DataRobot Helps D&G Find Success When the Price Is Right
Customer Company Size
Large Corporate
Region
- Europe
Country
- United Kingdom
Product
- DataRobot Automated Machine Learning Platform
Tech Stack
- Python
- R
- DataRobot API
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
Domestic & General (D&G) is a company that specializes in providing specialist warranties for household appliances. The company has been in operation for over 100 years and is headquartered in the UK. D&G has more than 16 million customers in 14 countries and protects over 200 different types of electrical items. The company focuses on customer satisfaction and personalization, making it the largest appliance and gadget insurance company. D&G offers more coverage than other warranty providers and aims to deliver personalized and relevant offers for complementary services and products that make sense for each customer.
The Challenge
Domestic & General (D&G), a specialist in providing warranties for household appliances, was facing a challenge in personalizing and delivering relevant offers to its customers. With 9 million customers in the UK and 16 million globally, the company was resource-constrained for the scale of personalized customer service and offerings they were trying to reach. The company's pricing team had to build a lot of models for each customer, which was a laborious and time-consuming process. D&G wanted to predict the likelihood of churn when customers are up for renewal and determine the price point at which customers are most likely to be happy with the warranty coverage they receive and renew their policies. However, delivering this level of personalization to individual customers required building a lot of pricing models, which was not scalable with their existing resources.
The Solution
D&G turned to DataRobot's automated machine learning platform to automate the building of their predictive machine learning models. The company launched a POC engagement with DataRobot at the beginning of 2017, testing a price optimization approach with the DataRobot API. The POC delivered more accurate models in less time than the status quo in R. Once they got buy-in and security approval to move forward with the cloud, D&G was up and running and ready to optimize pricing for all its customers. Now, all of their pricing models are built in DataRobot and fed into D&G’s price optimization system. For each customer, D&G’s pricing system makes a call to DataRobot and identifies the customer’s profile. DataRobot then spits out 200 price points for each customer, with a prediction at each price point. The system then identifies the optimal price where the customer will most likely renew and be happy with his service and delivers that to him at the time of renewal.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Procter & Gamble Implements Terra Technology's Demand Sensing for Improved Forecast Accuracy
Procter & Gamble (P&G) faced significant challenges in accurately forecasting short-term demand for their consumer products. Their existing 24-month forecast provided a good overview for monthly or weekly production, but it was insufficient for the immediate needs of supply chain planning and manufacturing teams. These teams required a short-term forecast to plan production effectively and avoid 'fire-fighting' practices. P&G needed a solution that could provide accurate short-term demand forecasts to ensure agility and flexibility in manufacturing, especially for products with very short production and order lead times. The company explored various solutions but found that most big software companies lacked the agility to meet their specific demand sensing needs. Terra Technology's Real-Time Forecasting, later known as Demand Sensing (DS), emerged as a promising solution due to its specialized focus on consumer packaged goods (CPG) demand planning and forecasting.
Case Study
Blue Bottle Coffee Enhances Ordering Accuracy and Reduces Waste with ML-Driven Demand Forecasting
Blue Bottle Coffee (BBC), a global coffee roaster and retailer, faced a significant challenge in managing the supply of pastries across its international network of cafes. The company was using a manual ordering system, where cafe leaders estimated the required quantity of pastries based on historical sales data, current inventory, and growth projections. This system was effective when BBC had a few cafes, but with over 70 cafes worldwide, it became inefficient and inaccurate. The inaccuracies led to either under-ordering, causing sell-outs and customer dissatisfaction, or over-ordering, resulting in food waste and profit loss. The suboptimal utilization of pastries was also affecting BBC's bottom line. Therefore, BBC needed a scalable, precise, and predictive ordering solution to improve pastry ordering accuracy, reduce food waste, and meet its sustainability goals.
Case Study
Designing an intuitive UI for effective product demand forecasting in retail
The client, a leading luxury store chain operating in over 100 countries, was facing challenges with their product demand forecasting process. The process involved a significant amount of manual work, with all sales-related data being kept in Excel tables and calculated manually. The client's merchandising and planning experts used a demand forecasting web application to make estimations of customer demand over a specific period of time. The solution calculated historical data and other analytical information to produce the most accurate predictions. However, the client wanted to improve the efficiency and effectiveness of this process, making it faster, more accurate, and less complicated for their employees. They sought to unify all processes under an intuitive UI.
Case Study
YAZAKI Europe Limited: Ensuring rapid, cost-efficient order fulfillment and quicker insights into business performance
YAZAKI Europe, a leading automotive supplier specializing in the production of customized wiring harnesses for car manufacturers, was facing challenges in meeting the automotive industry’s demand for same-day delivery. The company's continuous growth was impacting its ability to analyze performance and complete its end-of-month consolidation and reporting processes for its headquarters in Japan. The company needed to eliminate delays and manual processing in its logistics to ensure extremely efficient operations. The company also needed to boost its data analytics capabilities for its finance and controlling departments to accelerate the delivery of complex financial reports.
Case Study
How the Philadelphia 76ers Win Off the Court Using Machine Learning from DataRobot
The Philadelphia 76ers, a professional basketball team in the NBA, is part of a new wave of sports franchises that are leveraging data analytics to optimize both their on-court performance and business operations. The organization has a strong focus on using data to inform decision-making processes across all levels. One of the key challenges faced by the 76ers' Analytics Team was improving the efficiency of their season ticket renewal process. The team had been using data science and simple modeling techniques, but lacked a dynamic machine learning tool that could adapt and learn as more data was collected. This meant that the team had to do a lot of work in the offseason to produce a static model. The goal was to transform the renewal process from a once-a-year event into a year-round retention process.
Case Study
Business services company saves approx. 80% of projected deployment costs with Acumatica
Caystone Solutions Ltd., a small but ambitious company, aimed to provide a variety of services to entrepreneurs and individuals globally. To achieve this, Caystone needed to operate with extraordinary efficiency and be able to easily manage growth. The company required an accounting system that could support its own business requirements as well as its clients’. The system needed to be customizable, support multiple currencies, and unlimited users. It also needed to provide both Caystone and its clients anytime, anywhere access from any web browser. Caystone required a system that wouldn’t necessitate a big—and costly—hardware infrastructure. And Bahamas-based Caystone needed the comfort of knowing its client data was stored locally.
