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Revolutionizing Dynamic Pricing with Pricemoov and Dataiku
Technology Category
- Analytics & Modeling - Predictive Analytics
- Automation & Control - Human Machine Interface (HMI)
Applicable Industries
- E-Commerce
- Retail
Use Cases
- Personnel Tracking & Monitoring
- Time Sensitive Networking
The Challenge
Pricemoov, a yield management solution provider, faced a significant challenge in handling and cleaning data from old SI systems, Oracle, or MySql. The data was dirty and required a full-time developer to perform long ETL (extract-transform-load) steps in PHP for cleaning. Once cleaned, the datasets were painstakingly entered into a model, as they were custom-built pipelines. The replication and deployment process for the next customer was taking weeks. This slow and inefficient process was hindering Pricemoov's ability to provide optimal pricing suggestions and solutions to its customers in a timely manner.
The Customer
PriceMoov
About The Customer
Pricemoov is a Plug and Play Yield Management solution provider founded in 2016. It has been experiencing strong growth and its users include car rental services, airline companies, and event organizers. Pricemoov provides a service that 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 Solution
Pricemoov adopted Dataiku, a tool that transformed their business by significantly speeding up data cleaning processes and enabling quick replication of existing work. This allowed Pricemoov to run proof-of-concepts for potential customers on short notice and provide better pricing options overall. The Data Department at Pricemoov used Dataiku to replicate existing workflows, speed up data cleaning and exporting, and enable less experienced staff to assist with this process. This left tenured data scientists to focus on modeling rather than data prep and plumbing. Non-technical teams could build their skills and scale their efforts thanks to an intuitive, visual point-and-click interface. Dataiku also helped Pricemoov to 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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