Published: January 2023 | Report Code: 11843 | Available Format: PDF | Pages: 221
The global automated machine learning market is estimated to generate USD 631.0 million in 2022, and it is expected to grow at a CAGR of 49.2% during 2022–2030, reaching USD 15,499.3 million by 2030. The key factors responsible for the growth of the industry include the increasing demand for efficient fraud detection solutions and surging need for personalized product recommendations.
The use of predictive lead scoring systems for customer segmentation and targeting potential customers is another factor driving the demand for AutoML solutions across the globe. Identifying potential customers is critical to run a business successfully. Previously, businesses had to use a number of resources, huge money, and a lot of time to identify potential consumers. Implementing a lead scoring system is easier than the traditional methods of finding customers.
AutoML solutions collect information about customer behavior, company size, and industry from several internal (CRM databases) as well as external (social media and web data) sources to evaluate each lead and generate a score against it. This score ascertains whether the lead is qualified or not for that particular business. Additionally, the solution can also offer reason codes for each lead, so that sales representatives know the key factors that make the lead valuable.
During the COVID-19 pandemic, financial services evolved and transformed digital business models, and their adoption has positively affected the market. In addition, many healthcare and government institutions have included machine-learning-enabled chatbots for the contactless screening of COVID-19 symptoms. For instance, Clevy.io, a French startup and Amazon Web Services (AWS) customer, has launched a chatbot to make it easier for people to find official government communications about the pandemic.
The platform category held the larger revenue share, of 73%, in 2022, based on offering. This is majorly attributed to the increasing adoption of these platforms across all verticals for fraud minimization, operational cost reduction, and customer service enhancement. In addition, the pandemic accelerated digital transformation in almost every sector, including manufacturing, healthcare, and BFSI, which led to the growing adoption of this technology.
The cloud category accounted for the higher revenue in the market in 2022, based on deployment type. This is because of the enhanced scalability and flexibility of cloud-based automated machine learning platforms, which clients can customize as per their requirements. In addition, as the cloud reduces operational and infrastructure costs, a large number of companies are increasingly adopting cloud-based solutions.
The large enterprise category dominated the market in 2022, based on enterprise size. This is mainly because of the diverse operations of such companies across the globe, which drives them to adopt these solutions for cost reduction, competitor analysis, customer retention, and effective marketing and sales strategies.
The sales & marketing management category is projected to register the fastest growth during the forecast period, based on application. This is because a large number of companies across all verticals are using these platforms to gain insights into customer emotion and facilitate content personalization, lead scoring, customer segmentation, and customer engagement.
The healthcare category is projected to register the highest growth rate in the market during the forecast period, based on industry. This can be attributed to the surging demand for machine learning by healthcare organizations for the early detection of diseases, research, training, and treating patients quickly and effectively, while saving time, money, and resources.
North America generated the highest revenue in the market in 2022. Technological advancements, the developed information technology (HIT) infrastructure, presence of key AutoML platform providers, and prosperous BFSI, IT & telecom, and healthcare industries are the major factors driving the growth of the market in the region.
The region has the strongest AI ecosystem in terms of government initiatives, funding, number of companies (almost 45% of all AI companies are based in North America), and global reach. Moreover, the widespread AI implementation across a number of sectors, including government organizations, is fueling the growth of the market in the region. For instance, as per International Development Research Center (IDRC), in the global government AI readiness index, the U.S. (4th rank) and Canada (6th rank) are better placed in terms of AI adoption by government bodies across the world.
The APAC market is expected to witness the fastest growth in the global market in this decade. This can be ascribed to the swift economic growth, increasing investments in the IT infrastructure, growing adoption of emerging technologies, and surging count of government initiatives for the development of AI technologies. Furthermore, APAC countries are the preferred destination for IT outsourcing. Owing to this, IT companies receive large-scale requests for application development, which boosts the market growth.
Cloud-based AutoML platforms are a major trend being observed in the market. Cloud-based solutions involve the software-as-a-service (SaaS) model, in which users can access the solutions virtually through a secured platform, over the internet. The cloud deployment of the solutions offers greater flexibility, scalability, and affordability and reduces IT infrastructure costs. As a result, large enterprises as well as SMEs are increasingly adopting cloud-based AutoML solutions. For instance, the global public cloud services sector grew from nearly USD 230 billion in 2019 to almost USD 270 billion in 2020.
The detection and prevention of frauds are a huge challenge for organizations across all verticals. Thus, the growing need for fraud detection solutions is leading to significant growth of the market for automated machine learning. For instance, as per the U.S. Government Accountability Office, in 2021, improper payments (payments that should not have been made or were made in an incorrect amount) totaled about USD 281 billion, an increase of about USD 75 billion from Fiscal Year 2020 (USD 206 billion). Furthermore, according to the Reserve Bank of India’s 2019–20 Annual Report, in the fiscal year ended June 2020, bank frauds totaling more than INR 1.85 lakh crore were reported, a significant rise over INR 71,500 crore the previous fiscal. Additionally, 4,160 major fraud cases were reported, totaling INR 1,82,051 crore and accounting for 98% of the total frauds. Further, a 28% rise was recorded in bank frauds, which numbered 8,707 in 2019–20 compared to 6,799 in 2018–19.
Moreover, with the increasing popularity of online shopping, the demand for personalized content is increasing, as customers prefer products that can meet their specific needs. Personalized product recommendations help companies increase the average order value. Thus, to meet the evolving need of customers for updated products, companies are investing heavily in new technologies, to offer better product recommendations. AutoML solutions find patterns in customer behavior from clickstream data, prior purchases, demographics, browsing history, and previous product searches and create 1:1 best personalized product recommendations that match their needs and preferences. Therefore, the demand for these solutions is increasing for personalized product recommendations in several verticals.
Market Size in 2022
USD 631.0 Million
Revenue Forecast in 2030
USD 15,499.3 Million
Market Trends, Drivers, and Restraints; Revenue Estimation and Forecast; Segmentation Analysis; Impact of COVID-19; Companies’ Strategic Developments; Market Share Analysis of Key Players; Company Profiling
By Offering; By Deployment Type; By Enterprise Size; By Application; By Industry; By Region
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The rising importance of effective product assortment in the retail store network would further boost the growth of the global industry in the coming years. Choosing the right mix of products in a retail store is extremely important for retailers, in order to meet the needs of customers and retain them. However, it is sometimes difficult for retailers to find a balance between product breadth and depth, as every store has different environmental factors, display capacities, and customer segments.
In this regard, automated machine learning solutions can be ideal for retailers for effective product assortment. These solutions can look at various factors, such as location, customer segments, weather patterns, store display space, and past sales records, to find out which products would be the best fit for a given store location. In addition, AutoML-based optimization can prevent stockouts and minimize markdowns by rerouting the inventory between stores where they can be sold at full price.
The report analyzes the impact of the major drivers and restraints on the market, to offer accurate market estimations for 2017–2030.
Based on Offering
Based on Deployment Type
Based on Enterprise Size
Based on Application
Based on Industry
The total value of the market for Automated machine learning was USD 631.0 million in 2022.
The growth rate of the market for Automated machine learning will be 49.2% during 2022–2030.
The forecast size of the market for Automated machine learning will be USD 15,499.3 million in 2030.
North America generated the highest revenue in the Automated machine learning industry.
APAC has the fastest growth in the Automated machine learning industry.
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