Though there is a lot of buzz around autoML, we haven’t found a good compilation of case studies. So we built our comprehensive list of automated machine learning case studies so you can see how autoML could be used in your function/industry.
This AutoML case study list will help us to understand what AutoML is and how you can use it in your business function. The most common application areas of autoML are decision-making and forecasting. Read on to discover how AutoML can support your business function.
What are the typical results of AutoML projects?
In these case studies, we discovered companies gain various benefits from automation processes. These benefits support companies to improve their businesses and provide more efficient services. Below, you can find the top 3 typical results of those case studies.
- Time savings: AutoML provides faster deployment time by automating data extraction, and algorithms. In the end, manual parts of the analyses are eliminated and the deployment time reduces significantly. As an example, Consensus Corporation reduced its deployment time from 3-4weeks to 8 hours.
- Improved accuracy: As businesses continue, the data grows and, the industry trends change. AutoML automates these facts and removes any manual actions. As a result, any possible errors are eliminated and, continuously-evolving algorithms improve accuracy. With this benefit, companies can reach high levels of accuracy rate in their predictions. Trupanion can identify two-thirds of its customers will churn before they churn.
- Democratization: Machine learning applications require high-level skills which make companies dependent on data scientists. By AutoML, these processes can be done without high-level knowledge.
What are typical cases for AutoML?
Companies can automate their machine learning processes for a variety of purposes. In most of these use cases, companies have already implemented machine learning and want to improve their performance. Mostly, companies want to have automated insights for better data-driven decisions and predictions. The typical processes we have observed from the case studies are:
The full list of case studies that we have collected from different AutoML vendors can be found below. You can filter the list by the vendor, industry, or use case and investigate the achieved results.
| Company | Country | AutoML Tool | Industry | Use Case | Results |
|---|---|---|---|---|---|
| Ascendas-Singbridge Group (ASG) | Singapore | DataRobot | Real Estate | Parking Lot Efficiency | ▪ 20% increase in revenue |
| Avant | US | DataRobot | Finance | Loan Decisions | ▪ Time savings |
| California Design Den | US | Google Cloud AutoML | Retail & Consumer Goods | E-Commerce | ▪ 50% reduction in inventory carryovers |
| Consensus Corporation | US | DataRobot | Technology | Fraud Detection | ▪ 24% improvement in fraud detection |
| DemystData | US | DataRobot | Technology | Product Quality | ▪ Democratization of the process |
Domestic & General (D&G) | UK | DataRobot | Insurance | Customer Experience | ▪ Increased number of customers who gets optimal price from 40,000 to 300,000 |
| Evariant | US | DataRobot | Healthcare | Service Delivery and Marketing Management | ▪ 10 times shorter deployment time |
| G5 | US | H2O.ai | Real Estate | Marketing and Call Center Management | ▪ 5 times faster model building |
| Harmoney | Australia | DataRobot | Fintech | Credit Application Process | ▪ More accurate risk assessment |
| Hortifrut | Chile | H2O.ai | Agriculture | Product Quality | ▪ Reduced deployment time from weeks to hours |
| Imagia | Canada | Google Cloud AutoML | Healthcare | Research and Development | ▪ Reduced test processing time from 16 hours to 1 hour |
| Lenovo | Brazil | DataRobot | Technology | Sales and Manufacturing Operations | ▪ Increased accurate predictions from 80% to 87.5% |
| LogMeIn | US | DataRobot | Technology | Customer Experience | ▪ Reduced data analysis time from days to minutes |
| Meredith Cooperation | US | Google Cloud AutoML | Media & Entertainment | Content Classification | ▪ Improved awareness for future trends |
| NTUC Income | Singapore | DataRobot | Insurance | Pricing | ▪ Simplified data complexity |
| One Marketing | Denmark | DataRobot | Marketing | Email Marketing | ▪ Reduced spam for customers |
| Paypal | US | H2O.ai | Financial Services | Fraud Detection | ▪ Improved accuracy to 95% |
| Pelephone | Israel | DMWay | Telecommunications | Sales Management | ▪ Increased purchase rate by 3.5% in the first month |
| PGL | Israel | DMWay | Transportation Planning | Planning and Scheduling | ▪ Time savings in data analysis process |
| Steward Health Care | US | DataRobot | Healthcare | Staff Planning | ▪ Net $2 million savings per year from 1% reduction in registered nurses hours |
| Trupanion | US | DataRobot | Insurance | Pricing and Sales Management | ▪ 10 times improved productivity by speeding up processes |
| Vision Banco | Paraguay | H2O.ai | Banking | Risk Management | ▪ Doubled propensity to buy |
You can also check out our sortable and data-driven list of AutoML Software. To learn more about AutoML, you can read our in-depth AutoML guide.
You can also review our list of AutoML solution providers to find the right vendor for your business.
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