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Posts from the ‘SaaS’ Category

10 Charts That Will Change Your Perspective Of Big Data’s Growth

  • 10 Charts That Will Change Your Perspective Of Big Data's GrowthWorldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48% according to Wikibon.
  • Forrester predicts the global Big Data software market will be worth $31B this year, growing 14% from the previous year. The entire global software market is forecast to be worth $628B in revenue, with $302B from applications.
  • According to an Accenture study, 79% of enterprise executives agree that companies that do not embrace Big Data will lose their competitive position and could face extinction. Even more, 83%, have pursued Big Data projects to seize a competitive edge.
  • 59% of executives say Big Data at their company would be improved through the use of AI according to PwC.

Sales and Marketing, Research & Development (R&D), Supply Chain Management (SCM) including distribution, Workplace Management and Operations are where advanced analytics including Big Data are making the greatest contributions to revenue growth today. McKinsey Analytics’ study Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in) is a comprehensive overview of how analytics technologies and Big Data are enabling entirely new ecosystems, serving as a foundational technology for Artificial Intelligence (AI). McKinsey finds that analytics and Big Data are making the most valuable contributions in the Basic Materials and High Tech industries. The first chart in the following series of ten is from the McKinsey Analytics study, highlighting how analytics and Big Data are revolutionizing many of the foundational business processes of Sales and Marketing.

The following ten charts provide insights into Big Data’s growth:

  • Nearly 50% of respondents to a recent McKinsey Analytics survey say analytics and Big Data have fundamentally changed business practices in their sales and marketing functions. Also, more than 30% say the same about R&D across industries, with respondents in High Tech and Basic Materials & Energy report the greatest number of functions being transformed by analytics and Big Data. Source: Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in).

  • Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B. Source: Wikibon and reported by Statista.

  • According to NewVantage Venture Partners, Big Data is delivering the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%). Discovering new opportunities to reduce costs by combining advanced analytics and Big Data delivers the most measurable results, further leading to this category being the most prevalent in the study. 69.4% have started using Big Data to create a data-driven culture, with 27.9% reporting results. Source: NewVantage Venture Partners, Big Data Executive Survey 2017 (PDF, 16 pp.)

  • The Hadoop and Big Data Market are projected to grow from $17.1B in 2017 to $99.31B in 2022 attaining a 28.5% CAGR. The greatest period of projected growth is in 2021 and 2022 when the market is projected to jump $30B in value in one year. Source: StrategyMRC and reported by Statista.

  • Big Data applications and analytics is projected to grow from $5.3B in 2018 to $19.4B in 2026, attaining a CAGR of 15.49%. Big Data market worldwide includes Professional Services is projected to grow from $16.5B in 2018 to $21.3B in 2026. Source: Wikibon and reported by Statista.

  • Comparing the worldwide demand for advanced analytics and Big Data-related hardware, services and software, the latter category’s dominance becomes clear. The software segment is projected to increase the fastest of all categories, increasing from $14B in 2018 to $46B in 2027 attaining a CAGR of 12.6%. Sources: WikibonSiliconANGLE; Statista estimates and reported by Statista.

  • Advanced analytics and Big Data revenue in China are projected to be worth ¥57.8B ($9B) by 2020. The Chinese market is predicted to be one of the fastest growing globally, growing at a CAGR of 31.72% in the forecast period. Sources: Social Sciences Academic Press (China) and Statista.

  • Non-relational analytic data stores are projected to be the fastest growing technology category in Big Datagrowing at a CAGR of 38.6% between 2015 and 2020. Cognitive software platforms (23.3% CAGR) and Content Analytics (17.3%) round out the top three fastest growing technologies between 2015 and 2020. Source: Statista.

  • A decentralized general-merchandise retailer that used Big Data to create performance group clusters saw sales grow 3% to 4%. Big Data is the catalyst of a retailing industry makeover, bringing greater precision to localization than has been possible before. Big Data is being used today to increase the ROI of endcap promotions, optimize planograms, help to improve upsell and cross-sell sales performance and optimize prices on items that drive the greatest amount of foot traffic. Source: Use Big Data to Give Local Shoppers What They Want, Boston Consulting Group, February 8, 2018.

  • 84% of enterprises have launched advanced analytics and Big Data initiatives to bring greater accuracy and accelerate their decision-making Big Data initiatives focused on this area also have the greatest success rate (69%) according to the most recent NewVantage Venture Partners Survey. Over a third of enterprises, 36%, say this area is their top priority for advanced analytics and Big Data investment. Sources: NewVantage Venture Partners Survey and Statista.

Additional Big Data Information Sources:

4 Pain Points of Big Data and how to solve them, Digital McKinsey via Medium, November 10, 2017

53% Of Companies Are Adopting Big Data Analytics, Forbes, December 24, 2017

6 Predictions For The $203 Billion Big Data Analytics Market, Forbes, Gil Press, January 20, 2017

Analytics Comes of Age, McKinsey Analytics, January 2018 (PDF, 100 pp.)

Big Data & Analytics Is The Most Wanted Expertise By 75% Of IoT Providers, Forbes, August 21, 2017

Big Data 2017 – Market Statistics, Use Cases, and Trends, Calsoft (36 pp., PDF)

Big Data and Business Analytics Revenues Forecast to Reach $150.8 Billion This Year, Led by Banking and Manufacturing Investments, According to IDC, March 14, 2017

Big Data Executive Survey 2018, Data and Innovation – How Big Data and AI are Driving Business Innovation, NewVantage Venture Partners, January 2018 (PDF, 18 pp.)

Big Data Tech Hadoop and Spark Get Slow Start in Enterprise, Information Week, March 20, 2018

Big Success With Big Data, Accenture  (PDF, 12 pp.)

Gartner Survey Shows Organizations Are Slow to Advance in Data and Analytics, Gartner, February 5, 2018

How Big Data and AI Are Driving Business Innovation in 2018, MIT Sloan Management Review, February 5, 2018

IDC forecasts big growth for Big Data, Analytics Magazine. April 2018

IDC Worldwide Big Data Technology and Services 2012 – 2015 Forecast, Courtesy of EC Europa (PDF, 34 pp.)

Midyear Global Tech Market Outlook For 2017 To 2018, Forrester, September 25, 2017 (client access reqd.)

Oracle Industry Analyst Reports – Data-rich website of industry analyst reports

Ten Ways Big Data Is Revolutionizing Marketing And Sales, Forbes, May 9, 2016

The Big Data Payoff: Turning Big Data into Business Value, CAP Gemini & Informatica Study, (PDF, 12 pp.)

The Forrester Wave™: Enterprise BI Platforms With Majority Cloud Deployments, Q3 2017 courtesy of Oracle

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How Zero Trust Security Fuels New Business Growth

Bottom Line: Zero Trust Security (ZTS) strategies enabled by Next-Gen Access (NGA) are indispensable for assuring uninterrupted digital business growth, and are proving to be a scalable security framework for streamlining onboarding and systems access for sales channels, partners, patients, and customers of fast-growing businesses.

The era of Zero Trust Security is here, accelerated by NGA solutions and driven by the needs of digital businesses for security strategies that can keep up with the rapidly expanding perimeters of their businesses. Internet of Things (IoT) networks and the sensors that comprise them are proliferating network endpoints and extending the perimeters of growing businesses quickly.

Inherent in the DNA of Next-Gen Access is the ability to verify the user, validate the device (including any sensor connected to an IoT network), limit access and privilege, then learn and adapt using machine learning techniques to streamline the user experience while granting access to approved accounts and resources. Many digital businesses today rely on IoT-based networks to connect with suppliers, channels, service providers and customers and gain valuable data they use to grow their businesses. Next-Gen Access solutions including those from Centrify are enabling Zero Trust Security strategies that scale to secure the perimeters of growing businesses without interrupting growth.

How Zero Trust Security Fuels New Business Growth  

The greater the complexity, scale and growth potential of any new digital business, the more critical NGA becomes for enabling ZTS to scale and protect its expanding perimeters. One of the most valuable ways NGA enables ZTS is using machine learning to learn and adapt to users’ system access behaviors continuously. Insights gained from NGA strengthen ZTS frameworks, enabling them to make the following contributions to new business growth:

  1. Zero Trust Security prevents data breaches that cripple new digital business models and ventures just beginning to scale and grow. Verifying, validating, learning and adapting to every user’s access attempts and then quantifying their behavior in a risk score is at the core of Next-Gen Access’ DNA. The risk scores quantify the relative levels of trust for each system user and determine what, if any, additional authentication is needed before access is granted to requested resources. Risk scores are continuously updated with every access attempt, making authentication less intrusive over time while greatly reducing compromised credential attacks.
  2. Securing the expanding endpoints and perimeters of a digital business using NGA frees IT and senior management up to focus more on growing the business. In any growing digital business, there’s an exponential increase in the number of endpoints being created, rapidly expanding the global perimeter of the business. The greater the number of endpoints and the broader the perimeter, the more revenue potential there is. Relying on Next-Gen Access to scale ZTS across all endpoints saves valuable IT time that can be dedicated to direct revenue-producing projects and initiatives. And by relying on NGA as the trust engine that enables ZTS, senior management will have far fewer security-related emergencies, interruptions, and special projects and can dedicate more time to growing the business. A ZTS framework also centralizes security management across a digital business, alleviating the costly, time-consuming task of continually installing patches and updates.
  3. Zero Trust Security is enabling digital businesses globally to meet and exceed General Data Protection Regulation (GDPR) compliance requirements while protecting and growing their most valuable asset: customer trust. Every week brings new announcements of security breaches at many of the world’s most well-known companies. Quick stats on users affected, potential dollar loss to the company and the all-too-common 800 numbers for credit bureaus seem to be in every press release. What’s missing is the incalculable, unquantifiable cost of lost customer value and the millions of hours customers waste trying to avert financial chaos. In response to the need for greater oversight of how organizations respond to breaches and manage data security, the European Union (EU) launched General Data Protection Regulation (GDPR) which goes into effect May 25, 2018. GDPR applies not only European organizations, but also to foreign businesses that offer goods or services in the European Union (EU) or monitor the behavior of individuals in the EU. The compliance directive also states that organizations need to process data so in a way that “ensures appropriate security of the personal data, using appropriate technical and organizational measures,” taking into account “state of the art and the costs of implementation.”

Using an NGA approach that includes risk-based multi-factor authentication (MFA) to evaluate every login combined with the least privilege approach across an entire organization is a first step towards excelling at GDPR compliance. Zero Trust Security provides every organization needing to comply with GDPR a solid roadmap of how to meet and exceed the initiative’s requirements and grow customer trust as a result.

Conclusion

Next-Gen Access enables Zero Trust Security strategies to scale and flex as a growing business expands. In the fastest growing businesses, endpoints are proliferating as new customers are gained, and suppliers are brought onboard. NGA ensures growth continues uninterrupted, helping to thwart comprised credential attacks, which make up 81% of all hacking-related data breaches, according to Verizon.

83% Of Enterprises Are Complacent About Mobile Security

  • 89% of organizations are relying on just a single security strategy to keep their mobile networks safe.
  • 61% report that their spending on mobile security had increased in 2017 with 10% saying it had increased significantly.
  • Just 39% of mobile device users in enterprises change all default passwords, and only 38% use strong two-factor authentication on their mobile devices.
  • Just 31% of companies are using mobile device or enterprise mobility management (MDM or EMM).

These and many other insights are from the recently published Verizon Mobile Security Index 2018 Report. The report is available here for download (22 pp., PDF, no opt-in). Verizon commissioned an independent research company to complete the survey in the second half of 2017, interviewing over 600 professionals involved in procuring and managing mobile devices for their organizations. Please see page 20 of the study for additional details on the methodology.

The study found that the accelerating pace of cloud, Internet of Things (IoT), and mobile adoption is outpacing enterprises’ ability to scale security management, leaving companies vulnerable. When there’s a trade-off between the expediency needed to accomplish business performance goals and security, the business goals win the majority of the time. 32% of enterprises are sacrificing security for expediency and business performance, leaving many areas of their core infrastructure unsecured. Enterprises who made this trade-off of expediency over security were 2.4x as likely to suffer data loss or downtime.

Key takeaways from the study include the following:

  • 79% of enterprises consider their employees to be the most significant security threat. The study points out that it’s not due to losing devices, inadvertent security errors or circumventing security policies. It’s the threat of employees using their secured access for financial or personal gain. 58% of senior management leaders interviewed view employees with secure access as the most significant threat. Security platforms that can stop credential attacks using risk assessment models predicated on behavioral pattern matching and analysis by verifying an employee’s identity are flourishing today. One of the leaders in this field is Centrify, who espouses Zero Trust Security. The following graphic from the study shows the priority of which actors enterprise leaders are most concerned about regarding threats, with employees being the most often mentioned.

  • 32% of enterprises have sacrificed security for expediency and business performance leading to 45% of them suffering data loss or downtime. The study found that companies who sacrificed security were also 2.4x more likely to have experienced data loss or downtime as a result of a mobile-related security incident. For the 68% who prioritized security over expediency, just 19% had suffered data loss or downtime.

  • 89% of enterprises are relying on just a single security practice to keep their mobile networks safe. Verizon’s study found that the majority of enterprises are relying on just one security practice to protect their networks. 55% have two in place, and just 14% have four. Of the four security practices, only 39% change all default passwords. Just under half (47%), encrypt the transmission of sensitive data across open, public networks. The following graphic from the study illustrates the percentage of enterprises who have between 1 and all four security practices in place.

  • Just 49% of enterprises have a policy regarding the use of public WiFi, and even fewer (47%) encrypt the transmission of sensitive data across open, public networks. A startling high 71% of respondents use public Wi-Fi networks for work tasks, despite their companies prohibiting their use. Taking risks with unsecured Wi-Fi networks for expediency and business performance being done at the expense of security supports a key finding of this study. Nearly one in three (32%) of enterprises are sacrificing security for expediency and business performance, including accessing unsecured Wi-Fi networks. The following infographic from the study explains a few of the many security threats inherent in the design and use of public Wi-Fi networks.

 

10 Charts That Will Change Your Perspective Of Amazon Prime’s Growth

    • 70% of Americans with incomes of $150,000 or more who shop online have Amazon Prime memberships.
    • Amazon Prime international customers will grow at a 56% compound annual growth rate (CAGR) between 2016 to 2018.
    • Amazon shipped more than 5 billion items in 2017 with Prime worldwide.
    • By 2022 there will be 56 million Amazon Prime Video subscribers in the U.S., and 122 million worldwide.

Net Sales at Amazon reached $177.9B in 2017, a 31% increase from $136B in 2016 and Net Income increased from $2.4B in 2016 to $3B in 2017. Their fourth quarter, 2017 financial results are available here. Their latest financial results also reflect how increasing operating expenses are squeezing margins as the company builds out their fulfillment network in international markets, technology, content, and marketing efforts.

Amazon Prime is an annual membership program that includes unlimited free shipping of over 100 million items, access to unlimited instant streaming of thousands of movies and TV episodes, Alexa voice shopping, unlimited free access to thousands of Kindle books and content. Amazon Prime also includes free same day delivery on selected products, in addition to planned services Amazon is fine-tuning for launch later this year.

Revenue for online subscriptions to services like its Amazon Prime membership, Audible, Prime Video, and Prime Music Unlimited was up 49% year over year, handily outpacing the 20% year-over-year revenue growth from its online store segment. In January 2018 Amazon raised the price for Prime membership $2 to $12.99 for customers making monthly payments, totaling $156 per year. Amazon chose to leave the Prime membership price at $99 for those customers choosing to make one annual payment. Investment firm Cowen & Company estimates the $2 price increase to Prime subscribers who pay monthly will generate an additional $300M in revenue.

The following ten charts provide insights into Amazon Primes’ explosive growth:

  • 51% of U.S. households will be Amazon Prime subscribers in 2018, up from 45% in 2017 with Prime subscribers spending up to 4.6X more than non-prime customers. Morgan Stanley estimates that the average Amazon Prime customer spent $2,486 over the last twelve months compared to $544 for non-Prime Amazon customers. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • There are an estimated 90 million paying Amazon Prime subscribers in the United States today according to Consumer Intelligence Research Partners and Statista. Amazon was able to grow Prime memberships from 63 million in June 2016 to 90 million in September of last year. From just 25 million members in December 2013 to 90 million in September of last year, Amazon has been able to attain a 29.2% CAGR of subscribers over the last five years. Statista found that Amazon Prime members spend an average of $1,300 per year compared to non-Prime members who spend $700 annually. Source: Statista.   

  • 70% of Americans with incomes of $150,000 or more who shop online have Amazon Prime memberships. Alexa, Echo, Dash, IoT, Smart Home and Prime Now delivery services are predicated on attracting and retaining Prime customers who have higher disposable incomes and are willing to pay for convenience. Amazon realizes the most profitable Prime customers they have are facing a continual time shortage due to demanding jobs and travel schedules. The Prime services roadmap continues to reflect convenience and speed to serve high-income families, many of which have two wage earners, where time is at a premium. Source: Statista.

  • 46% of Amazon Prime subscribers buy something online using the benefits of their subscription at least once a week. In contrast, only 13% of non-Prime Amazon shoppers make weekly purchases. Amazon’s proliferation of services helps to keep Prime customers coming back. Combining a broad services portfolio and real-time convenience on a trusted platform, Amazon has found a way to become indispensable to customers who have high disposable incomes and little extra time. Source: Nearly Half of US Households Are Now Amazon Prime Subscribers, eMarketer Retail. January 30, 2018.

  • Amazon Prime international customers will grow at a 56% compound annual growth rate (CAGR) between 2016 to 2018, growing over two times as fast as the S. Prime customer base while expectations of shorter delivery times increase. Morgan Stanley estimates there will be 62 million U.S.-based Amazon Prime customers by the end of 2018, growing from an estimated 54 million in 2017. International Prime subscribers are projected to grow from 18 million in 2018 to 45 million in 2018. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • By 2022 there will be 56 million Amazon Prime Video subscribers alone in the U.S., and 122 million worldwide. Within four years it’s projected that Amazon Prime Video will grow its customer base globally to 122 million subscribers, with 45.9% from the U.S. alone. Amazon’s Source: Statista.

  • Amazon Prime Video is the primary growth catalyst for Amazon to gain new subscribers in Japan, Germany, and the UK. Amazon Prime membership jumped 16% in Japan in just three months following the launch of Prime Instant Video. Prime subscriber rates increased in the UK and Germany with the introduction of Prime Instant Video. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • 63% of Amazon online shopping users are also subscribers to Amazon Prime today. Gaining new Prime subscribers from existing online users have started to slow down compared to other areas of Amazon Prime growing at double-digit growth rates. Amazon’s strategy of broadening the base of services and devices including Alexa to attract new subscribers shows signs of working according to their latest financial results. Source: Statista.

  • Amazon Prime has 3.4 times the number of customers acquired Whole Foods Market has and is changing the pricing and profitability of food retailing now. Amazon is actively re-ordering the food retailing landscape by capitalizing on the scale of their operations in the supply chain, logistics and fulfillment operations. Morgan Stanley found that the primary reason customers aren’t shopping at Whole Foods Markets is the perception of lower prices elsewhere. Amazon’s selective reduction of prices at Whole Foods Markets is margin-driven today. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

  • Amazon is combining Prime Now 1 to 2-hour deliveries and Whole Foods Market local inventory to fuel and scale a profitable grocery delivery business. One of the most attractive benefits of Prime membership is the flexibility of ordering products for 1 to 2-hour By increasing the variety of products deliverable by the Prime Now service, Amazon is scaling its home delivery business profitably. Source: Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in).

Data Sources on Amazon Prime and their latest reported financial results:

Amazon Disruption Symposium Where so Far? Where to Next? Who is Safe?, Morgan Stanley, September 18, 2017. (PDF, 88 pp., no opt-in)

Amazon has around 80 million reasons to be excited for Prime Day, Business Insider. July 10, 2017

Amazon hikes the price of Prime monthly memberships by 18%, CNN, January 19, 2018

Amazon nipping at Netflix’s heels, IHS Markit, January 16, 2018

Amazon Prime Had A Ridiculously Good 2017, Slash Gear January 2, 2018

Amazon Prime had its best year of sign-ups ever, Quartz, Alison Griswold.

Amazon Prime Hits 90 Million US Members, Consumer Intelligence Research Partners, October 18, 2017 (PDF, 22 pp., no opt-in)

Amazon Prime’s Monthly Price Hike Will Generate $300 Million a Year, Bloomberg & Company, January 22, 2018

Don’t Overlook These Metrics From Amazon.com, Inc.’s Fourth Quarter, NASDAQ. February 10, 2018

For the wealthiest Americans, Amazon Prime has become the norm, Recode, June 8, 2017

Here’s How Much Amazon Prime Customers Spend Per Year, Fortune, October 18, 2017

Nearly Half of US Households Are Now Amazon Prime Subscribers, eMarketer Retail, January 30, 2018

Number of Amazon Prime Video subscribers worldwide in selected countries in 2022 (in millions), Statista, 2018.

Pros and Cons of Amazon Prime, Consumer Reports, February 22, 2018

Sixty-Four Percent Of U.S. Households Have Amazon Prime, Forbes, June 17, 2017

Why Amazon Bought Whole Foods, The Atlantic, June 16, 2017

10 Ways Machine Learning Is Revolutionizing Marketing

 

  • 84% of marketing organizations are implementing or expanding AI and machine learning in 2018.
  • 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.
  • 3 in 4 organizations implementing AI and machine learning increase sales of new products and services by more than 10% according to Capgemini.

Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

The strongest marketing departments rely on a robust set of analytics and Key Performance Indicators (KPIs) to measure their progress towards revenue and customer growth goals. With machine learning, marketing departments will be able to deliver even more significant contributions to revenue growth, strengthening customer relationships in the process.

The following are 10 ways machine learning is revolutionizing marketing today and in the future:

  1. 57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support. 44% believe that AI and machine learning will provide the ability to improve on existing products and services. Marketing departments and the Chief Marketing Officers (CMOs) running them are the leaders devising and launching new strategies to deliver excellent customer experiences and are one of the earliest adopters of machine learning. Orchestrating every aspect of attracting, selling and serving customers is being improved by marketers using machine learning apps to more accurately predict outcomes. Source: Artificial Intelligence: What’s Possible for Enterprises In 2017 (PDF, 16 pp., no opt-in), Forrester, by Mike Gualtieri, November 1, 2016. Courtesy of The Stack.

  1. 58% of enterprises are tackling the most challenging marketing problems with AI and machine learning first, prioritizing personalized customer care, new product development. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in).

  1. By 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate. The combined effect of these marketing technology improvements will increase sales effectiveness in retail and B2C-based channels. Sales Qualified Lead (SQL) lead generation will also increase, potentially reducing sales cycles and increasing win rates. Source: Can Machines be Creative? How Technology is Transforming Marketing Personalization and Relevance, IDC White Paper Sponsored by Gerry Brown, July 2017.

  1. Analyze and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models. Instead of relying on expensive and time-consuming approaches to minimize customer churn, telecommunications companies and those in high-churn industries are turning to machine learning. The following graphic illustrates how defining risk models help determine how actions aimed at averting churn affect churn impact probability and risk. An intervention model allows marketers to consider how the level of intervention could affect the probability of churn and the amount of customer lifetime value (CLV). Source: Analyzing Customer Churn by using Azure Machine Learning.

  1. Price optimization and price elasticity are growing beyond industries with limited inventories including airlines and hotels, proliferating into manufacturing and services. All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period and the product’s position in an overall product line pricing strategy. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Improving demand forecasting, assortment efficiency and pricing in retail marketing have the potential to deliver a 2% improvement in Earnings Before Interest & Taxes (EBIT), 20% stock reduction and 2 million fewer product returns a year. In Consumer Packaged Goods (CPQ) and retail marketing organizations, there’s significant potential for AI and machine learning to improve the entire value chain’s performance. McKinsey found that using a concerted approach to applying AI and machine learning across a retailer’s value chains has the potential to deliver a 50% improvement of assortment efficiency and a 30% online sales increase using dynamic pricing. Source:  Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

  1. Creating and fine-tuning propensity models that guide cross-sell and up-sell strategies by product line, customer segment, and persona. It’s common to find data-driven marketers building and using propensity models to define the products and services with the highest probability of being purchased. Too often propensity models are based on imported data, built in Microsoft Excel, making their ongoing use time-consuming. Machine learning is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model.

  1. Lead scoring accuracy is improving, leading to increased sales that are traceable back to initial marketing campaigns and sales strategies. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies. The following two slides are from an excellent webinar Mintigo hosted with Sirius Decisions and Sales Hacker. It’s a fascinating look at how machine learning is improving sales effectiveness. Source: Give Your SDRs An Unfair Advantage with Predictive (webinar slides on Slideshare).

  1. Identifying and defining the sales projections of specific customer segments and microsegments using RFM (recency, frequency and monetary) modeling within machine learning apps is becoming pervasive. Using RFM analysis as part of a machine learning initiative can provide accurate definitions of the best customers, most loyal, biggest spenders, almost lost, lost customers and lost cheap customers.
  2. Optimizing the marketing mix by determining which sales offers, incentive and programs are presented to which prospects through which channels is another way machine learning is revolutionizing marketing. Specific sales offers are created supported by contextual content, offers, and incentives. These items are made available to an optimization engine which uses machine learning logic to continually try to predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell. Amazon’s product recommendation feature is an example of how their e-commerce site is using machine learning to increase up-sell, cross-sell and recommended products revenue.

Data Sources On Machine Learning’s Impact On Marketing:

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.
AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn.  Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.
Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,
How Artificial Intelligence and Machine Learning Will Reshape Small Businesses, SMB Group (PDF, 8 pp., no opt-in) May 2017.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence. May 15, 2017.

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) PwC, 2017.

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in)

Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog, March 29, 2017

What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.

Machine Learning’s Greatest Potential Is Driving Revenue In The Enterprise

  • Enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.
  • International Data Corporation (IDC) is forecasting spending on artificial intelligence (AI) and machine learning will grow from $8B in 2016 to $47B by 2020.
  • 89% of CIOs are either planning to use or are using machine learning in their organizations today.
  • 53% of CIOs say machine learning is one of their core priorities as their role expands from traditional IT operations management to business strategists.
  • CIOs are struggling to find the skills they need to build their machine learning models today, especially in financial services.

These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here.

Digital transformation is a cornerstone of machine learning adoption. 72% of CIOs have responsibility for digital transformation initiatives that drive machine learning adoption. The survey found that the greater the level of digital transformation success, the more likely machine learning-based programs and strategies would succeed. IDC predicts that 40% of digital transformation initiatives will be supported by machine learning and artificial intelligence by 2019.

Key takeaways from the study include the following:

  • 90% of CIOs championing machine learning in their organizations today expect improved decision support that drives greater topline revenue growth. CIOs who are early adopters are most likely to pilot, evaluate and integrate machine learning into their enterprises when there is a clear connection to driving business results. Many CIO compensation plans now include business growth and revenue goals, making the revenue potential of new technologies a high priority.
  • 89% of CIOs are either planning to use or using machine learning in their organizations today. The majority, 40%, are in the research and planning phases of deployment, with an additional 26% piloting machine learning. 20% are using machine learning in some areas of their business, and 3% have successfully deployed enterprise-wide. The following graphic shows the percentage of respondents by stage of their machine learning journey.

  • Machine learning is a key supporting technology leading the majority Finance, Sales & Marketing, and Operations Management decisions today. Human intervention is still required across the spectrum of decision-making areas including Security Operations, Customer Management, Call Center Management, Operations Management, Finance and Sales & Marketing. The study predicts that by 2020, machine learning apps will have automated 70% of Security Operations queries and 30% of Customer Management ones.

  • Automation of repetitive tasks (68%), making complex decisions (54%) and recognizing data patterns (40%) are the top three most important capabilities CIOs of machine learning CIOs are most interested in.  Establishing links between events and supervised learning (both 32%), making predictions (31%) and assisting in making basic decisions (18%) are additional capabilities CIOs are looking for machine learning to accelerate. In financial services, machine learning apps are reviewing loan documents, sorting applications to broad parameters, and approving loans faster than had been possible before.

  • Machine learning adoption and confidence by CIOs varies by region, with North America in the lead (72%) followed by Asia-Pacific (61%). Just over half of European CIOs (58%) expect value from machine learning and decision automation to their company’s overall strategy. North American CIOs are more likely than others to expect value from machine learning and decision automation across a range of business areas, including overall strategy (72%, vs. 61% in Asia Pacific and 58% in Europe). North American CIOs also expect greater results from sales and marketing (63%, vs. 47% Asia-Pacific and 38% in Europe); procurement (50%, vs. 34% in Asia-Pacific and 34% in Europe); and product development (48%, vs. 29% in Asia-Pacific and 29% in Europe).
  • CIOs challenging the status quo of their organization’s analytics direction are more likely to rely on roadmaps for defining and selling their vision of machine learning’s revenue contributions. More than 70% of early adopter CIOs have developed a roadmap for future business process changes compared with just 33% of average CIOs. Of the CIOs and senior management teams in financial services, the majority are looking at how machine learning can increase customer satisfaction, lifetime customer value, improving revenue growth. 53% of CIOs from our survey say machine learning is one of their core priorities as their role expands from traditional IT operations to business-wide strategy.

Sources: CIOs Cutting Through the Hype and Delivering Real Value from Machine Learning, Survey Shows

53% Of Companies Are Adopting Big Data Analytics

  • Big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.
  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
  • Data warehouse optimization remains the top use case for big data, followed by customer/social analysis and predictive maintenance.
  • Among big data distributions, Cloudera is the most popular, followed by Hortonworks, MAP/R, and Amazon EMR.

These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research. This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology.

“Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “In 2017, IT has emerged as the most typical adopter of big data, although all departments – including finance – are considering future use. This is an indication that big data is becoming less an experimental endeavor and more of a practical pursuit within organizations.”

Key takeaways include the following:

  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.  Big Data ranks 20th across 33 key technologies Dresner Advisory Services currently tracks.  Big Data Analytics is of greater strategic importance than the Internet of Things (IoT), natural language analytics, cognitive Business Intelligence (BI) and Location intelligence.

  • 53% of companies are using big data analytics today, up from 17% in 2015 with Telecom and Financial Services industries fueling the fastest adoption. Telecom and financial services are the most active early adopters, with Technology and Healthcare being the third and fourth industries seeing big data analytics Education has the lowest adoption as 2017 comes to a close, with the majority of institutions in that vertical saying they are evaluating big data analytics for the future. North America (55%) narrowly leads EMEA (53%) in their current levels of big data analytics adoption. Asia-Pacific respondents report 44% current adoption and are most likely to say they “may use big data in the future.”

  • Data warehouse optimization is considered the most important big data analytics use case in 2017, followed by customer/social analysis and predictive maintenance. Data warehouse optimization is considered critical or very important by 70% of all respondents. It’s interesting to note and ironic that the Internet of Things (IoT) is among the lowest priority use cases for big data analytics today.

  • Big data analytics use cases vary significantly by industry with data warehouse optimization dominating Financial Services, Healthcare, and Customer/social analysis is the leading use case in Technology-based companies. Fraud detection use cases also dominate Financial Services and Telecommunications. Using big data for clickstream analytics is most popular in Financial Services.

  • Spark, MapReduce, and Yarn are the three most popular software frameworks today. Over 30% of respondents consider Spark critical to their big data analytics strategies. MapReduce and Yarn are “critical” to more than 20 percent of respondents.

  • The big data access methods most preferred by respondents include Spark SQL, Hive, HDFS and Amazon S3. 73% of the respondents consider Spark SQL critical to their analytics strategies. Over 30% of respondents consider Hive and HDFS critical as well. Amazon S3 is critical to one of five respondents for managing big data access. The following graphic shows the distribution of big data access methods.

  • Machine learning continues to gain more industry support and investment plans with Spark Machine Learning Library (MLib) adoption projected to grow by 60% in the next 12 months. In the next 24 months, MLib will dominate machine learning according to the survey results. MLib is accessible from the Sparklyr R Package and many others, which continues to fuel its growth. The following graphic compares projected two-year adoption rates by machine learning libraries and frameworks.

The Best Software Companies To Work For In 2018, According To Glassdoor

These and other findings are based on an analysis of Glassdoor rankings of Software Magazine’s 2017 Software 500 list of the leading software companies globally. An Excel spreadsheet was first created using the 2017 Software 500 list as the basis of the Glassdoor company comparisons. Rankings from Glassdoor were added today for the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO.The Software 500 list was used to preserve impartiality in the rankings.  The original data set the analysis is based on is available for download here in Microsoft Excel format.

To gain greater insights into the data sets a series of cross-tabulations and correlation analyses were done using IBM SPSS Statistics Version 25. The analysis shows CEOs have an even greater impact on improving their company’s recommendation scores, rising to 82% this year from 70% in 2015. The analysis also showed that companies who flood Glassdoor with fake reviews hit a wall around 10 posts, down from 15 in 2015. This doesn’t stop some companies from offering cash, prizes, and merchandise to their employees in exchange for positive reviews. Relying on Glassdoor and ideally in-office visits to see how a company culture is and how your potential boss treats others is ideal.

The following are the highest rated software companies to work for in 2018, based the (%) of employees who would recommend the company to a friend:

The following companies scored between 80% and 89% on the rating % of employees who would recommend this company to a friend:

Please see the entire data set for the rankings of all companies included in the Software Magazine 500 here in Microsoft Excel format.

Cloud Computing Market Projected To Reach $411B By 2020

  • Worldwide public cloud services market revenue is projected to grow 18.5% in 2017 reaching $260.2B, up from $219.6B in 2016.
  • 2016 worldwide SaaS revenue exceeded Gartner’s previous forecast by $48.2B.
  • SaaS revenue is expected to grow 21% in 2017 reaching $58.6B by the end of this year.
  • Infrastructure as a Service (IaaS) is projected to grow 36.6% in 2017 alone, reaching $34.7B this year making this area the fastest growing of all cloud services today.

Gartner’s latest worldwide public cloud services revenue forecast published earlier this month predicts Infrastructure-as-a-Service (IaaS), currently growing at a 23.31% Compound Annual Growth Rate (CAGR), will outpace the overall market growth of 13.38% through 2020. Software-as-a-Service (SaaS) revenue is predicted to grow from $58.6B in 2017 to $99.7B in 2020. Taking into account the entire forecast period of 2016 – 2020, SaaS is on pace to attain 15.65% compound annual growth throughout the forecast period, also outpacing the total cloud market. The following graphic compares revenue growth by cloud services category for the years 2016 through 2020. Please click on the graphic to expand it for easier reading.

Catalysts driving greater adoption and correspondingly higher CAGRs include a shift Gartner sees in infrastructure, middleware, application and business process services spending. In 2016, Gartner estimates approximately 17% of the total market revenue for these areas had shifted to the cloud. Gartner predicts by 2021, 28% of all IT spending will be for cloud-based infrastructure, middleware, application and business process services. Another factor is the adoption of Platform-as-a-Service (PaaS). Gartner notes that enterprises are confident that PaaS can be a secure, scalable application development platform in the future.  The following graphic compares the compound annual growth rates (CAGRs) of each cloud service area including the total market. Please click on the graphic to expand it for easier reading.

Source: Gartner Forecasts Worldwide Public Cloud Services Revenue to Reach $260 Billion in 2017

Gartner’s Top 10 Predictions For IT In 2018 And Beyond

  • In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs.
  • By 2020, IoT technology will be in 95% of electronics for new product designs.
  • By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related.

These and many other insights are being presented earlier this month at the Gartner Symposium/ITxpo 2017 being held in Orlando, Florida. Gartner’s predictions and the series of assumptions supporting them illustrate how CIOs must seek out and excel in the role of business strategist first, technologist second. In 2018 and beyond CIOs will be more accountable than ever for revenue generation, value creation, and the development and launch of new business models using proven and emerging technologies. Gartner’s ten predictions point to the future of CIOs as collaborators in new business creation, selectively using technologies to accomplish that goal.

The following are Gartner’s ten predictions for IT organizations for 2018 and beyond:

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%. Gartner has found that voice-based search queries are the fastest growing mobile search type. Voice and visual search are accelerating mobile browser- and mobile app-based transactions and will continue to in 2018 and beyond. Mobile browser and app-based transactions are as much as 50% of all transactions on many e-commerce sites today. Apple, Facebook, Google and Microsoft’s investments in AI and machine learning will be evident in how quickly their visual- and voice-search technologies accelerate in the next two years.
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity. The top digital giants include Alibaba, Amazon, Apple, Baidu, Facebook, Google, Microsoft, and Tencent. Examples of self-disruption include AWS Lambda versus traditional cloud virtual machines, Alexa versus screen-based e-commerce, and Apple Face ID versus Touch ID.
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies. Gartner estimates that the current combined value of cryptocurrencies in circulation worldwide is $155B (as of October 2017), and this value has been increasing as tokens continue to proliferate and market interest grows. Cryptocurrencies will represent more than half of worldwide blockchain global business value-add through year-end 2023 according to the Gartner predictions study.
  4. By 2022, most people in mature economies will consume more false information than true information. Gartner warns that while AI is proving to be very effective in creating new information, it is just as effective at distorting data to create false information as well. Gartner predicts that before 2020, untrue information will fuel a major financial fraud made possible through high-quality falsehoods moving the financial markets worldwide. By the same year, no significant internet company will fully succeed in its attempts to mitigate this problem. Within three years a significant country will pass regulations or laws seeking to curb the spread of AI-generated false information.
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust. AI and machine learning systems today can categorize the content of images faster and more consistently accurate than humans. Gartner cautions that by 2018, a counterfeit video used in a satirical context will begin a public debate once accepted as real by one or both sides of the political spectrum. In the next year, there will be a 10-fold increase in commercial projects to detect fake news according to the predictions study.
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chatbot creations than traditional mobile app developments. Gartner is predicting that by 2020, 55% of all large enterprises will have deployed (used in production) at least one bot or chatbot. Rapid advances in natural-language processing (NLP) make today’s chatbots much better at recognizing the user intent than previous generations. According to Gartner’s predictions study, NLP is used to determine the entry point for the decision tree in a chatbot, but a majority of chatbots still use scripted responses in a decision tree.
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related. By 2019, IT technical specialist hires will fall by more than 5%. Gartner predicts that 50% of enterprises will formalize IT versatilist profiles and job descriptions. 20% of IT organizations will hire versatilists to scale digital business. IT technical specialist employees will fall to 75% of 2017 levels.
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs. By 2020, AI-related job creation will cross into positive territory, reaching 2 million net-new jobs in 2025. Global IT services firms will have massive job churn in 2018, adding 100,000 jobs and dropping 80,000. By 2021 Gartner predicts, AI augmentation will generate $2.9T in business value and recover 6.2B hours of worker productivity.
  9. By 2020, IoT technology will be in 95% of electronics for new product designs. Gartner predicts IoT-enabled products with smartphone activation emerging at the beginning of 2019.
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection. Gartner predicts IoT spending will increase sharply after 2020 following better methods of applying security patterns cross-industry in IoT security architectures, growing at more than 50% compound annual growth rate (CAGR) over current rates.The total IoT security market for products will reach $840.5M by 2020, and a 24% CAGR for IoT security from 2013 through 2020. Combining IoT security services, safety systems, and physical security will lead to a fast-growing global market. Gartner predicts exponential growth in this area, exceeding more than $5B in global spending by year-end 2020.

Gartner has also made an infographic available of the top 10 Strategic Technology Trends for 2018, in addition to an insightful article on Smarter with Gartner.  You can find the article here, at Gartner Top 10 Strategic Technology Trends for 2018.

Sources:

Gartner Reveals Top Predictions for IT Organizations and Users in 2018 and Beyond

Smarter With Gartner, Gartner Top 10 Strategic Technology Trends for 2018

Top Strategic Predictions for 2018 and Beyond: Pace Yourself, for Sanity’s Sake (client access reqd)

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