Machine learning is a computing technology that enables computers to explore, learn, and modify their analytical functionalities when exposed to new data sets, without being explicitly programmed. It is also used to capture data and consequently run discrete modelers to create patterns for subsequent processing, analysis, and interpretations required for real-time decision making.

The global machine learning softwares market is expected to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The main driving factors for machine learning softwares market are proliferation in data generation and technological advancement.

In the services segment, the managed service segment is expected to grow at a higher CAGR, whereas professional service segment is expected to be a larger contributor during the forecast period. The managed service is said to be growing faster, as it helps organizations to increase efficiency and save costs for managing on-demand best machine learning softwares services. The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of best machine learning softwares.

In the deployment mode segment, the cloud deployment mode is expected to hold the largest market share and grow at the highest CAGR during the forecast period. Flexibility, automated software updates, disaster recovery through cloud-based backup systems, increased collaboration, monitoring document version control, and data loss prevention with robust cloud storage facilities are some of the crucial benefits that have resulted in the adoption of cloud-based delivery models for best machine learning softwares and services.

In the organization size segment, the large enterprises segment is expected to have the largest market share, whereas the SMEs segment is expected to grow at the highest CAGR during the forecast period. The rapidly emerging and highly active SMEs have increased the adoption of best machine learning softwares and services globally, as a result of the growing digitization and increased cyber risks to critical business information and data. Large enterprises have been heavily adopting best machine learning softwares to extract the required information from a large amount of data and forecast the outcome of various problems.

In the verticals segment, the Banking, Financial Services, and Insurance (BFSI) vertical is expected to be the highest contributor, whereas the healthcare and life sciences vertical is projected to grow at highest CAGR during the forecast period. Both the verticals generate data in a huge amount every second, and there is accelerated demand for data management technologies such as machine learning and predictive analytics in order to extract business critical insights from this ever-increasing data. The other industry verticals, such as manufacturing, telecommunication, energy and utilities, retail, government and defense are contributing significantly to the best machine learning softwares market. These verticals are also expected to witness significant growth rates during the forecast period due to the increased concerns for managing the complex business processes with improved efficiency and lowering the overall costs.

The global machine learning softwares market has been segmented on the basis of regions into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is estimated to be the largest revenue-generating region. This is mainly because, in the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. These regions have the most competitive and rapidly changing best machine learning softwares market in the world. The APAC region is expected to be the fastest-growing region in the best machine learning softwares market. The increased awareness for business productivity, supplemented with competently designed best machine learning softwares offered by vendors present in the APAC region, has led APAC to become a highly potential market.

The major issue faced by most of the organizations while incorporating best machine learning softwares in their business processes is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.

The major vendors that offer the best machine learning softwares across the globe are Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US).

VISIONARY LEADERS

Vendors who fall into this category in the best machine learning softwares market receive high scores for most of the evaluation criteria. They have a strong and established product portfolio and a very strong market presence. They provide mature and reputable machine learning softwares. They also have strong business strategies. Microsoft, IBM Corporation, SAP SE, SAS Institute Inc., Google Inc., and Amazon Web Services, Inc. are the vendors who fall into the visionary leaders’ category.

INNOVATORS

Innovators in the MicroQuadrant are vendors that have demonstrated substantial product innovations in the best machine learning softwares market as compared to their competitors. They have a much-focused product portfolio. However, they do not have a very strong growth strategy for their overall business. Baidu, BigML, FICO, HPE, Intel Corporation, KNIME.com AG, RapidMiner., Dataiku and Angoss Software Corporation are the vendors who fall into the innovator's category.

DYNAMIC DIFFERENTIATORS

They are established vendors in the best machine learning softwares market with very strong business strategies. However, they are low in their product portfolios. They focus on specific type of technology related to the product. Dell and Oracle Corporation are the vendors who fall into the dynamic differentiators’ category.

EMERGING COMPANIES

They are vendors with niche product offerings in the best machine learning softwares market and are starting to gain their position in the market. They do not have much strong business strategies, as compared to other established vendors. They might be new entrants in the market and require some more time to get significant traction in the market. H2O.ai, Alpine Data, Domino Data Lab, Luminoso Technologies, Skytree, Fractal Analytics, TIBCO Software, and Teradata are the vendors who fall into the emerging companies’ category.

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN BFSI

The consistently evolving BFSI industry is data-driven, which stores huge volumes of unstructured information, most of which is not utilized properly. With the introduction of AI, best machine learning softwares, and other advanced analytics, banking institutes are now able to tap into the unstructured data that provides them with key insights about the consumers. Progress in big data and analytics has led to the emergence of new products, services, and solutions. Moreover, after the integration of best machine learning softwares with these services and solutions, the banking and financial services have become agiler and smarter. Additionally, fraud detection helps in identifying patterns in clustered data and has the ability to differentiate fraudulent activity from normal activity. The best machine learning softwares are also used to provide personalized product offering based on the patterns recognized from the user activities, which eventually leads to customer retention. The major applications of BFSI using best machine learning softwares include fraud and risk management, customer segmentation, sales and marketing campaign management, investment prediction, digital assistance, others (compliance management and credit underwriting).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN HEALTHCARE AND LIFE SCIENCES

Machine learning is a rapidly growing trend in the healthcare and life sciences industry, as it makes processes more efficient and forecasts future outcomes for patients. These services are used to understand customer behavior and derive in-depth insights into clinical performance. They also assist medical experts to analyze data in order to recognize trends that may lead to better diagnosis and treatment of patients. Many companies are using various machine learning softwares to cater the requirements and solve problems of the medical experts. As best machine learning softwares are capable of delivering real-time data, companies are applying machine learning with medical wearable devices, which allow the medical experts to be more accurate and predict diagnosis, owing to which they can intervene sooner. Moreover, as the received data sets are dynamic, the experts are able to detect changes more quickly. Apart from this, machine learning SMEs are using image analysis to provide faster medical diagnostic. The major applications of healthcare and life sciences sector using machine learning softwares include disease identification and diagnosis, image analytics, personalized treatment, drug discovery/manufacturing, and others (clinical trial research and epidemic outbreak prediction).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN RETAIL

Machine learning is being used on a large scale in the retail industry. Retail organizations have huge amounts of data, such as everyday transactions, customer information, preferences, product information, and sales. This huge data has to be processed and analyzed for key insights and to enhance and fasten the decision-making process, thus helping organizations to outline strategies for new product development and revenue optimization. Marketing campaigns and promotions are planned according to the customers and industry trends. With the help of machine learning algorithms, the retailer can predict the data to renew pricing decisions to match the interest of customers. Retailers are capable of adjusting their value chain adequately, which eventually permits them to deliver the right margins. The retail sector uses machine learning, majorly for inventory planning, recommendation engines, upsells and cross channel marketing, segmentation and targeting, and others (customer ROI and lifetime value and customization management).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN TELECOMMUNICATIONS

Telecommunication is one of the fastest-growing verticals and has undergone various transformations over time. The ever-changing preferences of customers and increased usage of the internet, laptops, tablets, and mobile phones mandate the service providers to understand customer preferences, behaviors, and trends in the industry. Telecom operators have to manage huge amounts of data generated from user networks and traffic data. With the help of real-time data collection and predictive analytics, the large amounts of data generated from the telecommunications vertical can be leveraged. Best machine learning softwares are used in this vertical for important purposes, such as customer churn reduction, where companies develop machine learning algorithms that predict the chance of customer churn by considering thousands of unique features and factors, including customer profiles and patterns of information. The telecommunications sector uses machine learning, mainly for customer analytics, network security, predictive maintenance, network optimization, and others (digital assistance/contact center analytics and marketing campaign analytics).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN GOVERNMENT AND DEFENSE

Government agencies are investing heavily into advancements in the field of AI. For instance, the U.S. Defense Department’s R&D arm called Defense Advanced Research Projects Agency (DARPA) is offering to fund projects, which will ease up the extremely complex task of building models for best machine learning softwares. With respect to cybersecurity, machine learning has an important significance. Cybersecurity has become a huge challenge in the modern world as many major corporations, individuals, and government agencies have become targets of cyber fraud. Monitoring critical networks, such as government clouds and websites for different types of intrusion detection systems and analysis of social network traffic and malware are some of the best machine learning softwares applications. Machine learning is carried out in various segments of this vertical, including threat intelligence, autonomous defense systems, and others (sustainability and operational analytics).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN MANUFACTURING

The manufacturing sector has started to incorporate machine learning throughout the production process. Machine learning and predictive algorithms are being used to plan machine maintenance adaptively rather than on a fixed schedule. Furthermore, quality control processes are becoming automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject the defected ones. The best machine learning softwares being developed are iterative, designed to learn continually, and find optimized outcomes. The algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months. The manufacturing sector uses machine learning, majorly for predictive maintenance, revenue estimation, demand forecasting, supply chain management, and others (root cause analysis and telematics).

BEST MACHINE LEARNING SOFTWARES : APPLICATIONS IN ENERGY AND UTILITIES

The energy and utilities vertical includes energy, water, and oil and gas. It is one of the largest industries serving a huge customer base. The adoption of best machine learning softwares is rapidly changing the operational and performance model of the energy and utilities industry. The need for new data sources, new programs, and efficient management of resources as well as increasing competition from alternative energy providers are the growth drivers for this market. The smart grid backed with best machine learning capabilities has been trending in the energy and utilities sector lately. It gives access to the customer usage data that can be analyzed with the help of best machine learning softwares to secure market competitiveness, increase energy efficiency, manage risks, and lower operational costs. Analytics solutions also enable oil and gas exploration companies to analyze the data about oil and gas reserves, which helps them to increase efficiency and lower the operational risks and costs.

Last updated on: Nov 15, 2019

Machine Learning Software Quadrant

Comparing 38 vendors in Machine Learning Software across 114 criteria.

Find the best Machine Learning Software solution for your business, using ratings and reviews from buyers, analysts, vendors and industry experts

EVALUATION CRITERIA

Below criteria are most commonly used for comparing Machine Learning Software tools.
  • Breadth and Depth of Product Offerings
    • Products/Solutions Offered
    • licenses
    • Services
      • Professional Services (Consulting & Training)
      • Managed services (Support & Maintenance)
    • Organization Size
      • Small and Medium Enterprise (Revenue< 100 Million)
      • Large Enterprises (Revenue> 500 Million)
  • Product Features and Functionality
    • cutomer satisfaction
    • Types of Offerings
      • Software Tools
      • Platform
      • Services
    • Product Features
      • Data visualization and exploration
      • Model Evaluation and Interpretation Tools
      • Modeling APIs
      • Machine Learning Algorithms
      • Model assessment and scoring
      • APIs for Batch and Real-time Predictions
      • Data Transformations
  • Focus on Product Innovation
    • Product Innovation
    • R&D Spend
    • New Product Developments
      • Product Upgradation
      • New Product Launches
  • Product Differentiation and Impact on Customer Value
    • Customer Feedback Frequency
    • Solution Scalability
  • Product Quality and Reliability
    • Level of Support
    • Customer Redressal Mechanism/Program
    • services
      • Technical Support
      • Customer Support
      • Sales Support
      • Others, please specify
    • Pre Sales Support
      • Software Requirement Specification (SRS)
      • Product Demos
      • Proof of Concept
      • Dedicated Account Manager (DAM)
    • Channel for Delivery of Support Services
      • On-Site Support
      • Remote Support

TOP VENDORS (38)

  • 1

    IBM Watson machine learning software helps enterprises to use their data to create, train, and deploy self-learning models. It also helps users in building analytical models and neural networks. IBM data science experience is a cloudbased, social workspace that helps data professionals to consolidate, create, and collaborate across multiple open sources tools, such as R and Python. The company's machine learning software makes it easy and cost-effective for the users to implement AI and machine learning assets in public, private, hybrid or multicloud environments. It facilitates the users to decentralize and distribute their model training by using Apache Spark to train machine learning and deep learning models on structured and unstructured data, whether it resides in relational databases, Hadoop and object storage.

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    • Enterprise
    • New York, USA
    • Founded: 1911
    • $50BN to $100BN
    • 1,00,001 to 5,00,000
  • 2

    SAS best machine learning software offers major features like automated model tuning, powerful data manipulation and management, flexible web-based programming environment, integrated text analytics, model assessment and scoring, and modern statistical, data mining, and machine-learning techniques. Their best machine learning software facilitate the end-to-end data mining and machine learning process with a visual and programming interface. It also boosts analytics teams of all skill levels with a simple yet powerful and automated way to tackle all tasks in the analytics life cycle.

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    • Enterprise
    • North Carolina, USA
    • Founded: 1976
    • $1BN to $5BN
    • 10,001 to 15,000
  • 3

    Azure Machine Learning is a fully managed best machine learning software service for advanced analytics in the cloud. It enables enterprises to build advanced analytic web services quickly and eradicate much of the heavy lifting associated with deploying machine learning in modern data-driven applications.

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    • Enterprise
    • Washington, USA
    • Founded: 1975
    • More than $100 BN
    • 1,00,001 to 5,00,000
  • 4

    Google engine can perform large scale training on a managed cluster and can also manage the trained models for large scale online and batch predictions. Major features of Cloud Machine Learning Software include portable models, notebook developer experience, scalable service, managed service, HyperTune, and the ability to discover and share samples. The company's Advanced Solutions Lab (ASL) supports businesses to partner with Google Cloud and apply machine learning software to tackle high-impact business challenges. The solution offers a unique opportunity for technical teams to understand from Google’s machine learning software experts.

    Read More
    • Enterprise
    • California, USA
    • Founded: 1998
    • More than $100 BN
    • 75,001 to 1,00,000
  • 5

    SAP's Machine Learning Software Foundation offers instantly consumable services, such as image processing, natural language processing, and tabular and time-series processing that help enterprises to learn from data, extract knowledge, and gain new insights. It helps organizations to incorporate intelligence into enterprise applications, without massive computing or data scientists. SAP's Conversational AI helps in developing bots that truly understand humans quickly, and easily. And it also includes off-the-shelf customer support bot for specific industries.

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    • Enterprise
    • Weinheim, Germany
    • Founded: 1972
    • $10BN to $50BN
    • 75,001 to 1,00,000
  • 6

    Amazon Machine Learning Software eases the job of obtaining predictions for user’s application using simple APIs without the need to implement custom prediction generation code or handle any infrastructure. It is considered to be highly scalable, and has the ability to generate billions of predictions on a daily basis and can serve those predictions in real time and at a high throughput as well. Amazon's best machine learning software helps in improving the quality of healthcare, fight human trafficking, provide better customer service, and protect users from fraud. Amazon's Machine Learning Software for Telecommunication industry helps the users in implementing frameworks end-to-end ML process on the AWS Cloud using Jupyter Notebook.

    Read More
    • Enterprise
    • Washington, USA
    • Founded: 1994
    • More than $100 BN
    • 5,00,001 & more
  • 7

    Baidu has developed best machine learning softare algorithms for voice and image recognition, as well as natural language processing, to help provide smarter, more useful, and more personalized search results. Baidu makes its technology available to other companies to develop their algorithms and applications. The company has made most of its software and systems open source and provided access to it on an “as-a-service” basis. Baidu’s open source deep learning platform, PaddlePaddle supports neural network architectures, including convolutional neural networks and recurrent neural networks. The platform is fully scalable and is designed to enhance mathematical operations using BLAS libraries, including Intel MKL, ATLAS, OpenBLAS and cuBLAS.

    Read More
    • Startup
    • Beijing, China
    • Founded: 2000
    • $10BN to $50BN
    • 45,001 to 50,000
  • 8

    The major business functions where FICO uses AI and best machine learning software are financial crimes, marketing, cybersecurity, customer management, threat detection, and strategic planning. FICO has more than 130 patents in analytics and decision management technology, including 70 in AI. FICP has introduced new explainable artificial intelligence toolkit (xAI Toolkit) which meets customers’ increasing demand for industry-leading artificial intelligence. This solution enhances decision performance by integrating predictive and prescriptive models directly into real-time business operations to create faster, more impactful business outcomes. It also offers Intuitive end user experience and delvers direct and immediate access to data and insights dynamically.

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    • Enterprise
    • California, USA
    • Founded: 1956
    • $500MN to $1BN
    • 1,001 to 5,000
  • 9

    Oracle's best machine learning software enable data scientists and analysts to work in collaboration to explore their data visually and design new analytical methodologies in the Autonomous Data Warehouse Cloud. Oracle's optimum, parallel and scalable in-Database implementations of best machine learning software algorithms are exposed via SQL and PL/SQL using Apache Zeppelin-based notebook technology. The company's best machine learning software notebooks facilitate teams to collaborate to build, assess, and deploy machine learning solutions, while improving data scientist productivity. They also focus on ease of use and simplified machine learning software for data science from preparation through deployment all in the Autonomous Database.

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    • Enterprise
    • California, USA
    • Founded: 1977
    • $10BN to $50BN
    • 1,00,001 to 5,00,000
  • 10

    Dell EMC is designed to meet customer needs no matter what they are in their Artificial Intelligence (AI) journey. It delivers a strong portfolio of modern infrastructure and AI-enabled IT solutions, including dedicated AI consulting services. Dell EMC solutions allow technology firms to start small and grow according to their AI use cases, expertise and business goals. Dell Precision workstations support in deploying and managing cognitive technology platforms, including Machine Learning (ML) Software, Artificial Intelligence (AI) and Deep Learning (DL). Dell Precision Optimizer uses Machine Learning Software developed on new, powerful Dell Precision workstations to enhance system reliability with automated updates and provide analytics to address bottlenecks.

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    • Enterprise
    • Texas, USA
    • Founded: 1984
    • $50BN to $100BN
    • 1,00,001 to 5,00,000
  • 11

    BigMLer is a free and an open-source tool, which aids users to perform refined machine learning software workflows by typing a single line in the command prompt. BigML-GAS is an add-on for Google Sheet, which puts in the missing values in a particular spreadsheet by utilizing its existing models and clusters. BigMLX is an app for Mac operating system, which provides the ability to build models and predict the outcomes by using the drag and drop functions on a Mac desktop.

    Read More
    • Startup
    • Corvallis, OR, US
    • Founded: 2011
    • Below $10 MN
    • 1 to 50
  • 12

    It enables users to apply machine learning software to applications and big data analysis, to analyze and extract insights from the text, audio, video, and image files, and build cognitive apps with machine learning APIs. Major features of the platform are diverse support, extensive composable APIs, cloud-based consumption, and rich developer ecosystem.

    Read More
    • Enterprise
    • California, USA
    • Founded: 2015
    • $10BN to $50BN
    • 50,001 to 75,000
  • 13

    With Sparkling Water, users can drive computation from Scala/R/Python and utilize the H2O Flow UI. Recently, the company introduced Driverless AI, which helps to prepare data, calibrate parameters, and determine the optimal algorithms for tackling specific business problems with machine learning software. Furthermore, it automates feature engineering, the process by which key variables are selected to build a model.

    Read More
    • Enterprise
    • California, USA
    • Founded: 2012
    • Below $10 MN
    • 101 to 500
  • 14

    Built on a hardware foundation that includes computing, memory, storage, and network, these platforms include an optimized, scalable software stack for predictive analytics.

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    • Enterprise
    • California, USA
    • Founded: 1968
    • $50BN to $100BN
    • 1,00,001 to 5,00,000
    • Startup
    • Zurich, Switzerland
    • Founded: 2008
    • Below $10 MN
    • 1 to 50
    • Startup
    • New York, US
    • Founded: 2013
    • $11MN to $50MN
    • 101 to 500
    • Startup
    • Massachusetts, US
    • Founded: 2006
    • $11MN to $50MN
    • 51 to 100
    • Startup
    • Ontario, Canada
    • Founded: 1984
    • Below $10 MN
    • 51 to 100
    • Enterprise
    • San Francisco, California, US
    • Founded: 2010
    • Below $10 MN
    • 51 to 100
    • Enterprise
    • California, USA
    • Founded: 2000
    • $500MN to $1BN
    • 1,001 to 5,000
    • Enterprise
    • California, USA
    • Founded: 1997
    • $1BN to $5BN
    • 5,001 to 10,000
    • Startup
    • California, USA
    • Founded: 2013
    • Below $10 MN
    • 101 to 500
    • Enterprise
    • California, USA
    • Founded: 1979
    • $1BN to $5BN
    • 10,001 to 15,000
    • Enterprise
    • Pennsylvania, USA
    • Founded: 2011
    • Below $10 MN
    • 1 to 50
  • 25

    The Cambridge based company Luminoso combines natural language with artificial intelligence to analyze text-based data. The machine learning software is the perfect example of no-code text analytics. The software can analyze any text-based data from call transcripts, open-ended survey responses, product reviews, chatbot or live chat transcripts, emails, articles, to NPS open-ends. It can process data in 15 languages including Korean, Chinese, English, Dutch, Italian, German, French, Russian, Swedish, Portuguese, Spanish, Arabic and Japanese.

    Read More
    • Startup
    • Massachusetts, US
    • Founded: 2010
    • Below $10 MN
    • 1 to 50
  • 26

    6Sense is a predictive software that helps companies and organizations in making predictions related to sales and marketing. It is the fastest and most reliant software out there currently. It uses a demographic, statistic, and firmographic analysis to make these predictions. The 85% accurate results delivered by the software makes it the most efficient and most used software by the companies. 6Sense is an organization’s best friend for making B2B connections because it combines time-sensitive data and uses it to make web-based predictions.

    Read More
    • Startup
    • California, USA
    • Founded: 2013
    • $11MN to $50MN
    • 101 to 500
    • Startup
    • California, USA
    • Founded: 2006
    • $11MN to $50MN
    • 101 to 500
  • 28

    Features of Alteryx are -

    • Repetitive analytics: Using repetitive analytics, it is straightforward to run complex queries using the software.
    • Data preparation: The software has the most common methods of data preparation built-in.
    • Flexible: The Alteryx machine learning is very flexible and facilitates fast data manipulation.

    Read More
    • SME
    • California, USA
    • Founded: 1997
    • $101MN to $500MN
    • 501 to 1,000
    • Startup
    • Illinois, USA
    • Founded: 2013
    • Below $10 MN
    • 101 to 500
    • Startup
    • Connecticut, USA
    • Founded: 2006
    • Below $10 MN
    • 51 to 100
    • Startup
    • California, USA
    • Founded: 2007
    • $51MN to $100MN
    • 101 to 500
    • Startup
    • California, USA
    • Founded: 2008
    • $51MN to $100MN
    • 101 to 500
    • SME
    • New York, USA
    • Founded: 1975
    • $101MN to $500MN
    • 1,001 to 5,000
    • Startup
    • Bracknell Forest, UK
    • Founded: 1987
    • Below $10 MN
    • 51 to 100
    • Enterprise
    • Pennsylvania, USA
    • Founded: 1993
    • $500MN to $1BN
    • 1,001 to 5,000
    • Startup
    • California, USA
    • Founded: 2012
    • $11MN to $50MN
    • 51 to 100
    • Startup
    • New York, USA
    • Founded: 2004
    • $101MN to $500MN
    • 501 to 1,000
    • Enterprise
    • California, USA
    • Founded: 2003
    • $1BN to $5BN
    • 1,001 to 5,000

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Marie Stelle

Engagement Partner - 360Quadrants.com