Action: Cross-promote high-satisfaction products (e.g., Flowers, Fashion) to customers who have purchased high-revenue products (e.g., Computers, Appliances). This can increase overall account satisfaction and introduce customers to a better product experience.

A/B Testing Opportunities

  • To validate our recommendations with data, we should implement the following tests:

A. Funnel/Experience Level

  • Hypothesis: Proactively notifying customers about a delivery delay with a $5 store credit will reduce the negative impact on their subsequent review score and increase the probability of a second purchase compared to not notifying them.
  • Control Group (A): Customers with a delayed order receive no special communication.
  • Test Group (B): Customers with a delayed order receive an automated email and SMS with an apology and a $5 coupon code.
  • Primary Metric: Average review score from these customers.
  • Secondary Metric: 30-day repeat purchase rate.

B. Product Level

  • Hypothesis: Displaying a "Fast & Reliable Delivery" badge on products fulfilled by our most dependable logistics partners will increase the conversion rate for those products.
  • Control Group (A): Product pages are displayed as they are now.
  • Test Group (B): On eligible product pages, a visually appealing badge is shown near the "Add to Cart" button.
  • Primary Metric: Add-to-cart rate and final conversion rate for badged products.

C. Marketing/Retention Level

  • Hypothesis: A targeted win-back email campaign for churned customers who experienced a delivery delay is more effective than a generic win-back campaign.

  • Control Group (A): Churned customers receive a standard "We Miss You" email with a 10% discount.

  • Test Group (B): Churned customers who previously had a late order receive a targeted "We Messed Up. Give Us Another Chance" email that acknowledges the past issue and offers a more aggressive 25% discount.

  • Primary Metric: Campaign conversion rate (number of churned customers who make a new purchase).

  • Tools Used: Python (Data Cleaning, Preprocessing, ML Modeling), Tableau (Visualization & Dashboarding)

KPI DASHBOARD

About

This project analyzes e-commerce sales, customer behavior, and logistics performance to uncover the key factors affecting customer lifetime value (CLV), churn, and revenue. Using Python for data analysis and Tableau for visualization, it transforms raw business data into actionable insights through interactive dashboards and KPI tracking.

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