Customer and partner announcements showcase momentum in generative AI

Google Cloud announced a series of new customers, partners, and product innovations to help every business, government, and user benefit from generative AI and leading cloud technologies.

"We are in an entirely new era of cloud, fueled by generative AI," said Thomas Kurian, CEO, Google Cloud. "Our focus is on putting gen AI tools into the hands of everyone across the organization—from IT, to operations, to security, to the board room. As the industry's most open cloud, our goal is to help companies use AI and other cloud technologies to streamline their operations, increase productivity, and create entirely new lines of business."

Customer momentum around the world
Organizations across industries and around the world are choosing Google Cloud to digitally transform, and this year we've shared stories from Culture Amp, Deutsche Borse, eDreams ODIGEO, Government of Singapore, HSBC, IHOP, IPG Mediabrands, John Lewis Partnership, The Knot Worldwide, Macquarie Bank, Mayo Clinic, Priceline, Shopify, U.S. Steel, and Wendy's. Today, we are announcing new or expanded relationships with The Estée Lauder Companies, FOX Sports, GE Appliances, General Motors, HCA Healthcare, and more.

Innovative gen AI startups, like Replit, Typeface, Jasper, and more, are increasingly choosing to build on Google Cloud. In fact, today more than half of all funded gen AI startups – including companies like Contextual, CoRover, Elemental Cognition, Fiddler, and Quora are Google Cloud customers. This includes 70% of gen AI unicorns, like AI21, Anthropic, Cohere, Runway, and others.

New infrastructure and tools to help customers
The advanced capabilities and broad applications that make gen AI so revolutionary demand the most sophisticated and capable infrastructure. Today, Google Cloud announced key infrastructure advancements to help customers, including:

• Cloud TPUv5e: This is Google Cloud's most cost-efficient, versatile, and scalable purpose-built AI accelerator to date. Now, customers can use a single Cloud TPU platform to run both large-scale AI training and inferencing.
• A3 VMs with NVIDIA H100 GPU: A3 VMs powered by NVIDIA's H100 GPU will be generally available next month, enabling organizations to achieve three times better training performance over prior generation A2.
• GKE Enterprise: This enables multi-cluster horizontal scaling required for the most demanding, mission-critical AI/ML workloads.
• Cross-Cloud Network: This is a global networking platform that helps customers connect and secure applications across clouds. It is open, workload-optimized, and offers ML-powered security to deliver zero trust.
• Google Distributed Cloud (GDC): GDC is designed to meet the unique demands of organizations that want to run workloads at the edge or in their data centers. The GDC portfolio will bring AI to the edge, with Vertex AI integrations and a new managed offering of AlloyDB Omni on GDC Hosted. 

Vertex AI platform gets even better
On top of Google Cloud's world-class infrastructure, the company delivers a comprehensive AI platform—Vertex AI—that enables customers to build, deploy, and scale machine learning models. Customers have access to more than 100 foundation models, including third-party and popular open-source versions, as well as industry-specific models like Sec-PaLM 2 for cybersecurity and Med-PaLM 2 for healthcare and life sciences. New innovations announced today include:

• Vertex AI Search and Conversation: Now generally available, the tools enable organizations to create Search and Chat applications using their data in just minutes, with minimal coding and enterprise-grade management and security built in.
• PaLM 2, Imagen and Codey Upgrades: This includes updating PaLM 2 to 32k context windows so enterprises can easily process longer form documents like research papers and books.
• Tools for tuning: For PaLM 2 and Codey, Google Cloud is making adapter tuning generally available, and introducing a new method for Imagen, called Style Tuning, so enterprises can create images aligned to brand guidelines with a small amount of reference images.
• New models: Google Cloud is announcing availability of Llama 2 and Code Llama from Meta, and Technology Innovative Institute's Falcon LLM, a popular open-source model, as well as pre-announcing Claude 2 from Anthropic. Google Cloud will be the only cloud provider offering both adapter tuning and RLHF for Llama 2.
• Vertex AI extensions: Developers can access, build, and manage extensions that deliver real-time information, incorporate company data, and take action on the user's behalf.
• Digital Watermarking on Vertex AI: Powered by Google DeepMind SynthID, this is a state-of-the art technology that embeds the watermark directly into the image of pixels, making it invisible to the human eye and difficult to tamper with.
• Colab Enterprise: This managed service combines the ease-of-use of Google's Colab notebooks with enterprise-level security and compliance capabilities, helping data scientists accelerate AI workflows.

From the beginning, Google Cloud designed Vertex AI to give users full control and segregation of their data, code and IP. User prompts and data, as well as user inputs at inference time, are not used to improve our models and are not accessible to other customers.

Duet AI in Workspace and Google Cloud
Google Cloud unveiled Duet AI at I/O in May, introducing powerful new features across Workspace and showcasing developer features such as code and chat assistance in Google Cloud. Today, Google Cloud is making Duet AI in Google Workspace generally available, while expanding the preview capabilities of Duet AI in Google Cloud, with general availability coming later this year.

Workspace is the world's most popular productivity tool, with more than 3 billion users and more than 10 million paying customers who rely on it every day to get things done. With the introduction of Duet AI just a few months ago, Workspace delivered a number of features to make users more productive, like helping write and refine content in Gmail and Google Docs, create original images in Google Slides, turn ideas into action and data into insights with Google Sheets, foster more meaningful connections in Google Meet, and more. New enhancements announced today include:

• Duet AI in Google Meet: Duet AI will take notes during video calls, send meeting summaries, and even automatically translate captions in 18 languages. Duet AI in Meet also today unveiled studio look, studio lighting, and studio sound.
• Duet AI in Google Chat: Users can chat directly with Duet AI to ask questions about their content, get a summary of documents shared in a space, and catch up on missed conversations.

Beyond Workspace, Duet AI can now provide AI assistance across a wide range of Google Cloud products and services—as a coding assistant to help developers code faster, as an expert adviser to help operators quickly troubleshoot application and infrastructure issues, as a data analyst to provide quick and better insights, and as a security adviser to recommend best practices to help prevent cyber threats. Duet AI in Google Cloud announcements include advancements for:

• Software development: Duet AI provides expert assistance across the entire software development lifecycle, enabling developers to stay in flow-state longer by minimizing context switching to help them be more productive. In addition to code completion and code generation, it can assist with code refactoring and building APIs using simple natural language prompts.
• Application and infrastructure operations: Operators can chat with Duet AI in natural language across a number of services directly in the Google Cloud Console to quickly retrieve "how to" information about infrastructure configuration, deployment best practices, and expert recommendations on cost and performance optimization.
• Data Analytics: Duet AI in BigQuery provides contextual assistance for writing SQL queries as well as Python code, generates full functions and code blocks, auto-suggests code completions and explains SQL statements in natural language, and can generate recommendations based on your schema and metadata.
• Accelerating and modernizing databases: Duet AI in Cloud Spanner, AlloyDB and Cloud SQL, helps generate code to structure, modify, or query data using natural language.
• Security Operations: Duet AI is also coming to security products including Chronicle Security Operations, Mandiant Threat Intelligence, and Security Command Center, which can empower security professionals to more efficiently prevent threats, reduce toil in security workflows, and uplevel security talent.

Duet AI was designed using Google's comprehensive approach to help protect customers' security and privacy, as well as Google's AI principles. With Duet AI, your data is your data. User code, user inputs to Duet AI, and recommendations generated by Duet AI will not be used to train any shared models nor used to develop any products.

Simplify analytics at scale with a unified data and AI foundation
Data sits at the center of gen AI, which is why Google Cloud is bringing new capabilities to Google's Data and AI Cloud that will help unlock new insights and boost productivity for data teams. In addition to the launch of Duet AI, which assists data engineers and data analysts across BigQuery, Looker, Spanner, Dataplex, and Google Cloud's database migration tools, other important announcements include:

• BigQuery Studio: A single interface for data engineering, analytics, and predictive analysis, BigQuery Studio helps increase efficiency for data teams.
• AlloyDB AI: An integral part of AlloyDB, our PostgreSQL-compatible database service,  AlloyDB AI offers an integrated set of capabilities for easily building GenAI apps including high-performance and vector queries.
• New Data Cloud Partners: Partners, like Confluent, DataRobot, Dataiku, Datastax, Elastic, MongoDB, Neo4j, Redis, SingleStore, and Starburst are all launching new capabilities to help customers accelerate and enhance gen AI development with data.

Addressing top security challenges
Google Cloud is the only leading security provider that brings together the essential combination of frontline intelligence and expertise, a modern security operations platform, and a trusted cloud foundation, all infused with the power of gen AI, to help drive the security outcomes customers are looking to achieve. Today, Google Cloud announced:

• Mandiant Hunt for Chronicle: This service integrates the latest insights into attacker behavior from Mandiant's frontline experts with Chronicle Security Operations' ability to quickly analyze and search security data, helping customers gain elite-level support without the burden of hiring, tooling, and training.
• Agentless vulnerability scanning: These posture management capabilities in Security Command Center detect operating system, software, and network vulnerabilities on Compute Engine virtual machines.
• Network security advancements: Cloud Firewall Plus adds advanced threat protection and next-generation firewall (NGFW) capabilities to our distributed firewall service, powered by Palo Alto Networks; and Network Service Integration Manager allows network admins to easily integrate trusted third-party NGFW virtual appliances for traffic inspection.

Expanding our ecosystem
Google Cloud's ecosystem is already delivering real-world value for businesses with gen AI, and bringing new capabilities, powered by Google Cloud, to millions of users worldwide. Partners are also using Vertex AI to build their own features for customers – including Box, Canva, Salesforce, UKG, and many others. New partner announcements include:

• DocuSign is piloting new gen AI features, built with Vertex AI, for its billion-plus users. This includes a new "smart contract assistant" in DocuSign that can summarize, explain, and answer questions about complex contracts and other documents.
• SAP is building new solutions utilizing SAP data and Vertex AI that will help enterprises apply gen AI to important business use cases, like streamlining automotive manufacturing or improving sustainability.
• Workday's applications for Finance and HR are now live on Google Cloud and they are working with Google Cloud to develop new gen AI capabilities within the flow of Workday, as part of its multicloud strategy. This includes the ability to generate high-quality job descriptions and to bring Google Cloud gen AI to app developers via the skills API in Workday Extend, while helping to ensure the highest levels of data security and governance for customers' most sensitive information.

Partnership Will Enable OneValley to Provide Up to 1.1M Entrepreneurial Users with Algorithmic-Driven Web Content Scored for Reliability and Personalized to Help Them Grow Their Business

Seekr, a revolutionary artificial intelligence company specializing in transparent content evaluation, announced today it has entered into a strategic partnership with OneValley, a Silicon Valley-based global entrepreneurship platform that powers many of the world's top innovation, entrepreneurial and non-profit ecosystems. The partnership integrates Seekr's groundbreaking search capabilities into OneValley's information-sharing platform, enabling 1.1 million entrepreneurial users supported by OneValley to access algorithmic-driven news that is personalized and scored for reliability.

"The OneValley platform is already a treasure trove of high-value information for entrepreneurs, providing guidance and insights on everything from ideation and launch to growth and scaling. Seekr's content evaluation capabilities amplify that value proposition, taking the platform to the next level," said Rob Clark, President and Chief Technology Officer at Seekr. "Our technology enables the OneValley platform to identify relevant information, regardless of whether it lives on the platform or on the broader internet, score it for reliability, curate it so that it's tailored to the unique needs and interests of each individual entrepreneur, and finally serve it up to the entrepreneur on-demand."

"As OneValley continues to expand worldwide, this unique partnership with Seekr will provide our customers with the knowledge they need to win in their markets," said Nikhil Sinha, CEO of OneValley. "We further expect that usage on the platform will continue to grow and enhance the life cycle growth from startup to big business."

Seekr's AI-powered search and evaluation technology will be fully integrated into OneValley's online consumer platform, Passport, and its enterprise platform, PassportOS. The technology will enable every OneValley user to generate a bespoke flow of news and other relevant content tailored to meet their unique individual interests and needs.

Under the terms of the agreement, the platform will also feature a stream of real-time news and relevant information powered by Seekr. Additionally, Seekr will provide OneValley with a dynamic competitive analysis tool that provides entrepreneurs and startups with insights into competitor movements and a fuller understanding of how market trends are moving over time.

Performance data cultivated from BigCommerce merchants show automotive category is picking up speed online, while omnichannel selling, data analytics and car subscription services are navigating the future of automotive ecommerce

BigCommerce, a leading Open SaaS ecommerce platform for fast-growing and established B2C and B2B brands, today released its 2023 Global Ecommerce Report: Automotive that looks into the performance data of its merchants in the automotive category and how buyers and sellers are shaping online purchasing trends.

The automotive industry is quickly shifting gears to ecommerce, leveraging new strategies such as digital advertising, virtual showrooms, flexible payment options and online vehicle marketplaces. And in the future, omnichannel selling, data analytics and subscription services seem poised to become the next big trends for automotive ecommerce.

Key findings include:

• Q1 2023 data showed a 4.3% increase in gross merchandise volume (GMV) for automotive merchants compared to Q1 2022. Similar growth held true for automotive’s average order value (AOV), which saw a 5% increase between Q1 2022 and Q1 2023.
• Between Q1 2022 and Q1 2023, the automotive category saw a 9.1% increase in GMV, a 4.1% increase in AOV and a 4.8% increase in orders via iPhone. However, by the same measure, the automotive industry saw a 3.5% increase in AOV, zero change for GMV and a 4% decrease in orders via Android.
• When it comes to pure B2B selling, the automotive industry is showing notable YoY improvement. Automotive GMV was up 9.1% from Q1 2022, AOV was up 8.7%, while orders remained relatively flat.

How the automotive industry performed

If the trends of Q1 remain constant or improve, our data also shows the automotive category is poised for growth in 2023.

• Mobile sales experienced a slight increase
  Data revealed only a slight increase in mobile sales for the automotive category. Comparing Q1 2022 to Q1 2023, automotive merchants experienced a 5% increase in GMV, zero change in orders and a 4.1% increase in AOV. When comparing order sources, however, online sales via iPhone slightly outperformed sales via Android.
  
  Between Q1 2022 and Q1 2023, the automotive category saw a 9.1% increase in GMV, a 4.1% increase in AOV and a 4.8% increase in orders via iPhone. However, by the same measure, the automotive industry saw a 3.5% increase in AOV, zero change for GMV and a 4% decrease in orders via Android.

• Automotive merchants pick up speed with B2B
  When it comes to pure B2B selling, the automotive industry is showing notable YoY improvement. Automotive GMV was up 9.1% from Q1 2022, AOV was up 8.7%, while orders remained relatively flat.
  
  Hybrid B2B (merchants selling both B2C and B2B) numbers also showed some improvement. Automotive GMV was up 3.3% and AOV was up 7%, while orders were down 3.4%.

• Automotive sees growth in AMER and EMEA
  While still trailing overall ecommerce performance, the automotive category did experience slight growth in North America, with a 4.8% increase in YoY GMV and 5.2% increase in AOV.
  
  In EMEA, automotive merchants saw a 1.7% increase in GMV between Q1 2022 and Q1 2023, as well as a 5.1% increase in AOV, while orders decreased by 3.3%. In APAC, however, automotive GMV, orders and AOV all remained relatively flat.

Ecommerce trends and predictions shaping the automotive category

Automotive merchants are revving up their ecommerce strategies and show no signs of slowing. But as a newer retail category in the ecommerce space, the automotive industry has found some unique ways to excel online. Here are some notable ecommerce trends we found that are shaping the automotive category in 2023:

• Digital showrooms bring the dealership experience online
  With the help of new technologies, automotive brands are able to create an immersive ecommerce experience that mimics that of a dealership. Digital showrooms will not only enable users to customize the features, trim and model of their car, but also perform virtual test drives and demos. For instance, BigCommerce merchant BB Wheels is bringing the showroom online with their interactive Vehicle Visualizer tool which enables customers to easily visualize how a specific wheel will look on a particular vehicle.

• Digital advertising helps drive revenue
  While it’s still common to see automotive billboards and television ads, an increasing number of auto retailers are turning to social channels like Facebook, Google and Instagram to engage with customers and showcase new products. Not only can this increase brand awareness, but it also allows auto retailers to collect first-party data in order to better tailor the advertising experience for the customer.

• Convenient payment options to streamline the experience
  According to a survey of dealers by PwC, 66% of car dealers agreed that customers expect purchasing information in digital form, particularly regarding financing options. Needless to say, purchasing has been a major pain point for car shoppers, leading both online and traditional dealers to think outside the box when it comes to payment options. Mobile wallets, payment scheduling and contactless transactions are just a few of the payment solutions changing the game in auto retail today.

• Vehicle marketplaces rev up online sales
  By integrating with third-party marketplaces such as Amazon, O’Reilly Auto Parts, Walmart and even Ebay, automotive companies can take advantage of a customer base on platforms already proven to succeed. We see rising demand from online car buyers and third-party vehicle marketplaces will have positioned themselves to fill the gap left by traditional dealers.

Navigating the future of automotive ecommerce

While the automotive industry has already undergone a major transformation, the future holds even more potential. From data analytics to online subscription services, the future of automotive ecommerce is coming in full speed.

• Omnichannel selling creates a connected user experience
  While many automotive customers are moving online, there remains a clear need for the in-dealership experience — which doesn’t seem to be disappearing anytime soon. Thus, automotive retailers will need to implement a holistic omnichannel strategy that weaves together all business channels, both online and offline, to create a consistent and engaging customer experience.

• Data analytics provide customer insights
  Particularly for dealers selling both online and offline, data analytics can be a powerful tool for improving inventory management by reducing overstocking or understocking. Additionally, big data and predictive analytics can provide insight into customer behavior, such as how long a customer spends on a page, what they’re browsing and what products they’re adding to their carts.

• Subscription services disrupt car ownership
  Today, nearly all ecommerce industries offer some sort of subscription service. According to a survey by PwC, roughly 4 in 10 consumers say they would consider using a car subscription service for their next vehicle, and 71% of dealers claim that subscription services are a “viable business model for their franchisees. Not only do subscription business models provide a flexible alternative for the customer, but they also help create new revenue streams for the dealer.

“BigCommerce is trusted by more than 2,600 automotive sites across the world, and our merchant data shows the power ecommerce has to fuel an industry leaning into digital transformation to better engage and sell to their customers,” said Lisa Eggerton, chief marketing officer at BigCommerce. “The opportunity for growth and innovation is remarkable.”

Read the full report here. To explore BigCommerce’s automotive ecommerce solutions and how they help transform how to sell auto parts online, click here.

Methodology

BigCommerce’s automotive data is sourced directly from our merchants and was pulled on May 16, 2023. All data is global and pertains to all countries where BigCommerce merchants do business, unless otherwise noted.

All comparisons are congruent comparisons between the same number of existing stores dating back to the earliest period used in the comparison. For example, a comparison between Q1 of 2022 and Q1 2023 would use data only from BigCommerce stores that existed in Q1 2022, unless otherwise noted.

The Global Machine Learning (ML) Platforms market size is projected to reach US$ 31360 million by 2028, from US$ 3997.7 million in 2021, at a CAGR of 33.6% during 2022-2028.

"Machine Learning (ML) Market" Insights 2022 By Types, Applications, Regions and Forecast to 2028. The global Machine Learning (ML) market size is projected to reach multi million by 2028, in comparison to 2022, with unexpected CAGR during the forecast period, the Machine Learning (ML) Market Report Contains 102 Pages Including Full TOC, Tables & Figures, and Chart with In-depth Analysis Pre & Post COVID-19 Market Outbreak Impact Analysis & Situation by Region.

Machine Learning (ML) Market - Covid-19 Impact and Recovery Analysis:

We have been tracking the direct impact of COVID-19 on this market, as well as the indirect impact from other industries. This report analyzes the impact of the pandemic on the Machine Learning (ML) market from a Global and Regional perspective. The report outlines the market size, market characteristics, and market growth for Machine Learning (ML) industry, categorized by type, application, and consumer sector. In addition, it provides a comprehensive analysis of aspects involved in market development before and after the Covid-19 pandemic. Report also conducted a PESTEL analysis in the industry to study key influencers and barriers to entry.

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Machine Learning (ML) Market - Competitive and Segmentation Analysis:

This Machine Learning (ML) Market report offers detailed analysis supported by reliable statistics on sale and revenue by players for the period 2017-2022. The report also includes company description, major business, Machine Learning (ML) product introduction, recent developments and Machine Learning (ML) sales by region, type, application and by sales channel.

The major players covered in the Machine Learning (ML) market report are:

• Palantier
• MathWorks
• Alteryx
• SAS
• Databricks
• TIBCO Software
• Dataiku
• H2O.ai
• IBM
• Microsoft
• Google
• KNIME
• DataRobot
• RapidMiner
• Anaconda
• Domino
• Altair

Short Summery About Machine Learning (ML) Market :

The Global Machine Learning (ML) market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2021, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

Market Analysis and Insights: Global Machine Learning (ML) Platforms Market

The global Machine Learning (ML) Platforms market size is projected to reach US$ 31360 million by 2028, from US$ 3997.7 million in 2021, at a CAGR of 33.6% during 2022-2028.

Fully considering the economic change by this health crisis, Cloud-based accounting for % of the Machine Learning (ML) Platforms global market in 2021, is projected to value US$ million by 2028, growing at a revised % CAGR from 2022 to 2028. While Small and Medium Enterprises (SMEs) segment is altered to an % CAGR throughout this forecast period.

China Machine Learning (ML) Platforms market size is valued at US$ million in 2021, while the North America and Europe Machine Learning (ML) Platforms are US$ million and US$ million, severally. The proportion of the North America is % in 2021, while China and Europe are % and respectively, and it is predicted that China proportion will reach % in 2028, trailing a CAGR of % through the analysis period 2022-2028. Japan, South Korea, and Southeast Asia are noteworthy markets in Asia, with CAGR %, %, and % respectively for the next 6-year period. As for the Europe Machine Learning (ML) Platforms landscape, Germany is projected to reach US$ million by 2028 trailing a CAGR of % over the forecast period 2022-2028.

With industry-standard accuracy in analysis and high data integrity, the report makes a brilliant attempt to unveil key opportunities available in the global Machine Learning (ML) Platforms market to help players in achieving a strong market position. Buyers of the report can access verified and reliable market forecasts, including those for the overall size of the global Machine Learning (ML) Platforms market in terms of revenue.

Overall, the report proves to be an effective tool that players can use to gain a competitive edge over their competitors and ensure lasting success in the global Machine Learning (ML) Platforms market. All of the findings, data, and information provided in the report are validated and revalidated with the help of trustworthy sources. The analysts who have authored the report took a unique and industry-best research and analysis approach for an in-depth study of the global Machine Learning (ML) Platforms market.

Global Machine Learning (ML) Platforms Scope and Market Size

Machine Learning (ML) Platforms market is segmented by players, region (country), by Type and by Application. Players, stakeholders, and other participants in the global Machine Learning (ML) Platforms market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application for the period 2017-2028.

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Report further studies the market development status and future Machine Learning (ML) Market trend across the world. Also, it splits Machine Learning (ML) market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.

On the basis of product type this report displays the production, revenue, price and market share and growth rate of each type, primarily split into:

• Cloud-based
• On-premises

On the basis of the end users/applications this report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:

• Small and Medium Enterprises (SMEs)
• Large Enterprises

Machine Learning (ML) Market - Regional Analysis:

Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Machine Learning (ML) in these regions, from 2015 to 2027, covering

• North America (United States, Canada and Mexico)
• Europe (Germany, UK, France, Italy, Russia and Turkey etc.)
• Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)
• South America (Brazil, Argentina, Columbia etc.)
• Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Some of the key questions answered in this report:

• What is the global (North America, Europe, Asia-Pacific, South America, Middle East & Africa) sales value, production value, consumption value, import and export of Machine Learning (ML)?
• Who are the global key manufacturers of the Machine Learning (ML) Industry? How is their operating situation (capacity, production, sales, price, cost, gross, and revenue)?
• What are the Machine Learning (ML) market opportunities and threats faced by the vendors in the global Machine Learning (ML) Industry?
• Which application/end-user or product type may seek incremental growth prospects? What is the market share of each type and application?
• What focused approach and constraints are holding the Machine Learning (ML) market?
• What are the different sales, marketing, and distribution channels in the global industry?
• What are the upstream raw materials and manufacturing equipment of Machine Learning (ML) along with the manufacturing process of Machine Learning (ML)?
• What are the key market trends impacting the growth of the Machine Learning (ML) market?
• Economic impact on the Machine Learning (ML) industry and development trend of the Machine Learning (ML) industry.
• What are the market opportunities, market risk, and market overview of the Machine Learning (ML) market?
• What are the key drivers, restraints, opportunities, and challenges of the Machine Learning (ML) market, and how they are expected to impact the market?
• What is the Machine Learning (ML) market size at the regional and country-level?

Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.

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Detailed TOC of Global Machine Learning (ML) Market Research Report 2022

1 Machine Learning (ML) Market Overview

1.1 Product Overview and Scope of Machine Learning (ML)
1.2 Machine Learning (ML) Segment by Type
1.2.1 Global Machine Learning (ML) Market Size Growth Rate Analysis by Type 2022 VS 2028
1.3 Machine Learning (ML) Segment by Application
1.3.1 Global Machine Learning (ML) Consumption Comparison by Application: 2022 VS 2028
1.4 Global Market Growth Prospects
1.4.1 Global Machine Learning (ML) Revenue Estimates and Forecasts (2017-2028)
1.4.2 Global Machine Learning (ML) Production Capacity Estimates and Forecasts (2017-2028)
1.4.3 Global Machine Learning (ML) Production Estimates and Forecasts (2017-2028)
1.5 Global Market Size by Region
1.5.1 Global Machine Learning (ML) Market Size Estimates and Forecasts by Region: 2017 VS 2021 VS 2028
1.5.2 North America Machine Learning (ML) Estimates and Forecasts (2017-2028)
1.5.3 Europe Machine Learning (ML) Estimates and Forecasts (2017-2028)
1.5.4 China Machine Learning (ML) Estimates and Forecasts (2017-2028)
1.5.5 Japan Machine Learning (ML) Estimates and Forecasts (2017-2028)

2 Market Competition by Manufacturers
2.1 Global Machine Learning (ML) Production Capacity Market Share by Manufacturers (2017-2022)
2.2 Global Machine Learning (ML) Revenue Market Share by Manufacturers (2017-2022)
2.3 Machine Learning (ML) Market Share by Company Type (Tier 1, Tier 2 and Tier 3)
2.4 Global Machine Learning (ML) Average Price by Manufacturers (2017-2022)
2.5 Manufacturers Machine Learning (ML) Production Sites, Area Served, Product Types
2.6 Machine Learning (ML) Market Competitive Situation and Trends
2.6.1 Machine Learning (ML) Market Concentration Rate
2.6.2 Global 5 and 10 Largest Machine Learning (ML) Players Market Share by Revenue
2.6.3 Mergers & Acquisitions, Expansion

3 Production Capacity by Region
3.1 Global Production Capacity of Machine Learning (ML) Market Share by Region (2017-2022)
3.2 Global Machine Learning (ML) Revenue Market Share by Region (2017-2022)
3.3 Global Machine Learning (ML) Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.4 North America Machine Learning (ML) Production
3.4.1 North America Machine Learning (ML) Production Growth Rate (2017-2022)
3.4.2 North America Machine Learning (ML) Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.5 Europe Machine Learning (ML) Production
3.5.1 Europe Machine Learning (ML) Production Growth Rate (2017-2022)
3.5.2 Europe Machine Learning (ML) Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.6 China Machine Learning (ML) Production
3.6.1 China Machine Learning (ML) Production Growth Rate (2017-2022)
3.6.2 China Machine Learning (ML) Production Capacity, Revenue, Price and Gross Margin (2017-2022)
3.7 Japan Machine Learning (ML) Production
3.7.1 Japan Machine Learning (ML) Production Growth Rate (2017-2022)
3.7.2 Japan Machine Learning (ML) Production Capacity, Revenue, Price and Gross Margin (2017-2022)

4 Global Machine Learning (ML) Consumption by Region
4.1 Global Machine Learning (ML) Consumption by Region
4.1.1 Global Machine Learning (ML) Consumption by Region
4.1.2 Global Machine Learning (ML) Consumption Market Share by Region
4.2 North America
4.2.1 North America Machine Learning (ML) Consumption by Country
4.2.2 United States
4.2.3 Canada
4.3 Europe
4.3.1 Europe Machine Learning (ML) Consumption by Country
4.3.2 Germany
4.3.3 France
4.3.4 U.K.
4.3.5 Italy
4.3.6 Russia
4.4 Asia Pacific
4.4.1 Asia Pacific Machine Learning (ML) Consumption by Region
4.4.2 China
4.4.3 Japan
4.4.4 South Korea
4.4.5 China Taiwan
4.4.6 Southeast Asia
4.4.7 India
4.4.8 Australia
4.5 Latin America
4.5.1 Latin America Machine Learning (ML) Consumption by Country
4.5.2 Mexico
4.5.3 Brazil

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5 Segment by Type
5.1 Global Machine Learning (ML) Production Market Share by Type (2017-2022)
5.2 Global Machine Learning (ML) Revenue Market Share by Type (2017-2022)
5.3 Global Machine Learning (ML) Price by Type (2017-2022)
6 Segment by Application
6.1 Global Machine Learning (ML) Production Market Share by Application (2017-2022)
6.2 Global Machine Learning (ML) Revenue Market Share by Application (2017-2022)
6.3 Global Machine Learning (ML) Price by Application (2017-2022)

7 Key Companies Profiled
7.1 Company
7.1.1 Machine Learning (ML) Corporation Information
7.1.2 Machine Learning (ML) Product Portfolio
7.1. CMachine Learning (ML) Production Capacity, Revenue, Price and Gross Margin (2017-2022)
7.1.4 Company’s Main Business and Markets Served
7.1.5 Company’s Recent Developments/Updates

8 Machine Learning (ML) Manufacturing Cost Analysis
8.1 Machine Learning (ML) Key Raw Materials Analysis
8.1.1 Key Raw Materials
8.1.2 Key Suppliers of Raw Materials
8.2 Proportion of Manufacturing Cost Structure
8.3 Manufacturing Process Analysis of Machine Learning (ML)
8.4 Machine Learning (ML) Industrial Chain Analysis

9 Marketing Channel, Distributors and Customers
9.1 Marketing Channel
9.2 Machine Learning (ML) Distributors List
9.3 Machine Learning (ML) Customers

10 Market Dynamics
10.1 Machine Learning (ML) Industry Trends
10.2 Machine Learning (ML) Market Drivers
10.3 Machine Learning (ML) Market Challenges
10.4 Machine Learning (ML) Market Restraints

11 Production and Supply Forecast
11.1 Global Forecasted Production of Machine Learning (ML) by Region (2023-2028)
11.2 North America Machine Learning (ML) Production, Revenue Forecast (2023-2028)
11.3 Europe Machine Learning (ML) Production, Revenue Forecast (2023-2028)
11.4 China Machine Learning (ML) Production, Revenue Forecast (2023-2028)
11.5 Japan Machine Learning (ML) Production, Revenue Forecast (2023-2028)

12 Consumption and Demand Forecast
12.1 Global Forecasted Demand Analysis of Machine Learning (ML)
12.2 North America Forecasted Consumption of Machine Learning (ML) by Country
12.3 Europe Market Forecasted Consumption of Machine Learning (ML) by Country
12.4 Asia Pacific Market Forecasted Consumption of Machine Learning (ML) by Region
12.5 Latin America Forecasted Consumption of Machine Learning (ML) by Country

13 Forecast by Type and by Application (2023-2028)
13.1 Global Production, Revenue and Price Forecast by Type (2023-2028)
13.1.1 Global Forecasted Production of Machine Learning (ML) by Type (2023-2028)
13.1.2 Global Forecasted Revenue of Machine Learning (ML) by Type (2023-2028)
13.1.3 Global Forecasted Price of Machine Learning (ML) by Type (2023-2028)
13.2 Global Forecasted Consumption of Machine Learning (ML) by Application (2023-2028)
13.2.1 Global Forecasted Production of Machine Learning (ML) by Application (2023-2028)
13.2.2 Global Forecasted Revenue of Machine Learning (ML) by Application (2023-2028)
13.2.3 Global Forecasted Price of Machine Learning (ML) by Application (2023-2028)

14 Research Finding and Conclusion

15 Methodology and Data Source
15.1 Methodology/Research Approach
15.1.1 Research Programs/Design
15.1.2 Market Size Estimation
15.1.3 Market Breakdown and Data Triangulation
15.2 Data Source
15.2.1 Secondary Sources
15.2.2 Primary Sources
15.3 Author List
15.4 Disclaimer

Continued….

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Salesforce (NYSE: CRM), the global leader in CRM, today announced that companies across industries and regions are seeing, on average, an estimated 25%* savings on IT costs and a 26%* increase in employee productivity using Salesforce, according to a survey of more than 3,500 customers.

Digital transformation is critical to navigating the growing economic turbulence we are experiencing today. While 74% of CEOs expect economic conditions to worsen in the short term, there are shades of optimism, with 83% of CEOs expressing confidence in the resilience of their companies to withstand economic jolts.

But maintaining a durable, resilient business that can drive success now is no small feat. Companies must consolidate and reduce complexity and automate workflows across their technology stack. With Salesforce Customer 360, the world's #1 CRM, the entire organization can work on one trusted platform in real time — giving every employee a single shared view of the customer to drive higher levels of productivity and customer loyalty at a lower overall cost to serve.

With automation, intelligence, and real-time data built directly into best-in-class applications for sales, service, marketing, commerce, analytics, and IT teams, now every company can exceed customer expectations. And, making the Salesforce Customer 360 more accessible than ever, Salesforce today released the Sales Productivity Bundle and Service Efficiency Bundle to help customers automate to lower costs, drive efficient growth, and consolidate their front office on a single trusted platform.

"CEOs and executive teams around the world are laser focused on delivering success now by connecting with their customers in new, simpler and more cost effective ways," said David Schmaier, Salesforce President and Chief Product Officer. "We are in a challenging economic climate but these times of change also provide opportunities for companies to play to win and transform their industry. The investments they make now will determine their success today and for the next decade."

Customers report cost savings and increased efficiencies when using Salesforce
Powering small businesses, the world's largest enterprises, and everything in between, Salesforce helps companies of all sizes across all industries rally every employee around a single view of every customer. With Salesforce, companies can increase productivity and automation and reduce the number of technology vendors they need to manage their business, saving time and money and reducing complexity.

Salesforce surveyed more than 3,500 customers to better understand how the technology was impacting their business. On average, organizations that use Salesforce estimate the following benefits*:

The right solutions for success right now
Salesforce is delivering success now for companies across every industry.

ADT Delivers Premium Customer Experience with Salesforce
A leader in American home and commercial security, ADT strives to deliver safe, smart, and sustainable security solutions. With Salesforce, ADT has been able to leverage automation and intelligence to drive cost savings, deliver faster customer support, and increased agent productivity — in fact, ADT has been able to move 40% of service appointments to virtual.

Elekta Saves $875,000 with Salesforce
A leading innovator of precision radiation therapy solutions, Elekta strives to deliver outcome-driven and cost-efficient solutions that meet patient needs around the globe. With Salesforce, Elekta has been able to improve patient care in a timely and cost-efficient way, saving over $875,000 in training, onboarding, and IT costs. 

SmartRent Saves $300,000 and 120 Hours with Salesforce
A leading smart home solutions provider, SmartRent is laser focused on driving automation and efficiency for its customers. SmartRent leverages Salesforce's automation capabilities to eliminate manual tasks and streamline processes, saving $300,000, increasing employee retention by 92%, and saving employees 120 hours in onboarding time.

Schneider Electric Saves $2.7 Million in IT Costs with Salesforce
The global digital transformation leader in energy management and automation, Schneider Electric, is transforming the way the world uses energy. Using Salesforce, Schneider Electric has been able to drive efficiency across the company — enabling their sales reps to close deals 30% faster and save $2.7 million in IT costs over a three-year period.

The right solutions for success now
Until Dec. 31, 2022, Salesforce is offering introductory pricing for the Sales Productivity Bundle and Service Efficiency Bundle to help customers significantly reduce costs and increase productivity on one connected platform.

More information:

• Learn about the Sales Productivity Bundle.
• Learn about the Service Efficiency Bundle.
• Read more Salesforce customer success stories here.

* Source: 2022 Salesforce Success Metrics Global Highlights study.
Data is from a survey of 3,706 Salesforce customers across the US, Canada, the UK, Germany, France, Australia, India, Singapore, Japan and Brazil conducted between June 8 and June 21, 2022. Results were aggregated to determine average perceived customer value from the use of Salesforce. Respondents were sourced and verified through a third-party B2B panel. Sample sizes may vary across metrics.

Super Apps Holdings and Technology & Telecommunication Acquisition Corporation Enter into a Merger Agreement

• Transaction values Super Apps at an estimated pro forma enterprise value of $1.1 billion upon completion
• With operations based in Malaysia, Super Apps will have a geographic advantage for expanding into the ASEAN market
• Super Apps will partner with MobilityOne Sdn. Bhd., a fintech technology company in the payment systems market in Malaysia. Together with OneShop Retail, the combined company is position to be a leading payments systems provider in the ASEAN market
• The combined company will be named TETE Technologies Inc. and will apply for listing on the Nasdaq under the ticker TETE
• Super Apps CEO Loo See Yuen and existing management team to lead the combined company while gaining new board members, Tek Che Ng and Loke Chow Wing, from the Technology & Telecommunication Acquisition Corporation team

Super Apps Holdings Sdn Bhd, a Malaysian private limited company (“Super Apps” or the “Company”), and Technology & Telecommunication Acquisition Corporation (“TETE”) (Nasdaq: TETE, TETEU and TETEW), a special purpose acquisition company, today announced that they have entered into a definitive agreement and plan of merger (the “Merger Agreement”) for a business combination (the “Business Combination”) that will result in Super Apps becoming a publicly listed company. Upon closing of the transaction, the combined company will be named TETE Technologies Inc. and is expected to remain listed on the Nasdaq Stock Market under the ticker symbol, "TETE". The transaction reflects an estimated pro forma enterprise value for the combined company of approximately $1.1 billion.

Super Apps entered into a share sale agreement with MobilityOne Sdn Bhd (“MobilityOne”), a wholly-owned subsidiary of AIM quoted MobilityOne Limited, pursuant to which Super Apps agreed to purchase 60% of MobilityOne’s ownership interest in OneShop Retail Sdn. Bhd. (“OneShop Retail”) in a transaction which will close prior to the consummation of the Business Combination. MobilityOne has developed an end-to-end e-commerce solution which connects various service providers across several industries such as banking, telecommunication and transportation through multiple distribution channels such as electronic data capture terminals, short messaging services, automated teller machines and Internet banking services.

MobilityOne currently has ownership of the intellectual property that OneShop Retail uses in its operations and, in connection with the closing of the Business Combination, MobilityOne will grant OneShop Retail a long term license for use of such intellectual property. MobilityOne’s technology platform is flexible, scalable and has been designed to facilitate cash, debit card and credit card transactions (according to the device) from multiple devices while controlling and monitoring the distribution of different products and services.

Super Apps and MYISCO Sdn Bhd (“MYISCO”), a wholly owned subsidiary of MyAngkasa Digital Services Sdn Bhd (“MDS”), a Malaysian private limited company led by Angkatan Koperasi Kebangsaan Malaysia (“ANGKASA”), entered into a collaboration agreement, which shall become effective upon closing of the Business Combination, allowing OneShop Retail, as the authorized bill payment collection and credit lending agency of ANGKASA, to operate its payment collection system through ANGKASA’s authorized dealers for the collection and remission of any payment of bills via cash payment, credit card, debit card or cheque. ANGKASA currently facilitates the monthly salary disbursements of its members under its salary deduction scheme.

Based upon the Company’s anticipated collaboration with MYISCO and other potential collaborations, the combined company projects revenue of approximately $348 Million for the financial year ending December 31, 2023.

The proceeds from this transaction will enable Super Apps to build out its technology infrastructure to support demand from blue chip customers in the fast-growing e-commerce payment solutions market and enhance revenue.

Loo See Yuen, Chief Executive Officer of Super Apps, commented, "We are excited to enter the public markets through our business combination with TETE. The proceeds from the business combination, combined with our leadership team’s significant fintech industry experience, will allow Super Apps to accelerate growth in revenue through the expansion of its workforce, including sales and marketing headcount. We believe this transaction will enable us to continue investing in our technology infrastructure and deliver on our aspirations to be the unrivaled payment systems provider in the ASEAN market."

Mr. Ng Tek Che, the Chairman and CEO of TETE, added, “From the many companies under consideration by TETE, our goal was to find a company with a stand-out technology that met our criteria for investing in long-term sustainability for the ASEAN market.

The seamless payment ecosystem is a growing market, and Super Apps plans to utilise digital technologies to enhance the revenue of the combined company by leveraging, through the collaboration agreement between Super Apps and MYISCO Sdn. Bhd., the large database of end users from MYISCO. The strategy fits perfectly with TETE’s strategy to support enterprises utilizing digitalization and big data analytics to improve outcomes. We believe this transaction will create value for our existing and new shareholders on a sustainable, long-term basis.”

As part of the deal, Super Apps will retain its experienced management team, led by CEO Loo See Yuen, while gaining new board members, and Loke Chow Wing, from the TETE team.

Transaction Overview

Pursuant to the Merger Agreement, Super Apps will merge with TETE Technologies Sdn Bhd, a Malaysian private limited company and wholly owned subsidiary of TETE, with Super Apps surviving and TETE acquiring 100% of the equity securities of Super Apps. In exchange for their equity securities, the shareholders of Super Apps will receive an aggregate number of ordinary shares of TETE (the "Merger Consideration") with an aggregate value equal to: (a) one billion one hundred million U.S. Dollars ($1,100,000,000), minus (b) any Closing Net Indebtedness (as defined in the Merger Agreement), of which $235,000,000 will be paid at the closing of the Business Combination with the remaining $865,000,000 subject to the earn-out provisions set forth in the Merger Agreement.

The Business Combination has been approved by the boards of directors of each of TETE and Super Apps. The Business Combination will require the approval of the shareholders of TETE and Super Apps and is subject to other customary closing conditions, including a proxy statement being filed with and cleared by the U.S. Securities and Exchange Commission. The transaction is expected to close in the first half of 2023.

Advisors

ARC Group Limited is acting as sole financial advisor to TETE. Loeb & Loeb LLP is acting as legal counsel to TETE. Jenny Chen-Drake is acting as legal counsel for the Company.

According to Fortune Business Insights, the global SaaS Market Size is projected to hit USD 716.52 Billion in 2027, at CAGR of 27.5% during forecast period [2022-2027]

The global SaaS Market Size is expected to gain momentum by reaching USD 716.52 billion by 2028 while exhibiting CAGR of 27.5% between 2022 to 2028. In its report titled “SaaS Market Share, 2022-2028,” Fortune Business Insight mentions that the market stood at USD 113.82 billion in 2020 and USD 130.69 billion in 2021.

SaaS is one of the primary components of cloud computing. Companies and organizations are using the SaaS model in varied applications such as conferencing, CRM, salesforce automation, web content management, and others. Thus with the increasing demand, the market is expected to grow substantially during the forecast period [2021-2028]. For instance, in June 2021, Kylas announced the launch of an Enterprise-Grade SaaS CRM Product for the Indian market to support cloud computing.

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Driving Factors

Increasing Investment made by End-use Enterprises to Drive Market Growth

The increasing investment made by end-use enterprises on the cloud-based solution is expected to drive the SaaS Market growth in the upcoming years. For instance, a report by Gartner suggests that end users are spending for cloud application services is projected to reach around USD 102.80 billion in 2020.

Additionally, the prominent players in the software as a service market are focused on increasing their investment to advance their product portfolio. For Instance, In September 2020, Accenture Plc made an investment of USD 3.00 billion and launched Cloud First.  This investment assisted users to gain access to Cloud-First across various industries and speed up their digital transformation to generate greater value at speed and scale.

Regional Insights

North America to Lead Backed by Existence of Vital Players in Region

North America is anticipated to remain at the forefront and hold the highest software as a service market share during the forecast period owing to the increasing application and investment done on SaaS by end-users across industries such as healthcare, retail & consumer goods, and others.  Additionally, the presence of major SaaS providers such as IBM Corporation, Microsoft Corporation, Oracle Corporation, Salesforce, Inc., in the U.S. and Canada is expected to promote the regional market.  The region’s market stood at USD 57.30 billion in 2020.

Asia Pacific is expected to display considerable software as service market share in upcoming years, owing to the presence of large enterprises in nations such as China, India, Japan, and Australia. Additionally, the growing venture capital investments to adopt SaaS platforms is promoting the regional market.

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Market Segmentation:

By deployment type, the market is divided into public cloud, private cloud, and hybrid cloud. By application, it is segmented into, customer relationship management(CRM), enterprise resource planning, content, collaboration & communication, business intelligence & analytics, human capital management, and others.

Based on the application, the CRM segment held a market share of 25.1% in 2020. This is attributable to the deployment of SaaS CRM across organizations to manage their contacts, team management, simplify processes, monitor agreements, develop sales pipelines, build relationships with potential and current customers, and others.

By industry, it is divided into BFSI, retail & consumer goods, healthcare, education, manufacturing, travel & hospitality, and others. Finally, based on region, the market is categorized into North America, Europe, Asia Pacific, the Middle East & Africa and South America.

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Pre and Post COVID-19 Impact

The pandemic crippled the global economy, however, amid the crisis demand for cloud computing increased. Thus, key players made full use of this crisis as an opportunity to come up with strategies to restructure their business model.  For instance, in May 2021 Microsoft Corporation collaborated with Aera Technology, an automation company that offers digital technology solutions. This collaboration is expected to help to integrate Microsoft Azure’s digital twins with Aera's cognitive operating system to come up with a virtual representation of a physical entity in the automation process.

What does the Report Provide?

The market report for software as a service provides a methodical evaluation of various factors such as the important drivers and restraints that will affect growth. Moreover, the report offers insights into the regional survey that covers diverse regions, backing the growth of the market. It involves the competitive landscape that includes the leading companies and the embracement of effective stratagem to present novel products, declare collaborations, and mergers to aid market growth.

Competitive Landscape

Key Players to New Launches to Strengthen Market Growth

The market is combined by prime companies determining to preserve their position by concentrating on novel launches, collaborations & mergers as well as procurements. Such tactics taken up by vital players are anticipated to reinforce its market opportunities.

Industry Development:

July 2021: Tata Consultancy Services (TCS) announced the launch of Jile 5.0. It is an updated version of SaaS-based corporate agile application. This new solution will help businesses to offer large-scale development needs across numerous remote teams.

Companies Profiled in SaaS Market Research Report:

• Microsoft Corporation (New Mexico U.S.)
• Salesforce.com, Inc. (California, U.S.)
• Oracle Corporation (California, U.S.)
• IBM Corporation (New York, U.S.)
• Accenture Plc. (Ireland)
• OutSystems - Software em Rede, S.A. (Massachusetts, U.S.)
• Cisco Systems, Inc. (California, U.S.)
• Hewlett Packard Enterprise Company (Texas, U.S.)
• GitLab, Inc. (California, U.S.)      
• Alphabet Inc. (Google LLC) (California, U.S.)

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Major Points in TOC:

• Introduction
  • Definition, By Segment
  • Research Methodology/Approach
  • Data Sources
• Key Takeaways
• Market Dynamics
  • Macro and Micro Economic Indicators
  • Drivers, Restraints, Opportunities and Trends
  • Impact of COVID-19
    • Short-term Impact
    • Long-term Impact
• Competition Landscape
  • Business Strategies Adopted by Key Players
  • Consolidated SWOT Analysis of Key Players
• Global Market Share Analysis and Matrix, 2020
• Key Market Insights and Strategic Recommendations
• Profiles of Key Players
  • Overview
    • Key Management
    • Headquarters etc.
  • Offerings/Business Segments
  • Key Details
    • Employee Size
    • Key Financials
      • Past and Current Revenue
      • Geographical Share
      • Business Segment Share
  • Recent Developments
• Annexure / Appendix
  • Global SaaS Market Size Estimates and Forecasts (Quantitative Data), By Segments, 2017-2028
    • By Deployment Type (USD)
      • Public Cloud
      • Private Cloud
      • Hybrid Cloud
    • By Application (USD)
      • Customer Relationship Management (CRM)
      • Enterprise Resource planning (ERP)
      • Content, Collaboration & Communication
      • BI & Analytics
      • Human Capital Management
      • Others
    • By Industry (USD)
      • Banking, Financial Services And Insurance (BFSI)
      • Retail & Consumer Goods
      • Healthcare
      • Education
      • Manufacturing
      • Travel & Hospitality
      • Others
    • By Region (USD)
      • North America
      • South America
      • Europe
      • Middle East & Africa
      • Asia Pacific
  • North America SaaS Market Size Estimates and Forecasts (Quantitative Data), By Segments, 2017-2028

TOC Continued…!

What industry is SaaS?

By industry, it is divided into BFSI, retail & consumer goods, healthcare, education, manufacturing, travel & hospitality, and others. Finally, based on region, the market is categorized into North America, Europe, Asia Pacific, the Middle East & Africa and South America.

What is the market size of SaaS?

According to a report from Fortune Business Insights, the global SaaS market size was valued at $113.82 billion in 2020, and is projected to reach $716.52 billion by 2028, growing at a CAGR of 27.5% from 2021 to 2028.