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Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016

  • 200213603-00156.4% of developers are building robotics apps today.
  • 45% of developers say that Internet of Things (IoT) development is critical to their overall digital strategy.
  • 27.4% of all developers are building apps in the cloud today.
  • 24.7% are using machine learning for development projects.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. 1,441 developers completed the survey globally. Please see page 17 of the study for additional details on the methodology.

Key takeaways from the study include the following:

  • Big Data analytics developers are spending the majority of their time creating Internet of Things (IoT).  The second-most popular Big Data analytics applications are in professional, scientific and technical services (10%), telecommunications (10%), and manufacturing (non-computer related) (9.6%). The following graphic provides an overview of where Big Data analytics developers are investing their time building new applications.

Best Describes App

  • Robotics (56.4%), Arts, Entertainment and Recreation (56.3%), and Automotive (52.9%) are the three most popular industries data mining app developers are focusing on today. Additional high priority industries include telecommunications (48.3%), Internet of Things (47.1%) and manufacturing (46.7%). A graphic from the study is shown below for reference.

Data Mining adoption

  • Nearly one-third (27.4%) of all app developers globally are planning to build new apps on the cloud. 66.9% expect to have a new cloud app within 12 months. Overall, 81.3% of all developers surveyed are building cloud apps today. The following graphic compares developers’ predicted timeframes for cloud app development over the next two years.

Plans for Apps In the Clouds

  • Better security (51.9%), more reliability (42%) and better user experience (41%) are the top three areas that motivate developers to move to new cloud platforms. Additional considerations include a better breadth of services (39.4%), networking and data center speed (37.8%), better pricing options (37.5%), better licensing structures (34.6%) and completeness of vision (30.9%). The following graphic compares the key factors that most motivate developers to switch cloud platforms.

key factors

  • 45% of developers say that Internet of Things (IoT) development is very important to their overall digital strategy. 7% say that IoT is somewhat important to their digital strategy. The study also found that 29.5% of all developers are creating Internet of Things (IoT) apps today. The following graphic illustrates the relative level of importance of IoT to developers’ digital strategies.

importance of IoT strategy

  • 41% say that cognitive computing and artificial intelligence (AI) are very important to their digital strategies. In speaking with senior executives at services firms, the opportunity to provide artificial intelligence-based services using a subscription model is gaining momentum, with many beginning to fund development projects to accomplish this on a global scale.

AI Importance

  • Most frequently created machine learning apps include those for the Internet of Things (11.4%), Professional, Scientific and Technical Services (10%), and Manufacturing (9.4%) industries.  Additional industries include telecommunications (8.3%), utilities/energy (8.1%), robotics (7.2%) and finance or insurance (6.8%). The following graphic breaks out the industries where machine learning app development is happening today.

Machine learning industries final

  • The majority of developers (84.2%) say that analytics is important for enabling their organizations to operate today. Of that group, 45.7% say that analytics are very important for their organizations to attain their goals.
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2015 Gartner CRM Market Share Update

  • Worldwide customer relationship management (CRM) software totaled $26.3B in 2015, up 12.3% from $23.4B in 2014.
  • SaaS revenue grew 27% yr-over-yr, more than double overall CRM market growth in 2015.
  • Asia/Pacific grew the fastest of all regions globally, increasing 9% 2015, closely followed by greater China with 18.4% growth.

These and many other insights into the current state of the global CRM market are from Gartner’s Market Share Analysis: Customer Relationship Management Software, Worldwide, 2015 (PDF, client access) published earlier this month.  The top five CRM vendors accounted for 45% of the total market in 2015. Salesforce dominated in 2015, with a 21.1% annual growth rate and absolute growth of over $902M in CRM revenue, more than the next ten providers combined. Gartner found that Salesforce leads in revenue in the sales and customer service and support (CSS) segments of CRM, and is now third in revenue in the marketing segment. Gartner doesn’t address how analytics are fundamentally redefining CRM today, which is an area nearly every C-level and revenue team leader I’ve spoken with this year is prioritizing for investment. The following graphic and table compare 2015 worldwide CRM market shares.

CRM Market Share 2015

table 1

Adobe, Microsoft, and Salesforce Are Growing Faster Than The Market

Adobe grew the fastest between 2014 and 2015, increasing worldwide sales 26.9%. Salesforce continues to grow well above the worldwide CRM market average, increasing sales 21.1%. Microsoft increased sales 20% in the last year.  The worldwide CRM market grew 12.3% between 2014 and 2015.

Spending by vendor 2015

 Analytics, Machine Learning, and Artifical Intelligence Are The Future Of CRM

Advanced analytics, machine learning and artificial intelligence (AI) will revolutionize CRM in the next three years. Look to the five market leaders in 2015 to invest heavily in these areas with the goal of building patent portfolios and increasing the amount of intellectual property they own. Cloud-based analytics platforms offer the scale, speed of deployment, agility, and ability to rapidly prototype analytics workflows that support the next generation of CRM workflows. My recent post on SelectHub, Selecting The Best Cloud Analytics Platform: Trends To Watch In 2016, provides insights into how companies with investments in CRM systems are making decisions on cloud platforms today. Based on insights gained from discussions with senior management teams, I’ve put together an Intelligent Cloud Maturity Model that underscores why scalability of a cloud-based analytics platform is a must-have for any company.
cloud-maturity-model

Sources:  Gartner Says Customer Relationship Management Software Market Grew 12.3 Percent

Machine Learning Is Redefining The Enterprise In 2016

machine learning imageBottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before.

Industries Where Machine Learning Is Making An Impact  

The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry.

machine learning industries

Source: Tata Consultancy Services, Using Big Data for Machine Learning Analytics in Manufacturing – TCS

Machine Learning Is Revolutionizing Sales and Marketing  

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets. And increasingly data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data.  Machine learning is proving to be efficient at handling predictive tasks including defining which behaviors have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.  In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover the Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning. Key takeaways from their study results include the following:

  • 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
  • At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
  • 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
  • Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.

Why Machine Learning Adoption Is Accelerating

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes.  Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.
The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.
The following are the key factors enabling machine learning growth today:

  • Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily. Demand forecasts, CRM and ERP transaction data, transportation costs, barcode and inventory management data, historical pricing, service and support costs and accounting standard costing are just a few of the many sources of structured data enterprises make decisions with today.   The exponential growth of unstructured data that includes social media, e-mail records, call logs, customer service and support records, Internet of Things sensing data, competitor and partner pricing and supply chain tracking data frequently has predictive patterns enterprises are completely missing out on today. Enterprises looking to become competitive leaders are going after the insights in these unstructured data sources and turning them into a competitive advantage with machine learning.
  • The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimizing supply chain networks and increasing demand forecast predictive As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential growth of data generated. The availability and intrinsic value of these large-scale datasets are an impetus further driving machine learning adoption.
  • Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data. From weather forecasting to optimizing a supply chain network using advanced simulation techniques that generate terabytes of data, the ability to fine-tune forecasts and attain greater optimizing is also driving machine learning adoption. Simulated data sets of product launch and selling strategies is a nascent application today and one that shows promise in developing propensity models that predict purchase levels.
  • The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses. Online storage and public cloud instances can be purchased literally in minutes online with a credit card. Migrating legacy data off of databases where their accessibility is limited compared to cloud platforms is becoming more commonplace as greatest trust in secure cloud storage increases. For many small businesses who lack IT departments, the Cloud provides a scalable, secure platform for managing their data across diverse geographic locations.

Further reading

Companies Are Reimagining Business Processes with Algorithms. Harvard Business Review. February 8, 2016.  H. James Wilson, Allan Alter, Prashant Shukla. Source: https://hbr.org/2016/02/companies-are-reimagining-business-processes-with-algorithms

Domingos, P. (2012). A Few Useful Things to Know About Machine Learning. Communications Of The ACM, 55(10), 78-87.

Pyle, D., & San José, C. (2015). An executive’s guide to machine learning. Mckinsey Quarterly, (3), 44-53. Link: http://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning

Sales Gets A Machine-Learning Makeover.  MIT Sloan Management Review, May 17, 2016. H. James Wilson, Narendra Mulani, Allan Alter. Source: http://sloanreview.mit.edu/article/sales-gets-a-machine-learning-makeover/Sebag, M. (2014).

The Next Wave Of Enterprise Software Powered By Machine Learning.  TechCrunch, July 27, 2015. http://techcrunch.com/2015/07/27/the-next-wave-of-enterprise-software-powered-by-machine-learning/

What Every Manager Should Know About Machine Learning, Harvard Business Review,  July 7, 2015.  Link: https://hbr.org/2015/07/what-every-manager-should-know-about-machine-learning

What Is Machine Learning? Making The Complex Simple.  Mike Ferguson.  IBM Big Data & Analytics Hub. Link: http://www.ibmbigdatahub.com/blog/what-machine-learning

World Economic Forum White Paper Digital Transformation of Industries: In collaboration with Accenture Digital Enterprise, January 2016. Link: http://reports.weforum.org/digital-transformation-of-industries/wp-content/blogs.dir/94/mp/files/pages/files/digital-enterprise-narrative-final-january-2016.pdf

Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., & Yang, X. (2015, February). On machine learning towards predictive sales pipeline analytics. In Twenty-Ninth AAAI Conference on Artificial Intelligence.  Link: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9444/9488

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