Dynamic Pricing with Predictive Analytics
Customer Company Size
SME
Region
- Europe
Country
- France
Product
- PriceMoov
- Dataiku Data Science Studio (DSS)
Tech Stack
- GMM
- DBSCAN Time Series Forecasting
- Random Forest
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Software
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
PriceMoov is a Plug and Play Yield Management solution. Founded in 2016, Pricemoov has been experiencing strong growth. Some of its users are car rentals services, but also airline companies and event organizers. The company delivers optimal pricing suggestions and solutions to its customers by weighing the intrinsic value of the item, its seasonality, and the attributes of the customer himself through detailed segmentation. To do so, Pricemoov collects datasets from its customers that are updated daily through partitioning.
The Challenge
PriceMoov, a service that delivers optimal pricing suggestions and solutions to its customers, was facing a challenge with data originating from old SI systems, Oracle, or MySql. The data was dirty and required a fulltime developer to perform long ETL steps in PHP for cleaning. Once cleaned, the datasets were painfully entered into a model, as they were custom-built pipelines. And once finished, the replication and deployment process for the next customer was taking weeks. This long and arduous data preparation process was causing stale pricing recommendations.
The Solution
PriceMoov discovered Dataiku Data Science Studio (DSS), which transformed their business by allowing them to run proof-of-concepts for potential customers on short notice thanks to significantly faster data cleaning processes and the ability to quickly replicate existing work. The data department at Pricemoov now uses Dataiku to replicate existing workflows to get proof-of-concepts for potential customers up and running quickly, significantly speed up data cleaning and exporting, and better define a specific price per customer that evolves over time by melding data indicating demand with customers’ willingness to pay.
Operational Impact
Quantitative Benefit
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