Big Data Analytics & Business Intelligence — Vodafone

Served as General Manager at Vodafone, leading analytics and business intelligence initiatives across global telecom operations. Over 20 years of hands-on experience in data analytics informed a strategic approach to deploying big data at scale — from recommendation engines and churn reduction models to NPS modelling and ML deployment across production environments. Spoke at TMForum on the practical pitfalls and opportunities of big data in telcos.

1. Deploy ML models at scale across production systems

Designed and deployed machine learning models into live telecom environments, including recommendation engines and customer churn prediction. Each model was built with production constraints in mind — latency, throughput, and monitoring from day one.

# Example: Churn prediction pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import pandas as pd

def build_churn_model(df: pd.DataFrame, target="churned"):
    features = [c for c in df.columns if c != target]
    X, y = df[features], df[target]
    model = RandomForestClassifier(n_estimators=300, max_depth=10, random_state=0)
    model.fit(X, y)
    preds = model.predict(X)
    print(classification_report(y, preds))
    return model

2. Build NPS modelling and customer insight frameworks

Developed end-to-end NPS (Net Promoter Score) modelling pipelines that connected survey responses to operational data — enabling the business to identify the specific touchpoints driving customer satisfaction or dissatisfaction.

3. Drive churn reduction through predictive analytics

Built and iterated on churn-reduction models that flagged at-risk customers before contract expiry. These models were integrated into CRM workflows, allowing sales and retention teams to act on predictions in real time.

# Example: At-risk customer flagging
def flag_at_risk(customers: pd.DataFrame, model, threshold=0.6) -> pd.DataFrame:
    scores = model.predict_proba(customers.drop(columns=["customer_id"]))[:, 1]
    customers["churn_score"] = scores
    customers["at_risk"] = customers["churn_score"] >= threshold
    return customers[customers["at_risk"]].sort_values("churn_score", ascending=False)

4. Present insights at industry forums

Spoke at TMForum in France on the strategic use of big data in telecoms, covering common pitfalls — data silos, governance gaps, and over-engineering — and practical paths to value extraction.