Dataiku Case Studies Dynamic Pricing with Predictive Analytics

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	Dynamic Pricing with Predictive Analytics Dataiku

Dynamic Pricing with Predictive Analytics

Dataiku

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Software
Applicable Functions	Sales & Marketing
Use Cases	Predictive Replenishment
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Software

Applicable Functions

Sales & Marketing

Use Cases

Predictive Replenishment

Services

Data Science Services

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.

About 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.

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.

Impact #1	A two week improvement in the speed at which they could produce pricing and forecast models.
Impact #2	The creation of 10 times more scenarios.
Impact #3	An improvement in staff performance and development, allowing new hires to prototype code in Jupyter notebooks and sales teams better sell the product.

Impact #1

A two week improvement in the speed at which they could produce pricing and forecast models.

Impact #2

The creation of 10 times more scenarios.

Impact #3

An improvement in staff performance and development, allowing new hires to prototype code in Jupyter notebooks and sales teams better sell the product.

Benefit #1	Delivery of 10x more models
Benefit #2	Two week improvement in the speed at which they could produce pricing and forecast models
Benefit #3	Improvement in staff performance and development

Benefit #1

Delivery of 10x more models

Benefit #2

Two week improvement in the speed at which they could produce pricing and forecast models

Benefit #3

Improvement in staff performance and development

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Overview

Dynamic Pricing with Predictive Analytics

Operational Impact

Quantitative Benefit