Blend Café Dynamic Pricing Recommendation Engine
Rushikesh Sagar
Blend Café — Dynamic Pricing Recommendation Engine
Data Science Internship | Blend Café, Deccan, Pune | July 1 – August 15, 2025 Headline result:+4.64%projected revenue uplift on the test period (Jul 16–31), strongest condition: Dinner × Clear After Rain (+10.9% per item)
What This Project Does
Blend Café (Deccan, Pune — adjacent to Fergusson College and BMCC) uses entirely static pricing. Every item has one fixed price regardless of time of day, weather, or demand pattern. This project builds a three-phase dynamic pricing recommendation engine that analyses 3 months of transaction data and recommends item-level prices per time slot, day type, and weather state.
Key engineering constraint: The café owner needed an explainable system. A neural network outputting a price is not explainable. A majority vote of three interpretable signals is.
Quick Numbers
Metric Value Revenue uplift (test period) +4.64% (₹8,793 on ₹1,89,322) Test period Jul 16–31, 2024 — 671 transactions Best single condition Dinner × Clear After Rain +10.9% Best slot Dinner +7.35% Best weather state Clear After Rain +7.60% Price recommendations generated 6,400 (200 items × 32 conditions) Floor violations 0 Premium items unflagged 0 HOLD rate 84.7% — intentional, brand-protective LR test RMSE 0.3972 RF test RMSE 0.4024
Project Structure
Environment Setup
How to Run
Run all scripts from the project root in order:
Each script prints a completion summary. All scripts are fully re-runnable — outputs are overwritten cleanly on each run.
Three Phases
Phase 1 — Excel Analysis (Weeks 1–2)
Manual Excel work on
Raw_Transactions. Pivot tables, ABC classification, revenue heatmaps, AOV analysis. Python scripts replicate and extend this in scripts/data_analysis/01–03.Key finding: 95 items (47.5% of menu) drive 69.6% of revenue. The classic Pareto 80/20 does not hold — Blend Café's revenue is broadly distributed. This shaped the decision to build models for the full 200-item menu rather than a short priority list.
Phase 2 — Statistical Analysis (Weeks 3–4)
Correlation analysis, price elasticity, Pareto validation, time series decomposition, demand segmentation.
Key finding: Weather is the strongest demand signal. Cold Brew vs Temperature:
r = +0.503. Footfall vs Revenue: r = +0.906. 38 items show a rain-boost effect — demand increases on Heavy Rain days (Masala Chai, soups, hot chocolate). Time series decomposition revealed the June monsoon dip and July college-return recovery cleanly in the trend component.Phase 3 — Machine Learning (Weeks 5–6)
25 engineered features, Linear Regression baseline, Random Forest primary model, three-signal price recommender, revenue uplift comparison.
Key finding and honest failure: Transaction-level quantity (
Quantity_Units) had insufficient variance for meaningful ML forecasting — 80% ones, 20% twos, std ≈ 0.40. Both LR and RF converged to predict the mean (RMSE ≈ 0.40, R² ≈ 0).Predicting the mean of 1.2 every time gives RMSE of 0.40. That is almost exactly what both models produced. There was no signal to learn.
Redesign decision: The price recommender was rebuilt using three direct demand signals — footfall vs baseline, weather state, and slot-day revenue percentile — with a majority-vote verdict per recommendation. More explainable, fully traceable, and does not depend on a near-constant target.
Key Design Decisions
Classical ML only — no deep learning 3,818 rows, binary-range target, non-technical audience. Random Forest with feature importances produces a chart explainable to a café owner. PyTorch would have overfitted and added zero interpretability.
Chronological train/test split Train: May 1 – July 15. Test: July 16–31. Random split explicitly rejected — a pricing model must generalise to future dates, not random past dates.
85% price floor — data-driven, not gut-feel Derived from Phase 2 customer segment analysis: Premium items are bought by customers for whom the price is part of the signal. Discounting below 85% destroys the brand signal that drives full-price sales on high-value evenings. Zero floor violations in 6,400 recommendations.
84.7% HOLD rate — intentional Dynamic pricing does not mean constant change. The system only changes prices when two of three signals strongly agree. 84.7% HOLD means the café charges its normal price most of the time. This was a design choice, not a limitation.
Data
All transaction data is simulated. Blend Café does not have a digital POS system. The simulation covers May–July 2024 (Pune summer through full monsoon) using historically grounded assumptions for Pune weather, Deccan-area footfall, and Blend Café's own menu timing windows.
Stat Value Simulation period May 1 – July 31, 2024 (92 days) Total transactions 3,818 rows Menu items 200 across 24 categories Total simulated revenue ₹10,40,338 Simulation seed 42 (fully reproducible)
The raw Excel has four sheets:
Raw_Transactions, Daily_Summary, Item_Master, and README. Only data/obs/ is read-only ground truth.Key Outputs
File Location Description
price_recommendations.csv data/models/ 6,400 recommended prices revenue_uplift_analysis.csv data/reports/ Static vs dynamic comparison project_evaluation_summary.csv data/reports/ All headline metrics project_summary.png data/data_analysis/charts/ Four-panel project overview demand_segmentation_2x2.png data/data_analysis/charts/ Item pricing segments revenue_uplift_waterfall.png data/data_analysis/charts/ Revenue impact waterfall chartSpecs and Documentation
Document Description
spec/interview_project.yaml Master technical specification — all decisions, thresholds, and rationale spec/companion.md Interview narrative, failure stories, Q&A referenceAbout Me
Final-year CS student in Pune building toward ML engineering roles at YC-backed startups. My work sits at the intersection of deep learning, reinforcement learning, and RAG/LLM systems — all applied to financial markets.
Other projects:
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Blend Café Dynamic Pricing Engine — Data Science Internship, July–August 2025, Pune
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Posted Apr 27, 2026
6,400 price recommendations, 200 menu items. Diagnosed near-zero R², redesigned system. +4.64% revenue uplift.
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Timeline
Jul 1, 2025 - Aug 15, 2025
