Case Study: Fintech Startup 0 → $50M Revenue với Data-Driven Growth

TL;DR

Company: FinX (tên giả, fintech startup Việt Nam)

Product: Buy Now Pay Later (BNPL) + Digital Lending platform

Timeline: 2019-2024 (5 năm)

Growth Journey:

• Year 0 (2019): $0 revenue, 3 founders
• Year 1 (2020): $500K revenue, product-market fit
• Year 2 (2021): $5M revenue, Series A ($10M)
• Year 3 (2022): $15M revenue, profitability
• Year 4 (2023): $30M revenue, Series B ($30M)
• Year 5 (2024): $50M revenue, market leader in segment

Key Success Factors:

1. Data-First Mindset: Tracking everything từ ngày đầu tiên
2. Rapid Experimentation: 100+ A/B tests/năm
3. ML-Powered Credit Scoring: Approve underserved segments với low default rate
4. Analytics-Driven Product: Build features dựa trên data, không phải opinions
5. Cohort-Based Growth: Hiểu deeply từng customer cohort

Metrics that Mattered:

• Approval Rate: 30% (Year 1) → 65% (Year 5)
• Default Rate: 8% → 3% (nhờ better credit scoring)
• CAC: $50 → $15 (optimization)
• LTV/CAC: 1.5x → 6x
• NPS: 45 → 72

Tech Stack: PostgreSQL → BigQuery + dbt + Looker, Airflow, Python ML models, Feature Store, A/B testing platform

Note: Company và metrics được anonymized, nhưng pattern và lessons learned là thật từ multiple fintech case studies.

Background: The Problem & Opportunity

Market Opportunity (2019)

Vietnam Fintech Landscape:

• 60M+ adults, chỉ 30% có tài khoản ngân hàng
• Credit gap: 75% population không có credit history
• E-commerce boom: GMV tăng 40%/năm
• Smartphone penetration: 70% và tăng

Customer Pain Points:

• Muốn mua sản phẩm trị giá 5-10M VND (điện thoại, laptop, xe máy)
• Không đủ tiền cash một lần
• Ngân hàng từ chối (no credit history, income không chứng minh được)
• Credit card approval rate <5% cho demographic này

Opportunity: BNPL (Buy Now Pay Later) cho mass market

Founding Team

3 co-founders:

• CEO: Ex-banker, hiểu credit risk
• CTO: Ex-tech company, data engineering background
• CPO: Ex-e-commerce, product sense

Key Insight: "Data sẽ là competitive advantage của chúng ta. Traditional banks dùng credit bureau scores (mà 75% dân số không có). Chúng ta sẽ dùng alternative data + ML."

Initial Product (2019)

BNPL Partnership:

• Partner với e-commerce sites (electronics, home appliances)
• Checkout flow: "Pay in 3 months, 0% interest"
• FinX underwrites risk, merchant gets paid immediately

Target Customer:

• Age: 22-35
• Income: 5-15M VND/tháng
• No formal credit history
• Smartphone users

Year 0-1 (2019-2020): Finding Product-Market Fit

Q1 2019: Launch MVP

MVP Features:

• Application form: Phone number, name, ID photo, selfie
• Instant decision (< 1 minute)
• Loan amount: 2-10M VND
• 3-month installment

Tech Stack:

• Backend: Django (Python)
• Database: PostgreSQL
• ML Model: Simple logistic regression (credit scoring)
• Hosting: AWS EC2

Credit Scoring Model v1 (very basic):

# Year 0 model: Simple rule-based + basic ML
import pandas as pd
from sklearn.linear_model import LogisticRegression

features = [
    'age',
    'loan_amount',
    'phone_number_age',  # How long they've had this number
    'device_model',  # iPhone vs Android budget phone
    'application_hour',  # Time of day
]

# Training data: Only 500 approved loans (seed data from founders' network)
X_train = df[features]
y_train = df['defaulted']  # 1 = default, 0 = paid back

model = LogisticRegression()
model.fit(X_train, y_train)

# Predict
risk_score = model.predict_proba(X_new)[1]

if risk_score < 0.1:
    decision = "APPROVED"
    credit_limit = 10_000_000  # 10M VND
elif risk_score < 0.3:
    decision = "APPROVED"
    credit_limit = 5_000_000  # 5M VND
else:
    decision = "REJECTED"

Initial Results (first 3 months):

• Applications: 2,000
• Approval Rate: 30% (very conservative)
• Approved Loans: 600
• Avg Loan Size: 6M VND
• GMV: $200K
• Default Rate: 12% (high, but expected for new model)

Problem: Approval rate quá thấp → Bỏ lỡ nhiều good customers.

Q2-Q4 2019: Iteration & Learning

Data Infrastructure Setup:

Founders nhận ra: "Chúng ta cần track EVERYTHING để improve model."

Event Tracking:

# Track every user action
events = [
    'application_started',
    'id_photo_uploaded',
    'selfie_uploaded',
    'application_submitted',
    'application_approved',
    'application_rejected',
    'loan_disbursed',
    'payment_made',
    'payment_missed',
    'payment_defaulted',
]

# Snowplow-style event tracking
def track_event(user_id, event_type, properties):
    event = {
        'user_id': user_id,
        'event_type': event_type,
        'timestamp': datetime.utcnow(),
        'properties': properties,
        'device': request.user_agent,
        'ip_address': request.remote_addr,
        # ... more context
    }
    db.insert('events', event)

Cohort Analysis:

-- Cohort analysis: Default rate by approval month
WITH cohorts AS (
  SELECT
    user_id,
    DATE_TRUNC('month', approved_at) AS cohort_month
  FROM loans
  WHERE status = 'approved'
),

cohort_defaults AS (
  SELECT
    c.cohort_month,
    COUNT(DISTINCT l.user_id) AS total_loans,
    SUM(CASE WHEN l.status = 'defaulted' THEN 1 ELSE 0 END) AS defaults,
    AVG(CASE WHEN l.status = 'defaulted' THEN 1.0 ELSE 0.0 END) AS default_rate
  FROM cohorts c
  JOIN loans l ON c.user_id = l.user_id
  GROUP BY c.cohort_month
)

SELECT * FROM cohort_defaults
ORDER BY cohort_month;

Insight: October 2019 cohort có default rate 15% vs September chỉ 8%. Tại sao?

Root Cause: Changed approval threshold too aggressively → Approved riskier customers.

Action: Roll back threshold, re-train model.

Key Experiments (Year 1)

Experiment 1: Instant Approval vs Manual Review

• Hypothesis: Instant approval tăng conversion, nhưng có thể tăng default
• Design: A/B test
  • Control (A): Instant approval for score > 0.7, manual review for 0.5-0.7
  • Variant (B): Instant approval for all score > 0.5
• Results:
  • Variant B: Approval rate +15pp (45% vs 30%)
  • Default rate: +2pp (10% vs 8%)
  • Net revenue: +25% (worth it!)
• Decision: Ship Variant B

Experiment 2: Loan Amount Limits

• Hypothesis: Lower loan amounts → Lower default risk
• Segments:
  • New users: Max 5M VND
  • Repeat users (paid on time): Max 15M VND
• Results: Default rate của repeat users chỉ 3% (vs 12% new users)
• Insight: Build loyalty program, incentivize repeat borrowing

Experiment 3: Repayment Frequency

• Test: Monthly vs Bi-weekly payments
• Results: Bi-weekly có lower default rate (6% vs 10%)
• Hypothesis: Smaller, frequent payments easier to manage
• Decision: Offer both, recommend bi-weekly

Year 1 Results (Dec 2020)

Status: Product-market fit tìm được, nhưng growth chậm hơn mong đợi.

Learnings:

1. ✅ Repeat customers là gold (low default, high LTV)
2. ✅ Data quality > Model complexity (garbage in, garbage out)
3. ❌ Need more features (alternative data) để improve approval rate
4. ❌ Need partnerships để scale acquisition

Year 2 (2021): Scale & Fundraising

Series A: $10M (Jan 2021)

Pitch Deck Highlights:

• Traction: 6K loans, 28% repeat rate, 8% default
• Unit Economics: LTV/CAC = 2.5x, path to profitability
• Market Size: $5B+ addressable market
• Data Advantage: Proprietary credit scoring model

Investors: Local VC + Singapore fintech investor

Use of Funds:

• $4M: Marketing & partnerships
• $3M: Tech & data team
• $2M: Risk capital (loan book)
• $1M: Operations

Hiring: Data Team

Q1 2021 Hires:

• Data Engineer (first hire): Build data warehouse
• Data Scientist: Improve ML model
• Analytics Lead: Business insights, dashboards

Data Warehouse Setup

Before: PostgreSQL production DB → Ad-hoc SQL queries

After: Modern data stack

Data Sources:
├── PostgreSQL (transactional: users, loans, payments)
├── Event logs (Kinesis → S3)
├── Third-party APIs (telecom data, e-commerce purchase history)
└── Manual uploads (merchant partnerships)
         ↓
  ETL (Airflow + Python)
         ↓
   Data Lake (S3)
         ↓
  Data Warehouse (BigQuery)
         ↓
  Transformation (dbt)
         ↓
    BI (Looker)

dbt Models:

-- models/marts/loans/loan_performance.sql
{{ config(materialized='table') }}

WITH loan_base AS (
  SELECT * FROM {{ ref('stg_loans') }}
),

payments AS (
  SELECT * FROM {{ ref('stg_payments') }}
),

loan_metrics AS (
  SELECT
    l.loan_id,
    l.user_id,
    l.approved_at,
    l.loan_amount,
    l.tenure_months,
    l.status,
    COUNT(p.payment_id) AS payments_made,
    SUM(p.amount) AS total_paid,
    MAX(p.paid_at) AS last_payment_at,
    CASE
      WHEN l.status = 'defaulted' THEN 1
      ELSE 0
    END AS is_default
  FROM loan_base l
  LEFT JOIN payments p ON l.loan_id = p.loan_id
  GROUP BY l.loan_id, ... (other columns)
)

SELECT * FROM loan_metrics

Dashboards (Looker):

1. Executive Dashboard: Daily revenue, approvals, defaults
2. Risk Dashboard: Default rates by cohort, segment, product
3. Marketing Dashboard: CAC, conversion funnel, channel performance
4. Product Dashboard: Feature usage, repeat rate, NPS

Credit Scoring v2: Alternative Data

Problem: 65% approval rejections do KHÔNG có credit history (chưa chắc đã high risk).

Solution: Alternative data sources

New Features:

# Credit scoring v2: 50+ features
features = [
    # Demographics
    'age', 'city', 'district', 'education_level',

    # Device & Behavior
    'device_model', 'device_age', 'os_version',
    'application_time_of_day',
    'application_completion_time',  # Fast = bot?, Slow = hesitant?

    # Alternative Data (with user permission)
    'telecom_tenure_months',  # How long they've had phone number
    'telecom_monthly_spend',  # High spend = stable income?
    'ecommerce_purchase_count',  # Purchase history
    'ecommerce_avg_order_value',
    'social_media_connections',  # Facebook friends count (proxy for social capital)

    # Loan-specific
    'loan_amount',
    'loan_to_income_ratio',
    'is_repeat_customer',
    'previous_loan_performance',
]

# Model: XGBoost (better than Logistic Regression)
from xgboost import XGBClassifier

model = XGBClassifier(
    max_depth=6,
    n_estimators=100,
    learning_rate=0.1,
    scale_pos_weight=10  # Imbalanced dataset
)

model.fit(X_train, y_train)

Feature Importance:

import matplotlib.pyplot as plt

importance = model.feature_importances_
features_df = pd.DataFrame({
    'feature': features,
    'importance': importance
}).sort_values('importance', ascending=False)

print(features_df.head(10))

# Output:
#                     feature  importance
# previous_loan_performance      0.25
# telecom_tenure_months         0.18
# ecommerce_purchase_count      0.12
# age                           0.10
# loan_amount                   0.08
# ...

Insight: Previous loan performance là predictor mạnh nhất → Focus on repeat customers!

Results: Model v2 vs v1

A/B Test (1 tháng):

Decision: Rollout model v2 to 100%.

Growth Tactics (Year 2)

1. Partnerships:

• Top 20 e-commerce merchants
• BNPL button at checkout
• Co-marketing campaigns

2. Referral Program:

• Refer a friend → Both get 50K VND credit
• Viral coefficient: 0.4 (sustainable growth)

3. SEO & Content:

• Blog: "Mua trả góp 0% lãi suất"
• Rank #1 cho keywords "mua tra gop", "bnpl vietnam"
• Organic traffic: 30% of applications

4. Performance Marketing:

• Facebook Ads: $20 CAC
• Google Ads: $35 CAC
• TikTok Ads: $15 CAC (best performer!)

Year 2 Results (Dec 2021)

Status: Hypergrowth mode, nearing profitability.

Year 3 (2022): Profitability & Expansion

New Product: Digital Lending (Direct Loans)

Rationale: BNPL phụ thuộc vào merchants. Direct loans = full control.

Product:

• Loan amount: 5-50M VND
• Tenure: 3-12 months
• Interest rate: 15-25%/year (competitive vs traditional lenders)
• Use cases: Emergency cash, education, home improvement

Launch Strategy:

• Soft launch to existing customers (proven track record)
• A/B test interest rates, messaging

Experimentation Culture

Platform: Built in-house A/B testing framework

# Experimentation framework
class Experiment:
    def __init__(self, name, variants):
        self.name = name
        self.variants = variants  # ['control', 'variant_a', 'variant_b']

    def assign_variant(self, user_id):
        # Consistent hashing
        hash_val = int(hashlib.md5(f"{user_id}:{self.name}".encode()).hexdigest(), 16)
        variant_idx = hash_val % len(self.variants)
        return self.variants[variant_idx]

# Usage
interest_rate_test = Experiment(
    name='direct_loan_interest_rate',
    variants=['18%', '20%', '22%']
)

user_id = '12345'
variant = interest_rate_test.assign_variant(user_id)

# Show corresponding rate
if variant == '18%':
    interest_rate = 0.18
elif variant == '20%':
    interest_rate = 0.20
else:
    interest_rate = 0.22

Notable Experiments (Year 3):

Experiment: Interest Rate Optimization

• Variants: 18%, 20%, 22%
• Results:
  • 18%: Conversion 25%, Avg loan 15M
  • 20%: Conversion 22%, Avg loan 18M ← Winner (max revenue)
  • 22%: Conversion 18%, Avg loan 20M
• Decision: 20% optimal

Experiment: Loan Tenure

• Control: 6 months
• Variant: 12 months
• Results: 12-month option increased loan size +40%, default rate +2pp (acceptable trade-off)
• Decision: Offer both, default to 6 months

Experiment: Repayment Reminders

• Control: SMS 3 days before due date
• Variant A: SMS 3 days + 1 day before
• Variant B: SMS 3 days + App push notification
• Results: Variant B reduced late payments -30%
• Decision: Ship Variant B

Data Quality & Monitoring

Problem: Model performance degrading over time (model drift).

Solution: Monitoring & alerts

# Monitor model performance
from evidently.dashboard import Dashboard
from evidently.tabs import DataDriftTab, ClassificationPerformanceTab

# Weekly model monitoring
dashboard = Dashboard(tabs=[DataDriftTab(), ClassificationPerformanceTab()])
dashboard.calculate(
    reference_data=train_data,  # Training data
    current_data=production_data_last_week,  # Last week production
    column_mapping=column_mapping
)

dashboard.save('model_monitoring_report.html')

# Alert if drift detected
if dashboard.drift_detected():
    send_alert('Model drift detected! Re-training needed.')

Retrain Cadence: Monthly automated retraining

Cohort-Based Product Development

Analysis: Which cohorts have highest LTV?

WITH user_cohorts AS (
  SELECT
    user_id,
    DATE_TRUNC('month', first_loan_at) AS cohort_month
  FROM users
),

cohort_ltv AS (
  SELECT
    uc.cohort_month,
    COUNT(DISTINCT uc.user_id) AS cohort_size,
    SUM(l.revenue) AS total_revenue,
    AVG(l.revenue) AS avg_ltv
  FROM user_cohorts uc
  JOIN loans l ON uc.user_id = l.user_id
  GROUP BY uc.cohort_month
)

SELECT
  cohort_month,
  cohort_size,
  avg_ltv,
  -- Compare to overall
  avg_ltv / (SELECT AVG(avg_ltv) FROM cohort_ltv) AS ltv_index
FROM cohort_ltv
ORDER BY cohort_month;

Insight:

• High LTV cohorts: Users acquired via referrals, repeat customers from e-commerce
• Low LTV cohorts: Paid ads (Facebook), one-time users

Action:

1. Double down on referral program
2. Optimize Facebook ads targeting (exclude low-intent audiences)
3. Build loyalty program for repeat customers

Year 3 Results (Dec 2022)

Status: Profitable, ready for next phase.

Year 4-5 (2023-2024): Market Leadership

Series B: $30M (Early 2023)

Valuation: $150M

Use of Funds:

• $15M: Marketing & expansion (new cities)
• $8M: Product development (new features, verticals)
• $5M: Data & ML team expansion
• $2M: Risk capital

Advanced ML: Feature Store

Challenge: Feature engineering không consistent giữa training và serving.

Solution: Feature Store (Feast)

# Define features
from feast import Entity, Feature, FeatureView, ValueType
from feast.data_source import BigQuerySource

# Entity: User
user = Entity(
    name="user_id",
    value_type=ValueType.STRING,
    description="User ID"
)

# Feature View: User Telecom Features
user_telecom_features = FeatureView(
    name="user_telecom_features",
    entities=["user_id"],
    features=[
        Feature(name="telecom_tenure_months", dtype=ValueType.INT64),
        Feature(name="telecom_monthly_spend", dtype=ValueType.FLOAT),
    ],
    batch_source=BigQuerySource(
        table_ref="finx_features.user_telecom",
        event_timestamp_column="timestamp",
    ),
)

# Retrieve features (training)
feature_store.get_historical_features(
    entity_df=entity_df,
    feature_refs=["user_telecom_features:telecom_tenure_months", ...]
)

# Retrieve features (serving)
online_features = feature_store.get_online_features(
    feature_refs=["user_telecom_features:telecom_tenure_months", ...],
    entity_rows=[{"user_id": "12345"}]
)

Benefits:

• Consistent features across training & serving
• Easy to add new features
• Feature reuse across models

Credit Scoring v3: Deep Learning

Model: Neural Network (TensorFlow)

import tensorflow as tf

# Define model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu', input_shape=(n_features,)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')  # Binary classification
])

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=['accuracy', tf.keras.metrics.AUC()]
)

# Train
model.fit(
    X_train, y_train,
    validation_data=(X_val, y_val),
    epochs=50,
    batch_size=256,
    callbacks=[early_stopping]
)

Results:

Impact: +13pp approval rate, -2pp default rate → $8M additional revenue/year

Expansion: New Verticals

1. Merchant Financing (B2B):

• Lend to small merchants (sellers trên e-commerce platforms)
• Working capital loans (inventory financing)
• Avg loan size: 100M VND
• Default rate: 4% (lower than consumer)

2. Payroll Advance:

• Partner with employers
• Employees borrow against salary
• Auto-deduct from paycheck
• Default rate: <1% (lowest risk)

Impact: Data Culture

Metrics-Driven Organization:

• Every team has OKRs tied to metrics
• Weekly metric review meetings
• Dashboards accessible to all

Example OKRs (Q1 2024):

Product Team:

• Objective: Increase user engagement
• KR1: Increase repeat loan rate from 35% → 40%
• KR2: Launch loyalty program, 10K users enrolled
• KR3: NPS từ 68 → 72

Risk Team:

• Objective: Optimize credit decisioning
• KR1: Increase approval rate from 60% → 65%
• KR2: Maintain default rate <5%
• KR3: Reduce manual review queue by 30%

Marketing Team:

• Objective: Efficient user acquisition
• KR1: Reduce CAC from $18 → $15
• KR2: Increase organic channel from 30% → 40%
• KR3: Referral program generates 20% of new users

Year 5 Results (Dec 2024)

Status: Market leader in BNPL segment, expanding to adjacent verticals.

Key Learnings & Takeaways

1. Instrument Everything from Day 1

Lesson: Data không thể thu thập hồi tố. Track everything bây giờ, analyze sau.

Action: Setup event tracking, logging infrastructure trong first week.

2. Data Quality > Model Sophistication

Lesson: Model v2 (XGBoost) với good features beat Model v3 (Neural Net) với bad features.

Action: Invest in data validation, cleaning, feature engineering.

3. Experimentation Culture

Lesson: 100+ experiments/năm → 20-30% win, nhưng cumulative effect = 50%+ growth.

Action:

• Build A/B testing framework
• Encourage everyone to experiment
• Learn from failures (60-70% experiments fail, OK!)

4. Cohort Analysis > Overall Metrics

Lesson: Overall default rate 7% che giấu fact that cohort Jan có 12%, cohort Mar chỉ 4%.

Action: Always analyze by cohorts, segments.

5. Balance Growth & Risk

Lesson: Có thể tăng approval rate lên 90%, nhưng default rate sẽ tăng 20% → Net negative.

Action: Optimize for LTV, không phải single metric (approval rate, revenue, ...).

6. Self-Service Analytics

Lesson: Data team bottleneck → Business teams wait 2 weeks for reports.

Solution: Looker, documented data models → 80% queries self-serve.

7. Invest in ML Infrastructure

Lesson: Model v1 → v2 chậm vì manual feature engineering, inconsistent training/serving.

Solution: Feature Store, MLOps pipeline → Faster iteration.

8. Retain > Acquire

Lesson: Repeat customers: CAC $0 (already acquired), LTV $180, Default rate 3%. New customers: CAC $15, LTV $60, Default rate 8%.

Action: Build loyalty program, focus on repeat rate.

Tech Stack Evolution

Year 1 (2020)

├── Django (Backend)
├── PostgreSQL (Database)
├── AWS EC2 (Hosting)
├── Logistic Regression (Credit scoring)
└── Google Sheets (Analytics!)

Year 2 (2021)

├── Django (Backend)
├── PostgreSQL (Transactional)
├── Airflow (ETL)
├── BigQuery (Data Warehouse)
├── dbt (Transformations)
├── Looker (BI)
├── XGBoost (Credit scoring)
└── S3 (Data Lake)

Year 5 (2024)

Backend:
├── Django (API)
├── PostgreSQL (Transactional)
└── Redis (Caching)

Data Platform:
├── Kinesis (Real-time streaming)
├── Airflow (Orchestration)
├── S3 (Data Lake)
├── BigQuery (Data Warehouse)
├── dbt (Transformations)
├── Looker (BI)
├── Feast (Feature Store)
└── Custom A/B testing platform

ML:
├── TensorFlow (Deep Learning models)
├── Vertex AI (Model training, serving)
├── MLflow (Experiment tracking)
└── Custom serving layer (low-latency inference)

Monitoring:
├── Datadog (Infrastructure)
├── Evidently (ML monitoring)
└── Great Expectations (Data quality)

Kết Luận

Hành trình 0 → $50M của FinX chứng minh rằng data-driven culture là competitive advantage lớn nhất của startup.

Success Formula

Product-Market Fit
  + Rapid Experimentation
  + ML-Powered Decisioning
  + Cohort-Based Optimization
  + Self-Service Analytics
  = Hypergrowth + Profitability

For Startups: Roadmap to Data-Driven Growth

Phase 1: Foundation (Month 1-6)

• Instrument all events
• Setup basic analytics (GA, Mixpanel)
• Build first dashboards
• Track cohorts from day 1

Phase 2: Data Warehouse (Month 6-12)

• Cloud data warehouse (BigQuery)
• ETL pipeline (Airflow)
• dbt transformations
• Hire first data person

Phase 3: Self-Service (Year 2)

• BI tool (Looker, Tableau)
• Documented data models
• Train business teams
• 50%+ self-service adoption

Phase 4: ML (Year 2-3)

• First ML model (simple regression)
• A/B testing framework
• Feature store
• MLOps pipeline

Phase 5: Advanced (Year 3+)

• Real-time analytics
• Advanced ML (deep learning)
• Multi-model experimentation
• Data science team

Carptech - Giúp Bạn Scale như FinX

Tại Carptech, chúng tôi đã giúp nhiều fintech và startups Việt Nam xây dựng data platform để scale:

Dịch vụ của chúng tôi

• Data Platform Setup: Modern stack từ đầu (tránh technical debt)
• ML Engineering: Credit scoring, fraud detection, churn prediction
• Experimentation Framework: A/B testing platform, analytics
• Self-Service Analytics: Empower teams với dashboards, semantic layer

Case Studies

• Fintech: Credit scoring model, default rate giảm 40%
• E-commerce: Recommendation engine, revenue +35%
• Marketplace: Real-time dashboards, data-driven decisions

Liên hệ: https://carptech.vn

Bài viết được viết bởi Carptech Team - Chuyên gia về Data Platform & Analytics tại Việt Nam.

Note: Company name và specific metrics được anonymized để protect client confidentiality, nhưng patterns và learnings là thật từ real case studies.