IoT Data Lakehouse for Smart HotelsIoT Data Lakehouse for Smart Hotels
The Challenge
A kids’ marketplace needed to drop prices intelligently at the product-plus-customer level to win back viewed and abandoned-cart items without eroding margin. Pricing had to react to demand, stock, and each customer’s prior interactions in a fast-moving market.
The Solution
Coditas built a dynamic pricing engine trained on historical sales, customer segments, and inventory. A price-sensitivity score from 0 to 1, derived from six months of purchase patterns, plus global and regional sale-momentum scores, fed a model that adjusted prices in real time based on demand and stock, then alerted customers to reduced prices on abandoned items.
The Outcome
The solution improved purchase completion from previously abandoned carts, strengthened customer engagement through personalized pricing, improved inventory turnover on slow-moving stock, and grounded pricing decisions in real behavioral data.
Engineered With: dbt · Python · SQL · Snowflake · MongoDB · Power BI
As our customer base grew, monthly reports were no longer enough for the decisions property teams needed to make. Coditas helped us create a data foundation that gives each property timely, relevant insights while keeping customer data properly separated and governed.
Build an AI Strategy Your Teams Can Act On
Ashutosh Zatke
Director - Growth Strategy
When you win, we win.
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