Use Cases — analytics@speed

01

3-Part Series

Recommendation Engine: The Holy Grail of One-to-One Cross-Sell & Up-Sell

From the simplest bestseller logic to AI-driven adaptive personalisation — a practitioner's complete guide to recommendation systems. Amazon's 35% of sales. Netflix's 75% of viewership. All driven by recommendation. Here's how it actually works.

Type 01 · Part 1

Bestseller Recommendation

The simplest and computationally cheapest approach — purely driven by maximum sales volume within a category. No machine learning needed, but surprisingly effective for cold-start and onboarding scenarios.

SQL Cold Start Popularity

Type 02 · Part 1

Market Basket & Affinity Rules

Association analysis — Support, Confidence, and Lift. The "diaper and beer" model that became the foundation of Amazon's "frequently bought together". Real-world lessons from a big Indian retailer with 80+ outlets.

Python Association Rules Apriori

Type 03 · Parts 2–3

AI-Driven Adaptive Systems

Collaborative filtering, matrix factorisation, deep learning approaches — the full spectrum from user-based to item-based to model-based recommendation. Production-ready patterns with Python and deployment considerations.

Collaborative Filtering Deep Learning Python

35%

Amazon Sales from Recommendations

75%

Netflix Viewership via Recommendation

3

Part Series · Python · R · SQL

SQL · Bestseller Recommendation · Type 1

SELECT TOP(3) SKU_ID, SUM(Transaction_Count) AS Sales_Count
FROM Transactions
WHERE Category = 'Electronics'
GROUP BY SKU_ID
ORDER BY Sales_Count DESC;
-- Returns top 3 bestsellers in category for cold-start recommendation

02

Framework Deep-Dive

RFM & RFMC: From Customer Scoring to Ghost Flyer Detection

The proven multi-decade framework for customer segmentation — extended with a fourth Channel dimension for the modern digital business. Covers 10+ actionable objectives from a single analytical effort: churn, loyalty, upsell, winback, early adopters, and the elusive ghost flyer segment.

Framework

Classic RFM Scoring

Recency, Frequency, Monetary — quintile scoring, segment labelling, and the 10 simultaneous marketing objectives you can drive from a single RFM framework. Python implementation included.

Python Pandas Segmentation

Extension

RFMC + Ghost Flyer Detection

Adding the Channel dimension to surface low-frequency, high-monetary customers invisible to standard RFM. The ghost flyer segment — 8–14% of active base, 25–35% of premium transaction value.

RFMC Anomaly Detection Banking

Application

Co-Branded Card Targeting

Applying RFMC to airline co-branded credit card analytics — expat remittance corridors, corporate travel leakage identification, and segment-specific engagement strategies for premium cardholders.

Credit Cards Loyalty

Python · RFMC Ghost Flyer Detection

def flag_ghost_flyers(rfmc_df):
    """Ghost flyers: Low R+F scores, High M score, Active channel."""
    mask = (
        (rfmc_df['R_score'].astype(int) <= 2) &   # Low recency
        (rfmc_df['F_score'].astype(int) <= 2) &   # Low frequency
        (rfmc_df['M_score'].astype(int) >= 4) &   # High monetary
        (rfmc_df['C_score'].astype(int) >= 2)    # Active on digital
    )
    rfmc_df['segment'] = 'standard'
    rfmc_df.loc[mask, 'segment'] = 'ghost_flyer'
    return rfmc_df

03

Applied NLP

NLP in the Enterprise: From Sentiment Analysis to LLM Integration

Extracting actionable insights from unstructured text data — customer feedback, social listening, regulatory documents, and internal communications. Production NLP patterns for non-English and multilingual environments.

Foundation

Sentiment & Text Classification

Customer feedback analysis, complaint classification, and NPS driver identification using classical ML and transformer models. Handling Arabic and bilingual text across markets.

BERT Arabic NLP Classification

Advanced

LLM Integration & Fine-Tuning

Practical guide to fine-tuning large language models on domain-specific banking data — synthetic data generation, differential privacy, and safe deployment in regulated environments.

LLMs Fine-Tuning Privacy

Frontier

Agentic AI Pipelines

Autonomous AI systems that monitor loan portfolios, draft executive summaries, and flag anomalies without human triggers. Architecture patterns, failure modes, and governance frameworks.

Agents Orchestration Banking