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Dynamic Pricing With PriceMoov

 Dynamic Pricing With PriceMoov - IoT ONE Case Study
The Challenge

PriceMoov’s challenge was that data originating from old SI systems, Oracle, or MySql 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 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.

The Customer


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 rental services, but also 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.

The Solution

PriceMoov discovered Dataiku, which has transformed their business by not only allowing them to run proofs-of-concept for potential customers on short notice thanks to significantly faster data cleaning processes and the ability to quickly replicate existing work but also ultimately by enabling them to provide better pricing options overall.

The Data Department at PriceMoov Now Uses Dataiku To:

  1. Replicate existing workflows to get proofs-of-concept for potential customers up and running quickly.
  2. Significantly speed up data cleaning and exporting, leveraging Dataiku’s visual point-and-click interface to enable less experienced staff to assist with this process, and leaving tenured data scientists to focus on modeling rather than data prep and plumbing.
  3. Non-technical teams (like marketing) can build their skills and scale their efforts thanks to an intuitive, visual point-and-click interface. Longer term, the goal is to have them efficiently and independently leveraging website clickstreams and HDFS datasets.
  4. Better define a specific price per customer that evolves over time by melding data indicating demand with customers’ willingness to pay.
  5. Deliver specific insight for local branches by quickly applying geo clustering.
  6. Quickly submit pricing options to local branches of brick-and-mortar stores, who can then choose to accept the options or not and can seamlessly share feedback to improve the model.

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