Prerna Pant

About Prerna Pant

Strategic leader in loyalty, personalization, and customer insights with 15+ years driving customer growth for global retailers across grocery, fashion, and beauty. Expert in designing enterprise loyalty and pricing strategies, embedding data and science into executive decisions, and effective cross-collaboration with diverse teams to deliver best-in-class ML models and advanced analytics solutions

Experience

1. Director, Applied Data Science Retail

   dunnhumby

   Nov 2021 — Present · Seattle, WA, US

   Drove material commercial outcomes from loyalty and personalization:~1.5% of annual retailer sales attributed to loyalty,+1 pp YoY retention among loyal customers, and multi‑x ROI on investment- Applied DS leader for Loyalty & Personalization across 3–4 West Coast grocery banners; led teams up to 13 (4 direct; 9 dotted in India) and represented Data Science in NA and client MBRs- Partner to CMO/Chief Customer/Chief Merchandising leaders on loyalty strategy, investment optimization, and measurement—optimizing a low–eight‑figure annual spend and influencing mid–eight‑figure revenue in a multi‑billion‑dollar portfolio- Scaled enterprise personalization: 6 campaigns/month across 3 banners via email/print;>50% personalized comms;~10 offers per customer using >100 features; 200M+ offers/year with ~8M redemptions (~5% redemption;~20% email‑to‑redemption);~800K customers per bi‑weekly send- Built experimentation/measurement foundation (matched controls, stratified sampling, ANCOVA, causal inference); 2 A/B tests/month,~4‑week time‑to‑learn; monthly exec readouts with next‑best‑action- Streamlined and de‑risked campaign ops via cross‑collaboration with Research Science, Tech, and Product—cut bi‑weekly targeting from ~40 hours to ~8 with 100% error‑free delivery- Served as DS Lead on cross‑functional teams, architecting in‑house business performance analytics and campaign management platforms for several multi‑billion‑dollar retailers, delivering actionable insights that drove optimization- Core stack and methods: GCP, Spark, Python, SQL, Power BI; models across propensity, churn, behavioral segmentation, demand elasticity/forecasting, decision trees, causal inference- Team leadership: Hired 7 DS over 6 years (gender parity),~80% retention through July 2025; coached/advocated multiple promotions; leadership training and structured mentorship, including women leaders’ mentorship.

Education

• Delhi University

  Bachelors

  2001 — 2004

Skills

• Business Analytics
• Sas
• Predictive Analytics
• Analytics
• Customer Analysis
• Predictive Modeling
• Forecasting
• Sql
• Data Analysis
• Statistical Modeling
• Segmentation
• Sas Programming