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 ▪ Reduced deployment time ▪ More accurate predictions on parking lot usage |
| Avant | US | DataRobot | Finance | Loan Decisions | ▪ Time savings ▪ More accurate identification of risk |
| California Design Den | US | Google Cloud AutoML | Retail & Consumer Goods | E-Commerce | ▪ 50% reduction in inventory carryovers ▪ Improved profit margins |
| Consensus Corporation | US | DataRobot | Technology | Fraud Detection | ▪ 24% improvement in fraud detection ▪ 55% reduction in false-positives ▪ Reduced deployment time from 3-4 weeks to 8 hours |
| DemystData | US | DataRobot | Technology | Product Quality | ▪ Democratization of the process ▪ Reduced cost by one tenth |
| Domestic & General (D&G) | UK | DataRobot | Insurance | Customer Experience | ▪ Increased number of customers who gets optimal price from 40,000 to 300,000 ▪ Improved pricing optimization from 1.5% to 4% of the revenue |
| Evariant | US | DataRobot | Healthcare | Service Delivery and Marketing Management | ▪ 10 times shorter deployment time ▪ More one-to-one involvements with clients ▪ Increased ROI ▪ Improved service quality |
| G5 | US | H2O.ai | Real Estate | Marketing and Call Center Management | ▪ 5 times faster model building ▪ Improved accuracy to 95% |
| Harmoney | Australia | DataRobot | Fintech | Credit Application Process | ▪ More accurate risk assessment ▪ Increased profitability ▪ Shortened credit application process |
| 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 ▪ Improved diagnosis results |
| Lenovo | Brazil | DataRobot | Technology | Sales and Manufacturing Operations | ▪ Increased accurate predictions from 80% to 87.5% ▪ Reduced model creation time from 4 weeks to 3 days |
| LogMeIn | US | DataRobot | Technology | Customer Experience | ▪ Reduced data analysis time from days to minutes ▪ Continuously improved accuracy ▪ Reduced deployment time |
| Meredith Cooperation | US | Google Cloud AutoML | Media & Entertainment | Content Classification | ▪ Improved awareness for future trends ▪ Improved customer experience |
| NTUC Income | Singapore | DataRobot | Insurance | Pricing | ▪ Simplified data complexity ▪ Better identification of key drivers for prices |
| One Marketing | Denmark | DataRobot | Marketing | Email Marketing | ▪ Reduced spam for customers ▪ Improved mail open rate by 14% ▪ Improved mail click rate by 24% ▪ Increased ticket sales by 83% |
| Paypal | US | H2O.ai | Financial Services | Fraud Detection | ▪ Improved accuracy to 95% ▪ Reduced model training time to under 2 hours |
| Pelephone | Israel | DMWay | Telecommunications | Sales Management | ▪ Increased purchase rate by 3.5% in the first month ▪ Increased conversion rate by 300% |
| PGL | Israel | DMWay | Transportation Planning | Planning and Scheduling | ▪ Time savings in data analysis process ▪ Democratization of the process |
| Steward Health Care | US | DataRobot | Healthcare | Staff Planning | ▪ Net $2 million savings per year from 1% reduction in registered nurses hours ▪ Net $10 million savings per year from 0.1% reduction in patient length of stay |
| Trupanion | US | DataRobot | Insurance | Pricing and Sales Management | ▪ 10 times improved productivity by speeding up processes ▪ Identified that two thirds of customers will churn before they churn |
| Vision Banco | Paraguay | H2O.ai | Banking | Risk Management | ▪ Doubled propensity to buy ▪ Shortened and more accurate credit scoring process |
AutoML implementation challenges and solutions
Data quality issues
Challenge: Inconsistent data formats, missing values, and isolated data silos across departments often hinder AutoML adoption.
Solution: Establishing robust data governance frameworks and implementing unified data quality standards during the early stages ensures cleaner, more reliable datasets for modeling.
Technical integration problems
Challenge: Legacy systems frequently face compatibility issues with modern AutoML platforms and experience difficulties integrating via APIs.
Solution: Conducting thorough technical assessments before platform selection and planning for necessary system upgrades or middleware solutions helps ensure smoother integration.
Skills gap and resistance
Challenge: Limited understanding of AutoML tools among staff and skepticism from traditional analysts can slow down adoption.
Solution: Investing in comprehensive training programs that emphasize AutoML as a complementary tool fosters acceptance and empowers teams.
Resource constraints
Challenge: Budget limitations and the absence of dedicated teams for AutoML implementation can delay projects.
Solution: Initiating small-scale pilot projects with clear ROI helps demonstrate value early on, making it easier to secure further investment and resources.
Change management
Challenge: Shifting organizational culture towards data-driven automated decision-making may meet resistance.
Solution: Encouraging cross-functional collaboration and adopting a gradual implementation approach facilitates smoother cultural transitions.
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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