15% of U.S. tech leaders see biotech/digital health/healthcare IT as the most disruptive consumer-driven technology in the next three years.
13% of U.S. tech leaders predict data and analytics will be the most disruptive enterprise technology in three years.
Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/machine-to-machine (M2M) (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.
These and many other insights are from the fourth annual 2015 Global Technology Innovation Survey released via webcast by KPMG last month. KPMG surveyed 832 technology industry business leaders globally, with the majority of being C-level executives (87%). Respondents were selected from a broad spectrum of businesses including tech industry startups, mid- and large-scale enterprises, angel investors and venture capital firms. For an in-depth explanation of the survey methodology, please see slides 6 and 7 of the webinar presentation. The goals of the survey include spotting disruptive technologies, identifying tech innovation barriers and opportunities, and tracking emerging tech innovation hubs.
The five insights and predictions from the report include the following:
Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/M2M (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.U.S. tech leaders predict biotech/digital health/healthcare IT (15%), data and analytics (14%) and cloud computing (14%) will be the three most disruptive technologies over the next three years. Chinese tech leaders predict artificial intelligence/cognitive computing (15%) will be the most disruptive technology impacting the global business-to-consumer (B2C) marketplace.
The three most disruptive technologies predicted to drive business transformation in enterprises over the next three years in the U.S. include cloud computing (13%), data and analytics (13%), and cyber security (10%). Japanese tech leaders predict artificial intelligence/cognitive computing will have the greatest effect (23%), and 14% of Chinese tech leaders predict the Internet of Things/M2M (14%) will have the greatest impact on business transformation in their country. The following table compares global tech leader’s predictions of which technologies will disrupt enterprises the most and drive business transformation over the next three years.
Improving business efficiencies/higher productivity, and faster innovation cycles (both 20%) are top benefits tech leaders globally are pursuing with IoT strategies. The point was made on the webinar that in Asia, consumers are driving greater adoption of IoT-based devices to a richer contextual customer experience. Greatest challenges globally to adopting IoT is technology complexity (22%), lack of experience in the new technology or business model (16%), and both displacement of the existing tech roadmap and security (both 13%).
Analytics are most often adopted to gain faster innovation cycles (25%), improved business efficiencies and higher productivity (17%) and more effective R&D (13%). The greatest challenges are technology complexity (20%) and lack of experience in the new technology or business model (19%),
Tech leaders predict the greatest potential revenue growth for IoT in the next three years is in consumer and retail markets (22%). IoT/M2M is also expected to see significant revenue growth in technology industries (13%), aerospace and defense (10%), and education (9%). The following graphic compares tech leader’s predictions of the industries with the greatest potential revenue growth (or monetization potential) in the next three years.
Between 2015 and 2020, the number of data sources analyzed by enterprises will jump 83%.
9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes.
54% of marketers say marketing analytics is absolutely critical or very important to creating a cohesive customer journey.
High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data.
These and many other interesting insights are from the 2015 State of Analytics study from Salesforce Research. Salesforce conducted the study in mid-2015, generating 2,091 responses from business leaders from enterprises (not limited to Salesforce customers). Geographies included in the study include the U.S., Canada, Brazil, U.K., France, Germany, Japan, and Australia. While Salesforce is a leading provider of analytics, the report strives to deliver useful insights beyond just endorsing their product direction.
10 insights and predictions on the state of analytics include the following:
Between 2015 and 2020, the number of data sources analyzed will jump 83%. Salesforce Research found that the number of data sources actively analyzed by businesses has grown just 20% in the last five years. This is projected to accelerate rapidly, attaining a compound annual growth rate of 120% in the 10-year forecast period. High performing enterprises will be relying on a projected 50 different data sources by 2020, leading all performance categories tracked in the study.
Relying on manual processes to get all the data in one view (53%) is one of the greatest challenges enterprises face today. Additional factors driving enterprises to integrate more data sources into their analytics applications include finding that too much data is left unanalyzed (53%), spending too much time updating spreadsheets (52%), and analysis is performance by business analysts, not end users of the data (50%). All of these factors and those shown in the graphic below form the catalyst that is driving greater legacy, 3rd party and broader enterprise data integration into analytics applications.
9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes. In addition, 84% of high performers are projecting that the importance of analytics will increase substantially or somewhat in the next two years. 65% of all business leaders surveyed are predicting that the importance of analytics will increase substantially or somewhat in the next two years.
High performing enterprises are 4.6x more likely than underperformers to agree that data is driving their business decisions. In addition, 60% of high performing enterprises’ leaders agree with the statement that their organizations have moved beyond numbers keeping score to data driving business decisions. Salesforce Research also found that 43% of high performers rely on empirical data, developing hypotheses and then experimenting and observing the outcomes before making a decision.
Driving operational efficiencies and facilitating growth (both 37%) are the two areas enterprises are initially focused on with analytics today. Once analytics apps are delivering insights and are part of daily workflows, enterprises expand their use into optimizing operational processes (35%), identifying new revenue streams (33%) and predicting customer behavior (32%). The following graphic provides a comparison of the top ten use cases.
High performance enterprises consistently analyze more than 17 different kinds of data across their analytics apps. In contrast, underperforming organizations only analyze 10 different data sources, and moderate performers, 15. The following graphic provides an overview of the top ten most-used sources of data.
High performers are 3.5x more likely than underperformers to extensively use mobile reporting tools to analyze data wherever they are. 55% of high performing enterprises are more likely to be extensively using mobile reporting tools to analyze data. The following graphic compares mobile analytics adoption across high, medium and low performing enterprises.
Speed of deployment (68%), ease of use for business users (65%) and self-service and data discovery tools (61%) are the three top three priorities leaders place on selecting new analytics apps. Mobile capabilities to explore and share data (56%) and cloud deployment (54%) are the fourth and fifth factors leaders mentioned. The following graphic compares the decision factors that go into selecting an analytics app.
Industries who have the greater analytics adoption today (over 50% of users active on apps and tools) include high tech (36%) and financial services (32%). Automotive (30%) and media & communications (30%) also have attained significant adoption.
High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data. Leaders in high performance enterprises see the value of Big Data (76%) to a much greater extent than their lower performing counterparts (14%). High performing enterprises are 3.1x more likely than underperformers to be confident in ability to manage data from internal systems, customers, and third parties.
This year’s survey attracted an eclectic base of respondents, with median revenues of $4M a year, with 133 companies reporting less than $5M, and 57 over $25M. Annual Contract Value (ACV) across all respondents is $21K, with 17% of respondents reporting ACVs over $100K. Please see pages 3 & 4 of the study for a description of the methodology. Key take-aways from the study include the following:
SaaS GAAP revenue growth is accelerating in 2014 and is projected to increase further in 2015 from 44% to 46%. Median revenue growth in 2014 for all survey respondents was 44%, with the aggregate projected growth for 2015 reaching 46%. When SaaS companies with less than $2.5M in revenues are excluded, median GAAP growth was 35% in 2014 and is expected to reach that same level in 2015.
SaaS companies with mixed customer strategies are growing at 57% a year. Excluding respondent companies with less than $2.5M in revenues, a mixed customer strategy dominates all others. Concentrating on enterprises and small & medium businesses (SMBs) both drove 33% revenue growth of respondent companies this year.
40% of SaaS companies are using Amazon Web Services (AWS) to deliver their apps today. AWS is projected to increase to 44% three years from now, with Microsoft Azure increasing from 3% today to 6% in 3 years.
41% of all SaaS companies surveyed rely primarily on field sales. Factoring out the companies with less than $2.5M in revenue, field sales accounts for 32%.
Field sales dominates as the most effective sales strategy when median deal sizes are $50K or more. In contrast, inside sales dominates $5K to $15K deal sizes, and the Internet dominates deal sizes less than $1K. The following graphic provides insights into the primary mode of sales by median initial contract size.
16% of new Average Contract Value (ACV) sales is from upsells, with the largest companies being the most effective at this selling strategy. One of the strongest catalysts of a SaaS companies’ growth is the ability to upsell customers to a higher ACV, generating significantly greater gross margin in the process. SaaS companies with revenues between $40M to $75M increase their ACV by 32% using upsells. Larger SaaS companies with over $75M in sales generate 28% additional ACV with upsell strategies.
The highest growth SaaS companies are relying on upsells to fuel higher ACV. There is a significant difference between the highest and lowest growth SaaS companies when it comes to upsell expertise and execution. The following graphic provides an overview by 2014 GAAP revenue category of percent of ACV attributable to upsells.
60% are driving revenues with “Try Before You Buy” strategies, with 30% generating the majority of their revenues using this approach. On contrast, only 30% of companies generate revenues and ACV from freemium.