Top 22 AutoML Case Studies/Examples: In-depth Guide in 2024

Updated on Jan 12

3 min read

Written by

Table of contents

What are the typical results of AutoML projects?What are typical cases for AutoML?

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:

Fraud Detection
Pricing
Sales Management
Marketing Management

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

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.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.