logoChat01.ai

Hackathon Challenge: Decode the Pulse of E-Commerc...

作成日: 2025年7月19日

使用モデル O3 by Chat01

質問

final_ecommerce_customer_experience_updated.xlsx

Hackathon Challenge: Decode the Pulse of E-Commerce
Storyline:
In the dynamic world of e-commerce, customer expectations are evolving rapidly. Businesses
struggle to keep up with what customers want, how they feel, and when they might leave. Your
mission is to build an Intelligent Customer Experience Platform—a unified system that listens,
learns, and responds to customer behavior in real time.
You are the data detectives, the experienced architects, and the insight engineers. Your goal is to
empower business teams with a smart dashboard that transforms raw data into actionable
intelligence—without relying on large language models or generative AI.
Challenge Objective:
Design and build a backend dashboard that integrates three core capabilities:

  1. Customer Churn Prediction
  2. Customer Feedback Analysis
  3. QA Assistant
    All insights must be delivered through a centralized dashboard that supports real-time decisionmaking.
    Problem Statements
  • Predictive Intelligence
    Identify customers at risk of churn and recommend pricing strategies.
    • Use historical transaction and behavior data.
    • Apply any traditional learning techniques to generate churn risk scores.
  • Feedback Decoder
    Understand and summarize customer feedback on products they purchased.
    • Analyze customer reviews and support messages.
    • Extract sentiment and key topics using traditional methods.
  • QA assistant
    QA assistant to query through customer records without LLMs and Gen AI Concepts.
    Final Deliverable: Smart Backend Dashboard
    Your dashboard should:
    • Display churn risk and pricing insights.
    • Summarize customer feedback with sentiment indicators.
    • Include alerts, trend charts, exportable reports with QA chatbots.
    firstly tell me how to design the model for this and explain every possible
    explain step by step to design the model and my data is in excel sheet and we can use hugging face transformers to solve this

Python

python
file_path = '/mnt/data/final_ecommerce_customer_experience_updated.xlsx'
os.path.exists(file_path)

True

Python

python
xls = pd.ExcelFile(file_path)
xls.sheet_names

['Sheet1']

Python

python
df = pd.read_excel(xls, 'Sheet1')
df.head()

  Unnamed: 0  age  gender      geo location  purchase_count  \
0   CUST0152   67    Male     Lake Brittany              22   
1   CUST0742   25    Male  Port Cynthialand              33   
2   CUST0851   70    Male        Lyonshaven              17   
3   CUST0083   42    Male         Laurafort              35   
4   CUST0759   70  Female      North Judith              39   

   last_purchase_date churn_label product_id  product_title product_category  \
0 2025-03-23 14:02:00          No   PROD0434  Exactly Shirt           Beauty   
1 2024-12-12 19:19:00          No   PROD0284   Space Gadget             Home   
2 2025-06-15 09:28:00          No   PROD0164     Join Cream             Home   
3 2024-09-17 12:58:00          No   PROD0445  Military Ball           Beauty   
4 2024-10-29 07:17:00          No   PROD0262   Quickly Ball          Fashion   

   base_price  quantity purchase_date  total_spend  \
0      391.67         4    2024-10-07      1566.68   
1      367.87         1    2024-09-11       367.87   
2      275.92         3    2024-08-12       827.76   
3      279.26         4    2023-12-19      1117.04   
4      414.01         4    2023-11-14      1656.04   

                                       user_feedback delivery_rating  \
0  The beauty product met expectations and was we...        Positive   
1  The item arrived damaged. Received a wrong ite...   Very Positive   
2  The home item was functional and matched the d...   Very Positive   
3  Would definitely recommend this product. The r...         Neutral   
4  Excellent service and fast delivery. The retur...        Positive   

   product_quality_rating  support_rating  discount_percentage  pricing_score  
0                     NaN             5.0                   10       1410.012  
1                     2.0             5.0                    0        367.870  
2                     2.0             1.0                   25        620.820  
3                     3.0             1.0                   15        949.484  
4                     2.0             4.0                    0       1656.040

Python

python
# See shape and columns
df.shape

(3000, 20)

Python

python
df.columns

Index(['Unnamed: 0', 'age', 'gender', 'geo location', 'purchase_count',
       'last_purchase_date', 'churn_label', 'product_id', 'product_title',
       'product_category', 'base_price', 'quantity', 'purchase_date',
       'total_spend', 'user_feedback', 'delivery_rating',
       'product_quality_rating', 'support_rating', 'discount_percentage',
       'pricing_score'],
      dtype='object')

Python

python
df.dtypes

Unnamed: 0                        object
age                                int64
gender                            object
geo location                      object
purchase_count                     int64
last_purchase_date        datetime64[ns]
churn_label                       object
product_id                        object
product_title                     object
product_category                  object
base_price                       float64
quantity                           int64
purchase_date             datetime64[ns]
total_spend                      float64
user_feedback                     object
delivery_rating                   object
product_quality_rating           float64
support_rating                   float64
discount_percentage                int64
pricing_score                    float64
dtype: object

思考

Designing churn prediction model

The user wants a step-by-step guide for building a churn model, feedback analysis model, and QA assistant using transformers (no generative methods). Starting with data preprocessing—handling duplicates and missing values, then encoding categories. For churn prediction, we’d use binary classification with features like recency, frequency, monetary (RFM), and others. We'll select models like logistic regression or gradient boosting (XGBoost, LightGBM) and evaluate with metrics like AUC-ROC. For pricing strategies, we can apply uplift modeling.

Explaining model steps

For the feedback analysis, we can use sentiment analysis with pre-trained models

Thought for 55 seconds

回答

Below is a step-by-step blueprint you can follow to turn the Excel data you just shared into an Intelligent Customer Experience Platform with the three required capabilities—churn prediction, feedback analysis, and a lightweight QA assistant—while staying strictly within classic ML + Transformer encoders (no generative LLMs).

1 Ingest & Organize the Data

What you haveWhy it mattersImmediate actions
Excel sheet (final_ecommerce_customer_experience_updated.xlsx)<br>3000 rows × 20 columns (customers, transactions, feedback, ratings, discounts, etc.)One flat file that mixes customer fields (e.g. age, geo), order fields (e.g. product_id, quantity) and text feedback.1. Load into a staging table (e.g. Postgres or DuckDB).<br>2. Create three logical tables/views:<br>  • customers (1 row per customer_id)<br>  • orders (1 row per order_id)<br>  • feedback (1 row per feedback_id)<br>3. Convert all date strings to datetime; drop the unnamed index column.

(You can automate the split with a tiny ETL script; this keeps each model’s features tidy.)

2 Customer Churn Prediction

2.1 Feature Engineering

GroupFeatures to derive (examples)
RFM-stylerecency = days_since(last_purchase_date)<br>frequency = purchase_count / customer_lifetime_months<br>monetary = total_spend / customer_lifetime_months
Engagementavg quantity per order, number_of_products_bought, share_of_discounted_orders
Price Sensitivitymean(discount_percentage), mean(pricing_score)
Satisfactionmean(delivery_rating), mean(product_quality_rating), mean(support_rating)
Demographics (encoded)one-hot gender, age bucket, geo cluster

2.2 Model options

ModelWhy it fitsNotes
Gradient Boosting (XGBoost / LightGBM)Handles mixed feature types, small-to-medium data, interpretable via SHAPBaseline workhorse
TabTransformer (Hugging Face tabtransformer-base)Pure-encoder transformer for tabular data—still non-generative. Learns contextual embeddings across categorical columns.Good hackathon differentiator; fine-tune for 5–10 epochs.
Logistic RegressionSimple benchmarkSets a lower bound for lift

Process: stratified train/validation split (e.g. 70/30), SMOTE or class-weighting if churn_label is imbalanced, optimize AUC-ROC & F-β (β>1 to favor recall).

2.3 Turning risk into action

  1. Score every active customer nightly (or on new events).
  2. Pricing recommender:
    For high-risk customers → simulate individualized discount uplift:
uplift(d)=P(purchase again∣d)−P(purchase again∣d=0)\text{uplift}(d) = P(\text{purchase again} \mid d) - P(\text{purchase again} \mid d {=}0)

Use causal uplift trees or CausalML’s UpliftRandomForestClassifier. Output recommended discount tiers, then surface in the dashboard.

3 Feedback Decoder

3.1 Sentiment Classification

ApproachTools
Transformer encoder fine-tuneStart with distilbert-base-uncased-finetuned-sst-2-english (binary) or roberta-base-go_emotions (multi-class).<br>Fine-tune on your own labeled subset (if any).
Rule/lexicon fallbackVADER or TextBlob for low-resource languages or cold start

Store a sentiment_score ∈ [-1,1] and sentiment_label per feedback row.

3.2 Topic & Key-phrase Extraction

  1. BERTopic (sentence-transformer embeddings + HDBSCAN + c-TF-IDF).
  2. Output: topic_id, keywords, representative feedback excerpts.
  3. Smooth counts with exponential moving average to enable trend charts.

(No generative summarization: instead show “Top n key phrases” & “Most representative sentences” for each topic with a TextRank extractive method.)

4 QA Assistant (No LLM, No Generation)

Goal 📌: Let ops teams ask “Which customers in California bought ‘Exactly Shirt’ last month and contacted support twice?” without writing SQL.

4.1 Tech stack

LayerToolingRationale
Vector indexFAISS or pgvectorSearch by semantic similarity
Embedding modelall-MiniLM-L6-v2 (sentence-transformers)Tiny, fast, encoder-only
Keyword/BM25 indexElasticSearch / OpenSearchExact filters & fast metadata facets
RouterSimple rules: if query contains “count / how many / average…” send to analytic SQL; else combine BM25 + vector search.

Return a pre-formatted answer card:
• Relevant records (top k rows)
• Aggregates (e.g. total_spend) when patterns like “sum / total / average” detected
(No text generation—just assembled facts.)

5 Serving & Real-Time Architecture

mermaid
graph TD
  subgraph Batch & Streaming
    Excel-->|ETL|Staging[(DB)]
    Orders-->FeatEng
    Customers-->FeatEng
    FeatEng-->|Features|ChurnModel
    Feedback-->|BERT|Sentiment
    Feedback-->|BERTopic|Topics
  end
  FeatEng-->Scores[(Churn_Scores)]
  Sentiment-->NLP_DB[(Feedback_Insights)]
  Topics-->NLP_DB
  subgraph API Layer (FastAPI)
    Scores & NLP_DB & VectorIndex --> API
  end
  API-->Dashboard[(React / Streamlit)]
  Kafka/EventHub --> FeatEng  %% for real-time adds

Batch jobs refresh predictions daily; streaming micro-batch (e.g. every 5 min) can update a single customer right after an event.

6 Dashboard Sketch (Backend-focused)

SectionAPI EndpointData source
Churn Heatmap/churn/top_risk?limit=100Scores table
Price Recommendations/pricing/discount_suggestions?customer_id=…Uplift model outputs
Sentiment Trends/feedback/trends?period=30dNLP_DB
Topic Drill-down/feedback/topic/{topic_id}NLP_DB, raw feedback
QA Search/qa/query?q=…Vector + BM25 + SQL engine

Return JSON; the front-end can render with Plotly/Chart.js and trigger CSV exports.

7 Evaluation & Monitoring

CapabilityOffline metricOnline metric
ChurnAUC-ROC, PRAUC, recall@kNet revenue saved per campaign, uplift vs. control
SentimentF1, MCCCorrelation with CSAT / support tickets
TopicsTopic coherenceAnalyst feedback acceptance rate
QAPrecision@5 (manual audit)Query success rate, latency

Set up MLflow or Weights & Biases for experiment tracking, plus Prometheus/Grafana for live health (latency, missing features, drift).

8 Implementation Timeline (Hackathon-style)

DayMilestone
1Ingest Excel ➜ clean schema ➜ quick EDA
2Build churn feature pipeline ➜ baseline XGBoost model
3Fine-tune DistilBERT for sentiment ➜ run BERTopic
4Stand up vector+BM25 QA service ➜ API skeleton
5Wire APIs into minimal dashboard ➜ iterate on UX
6Add uplift pricing module ➜ evaluate & polish charts
7Prepare demo dataset ➜ write readme & deployment scripts

Key Takeaways

  • Traditional ML + encoder-only transformers give you modern accuracy without violating the “no LLM/Gen-AI” constraint.
  • Keep each capability in its own micro-service but expose one unified REST/GraphQL API so the dashboard stays simple.
  • Focus on actionability, not just analytics—surface who to contact, what discount to offer, and why the customer is unhappy.

You now have a clear blueprint to start building. Let me know if you want code snippets, sample notebooks, or deeper dives into any step.

このQ&Aを共有