Zalingo Synthetic Retail — Price Elasticity & Promo Uplift — 100k

Need larger volumes or refresh? After purchase, ask about enterprise bundles and monthly/weekly/daily subscriptions (pricing/promo only or mixed retail).

Dataset Features (tight, buyer-friendly)

• order_id, basket_id, ts_utc, channel — POS | ecommerce | click-&-collect.
• store_id / site_id, country, city — Context.
• customer_id — Synthetic, non-linkable.
• item_sku, item_name, category, subcategory, brand — Taxonomy.
• list_price, unit_price, currency — Pre-promo vs actual price.
• discount_amount, discount_pct, promo_flag, promo_mechanics — bogof | multibuy | %off | price-point | coupon.
• treatment_flag, control_group_id — For uplift experiments.
• promo_window_start_utc / promo_window_end_utc — Promo span.
• baseline_units, promo_units, uplift_pct, uplift_label — Counterfactual vs observed.
• elasticity_estimate (continuous), elasticity_bucket — elastic | inelastic | unknown.
• price_index, competitor_price_index — Relative indices (synthetic).
• markdown_flag — Clearance indicator.
• basket_items_count, basket_value, cross_sell_index — Basket context.
• stock_on_hand, stockout_flag — Availability constraints.
• holiday_flag, weather_index — External factors (synthetic).
• return_flag, return_qty — Post-purchase behaviour.

Distribution

• Format: ZIP with Parquet shards (Snappy) + README.
• Volume: 100k rows, ~22–35 cols, 1–5 parts.
• Partitioning (full versions): by date / store(site) / category / SKU.

Usage

• Elasticity modelling (SKU/category/store).
• Promo uplift & ROI (treatment vs matched control).
• Markdown optimisation; cannibalisation checks.
• Demand forecasting with price/promo regressors.
• Pricing pipeline QA (dashboards, alerts).