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Feb 27

Roundup Of Machine Learning Forecasts And Market Estimates, 2018

  • Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted.
  • International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021.
  • Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.

These and many other fascinating insights are from the latest series of machine learning market forecasts, market estimates, and projections. Machine learning’s potential impact across many of the world’s most data-prolific industries continues to fuel venture capital investment, private equity (PE) funding, mergers, and acquisitions all focused on winning the race of Intellectual Property (IP) and patents in this field. One of the fastest growing areas of machine learning IP is the development of custom chipsets. Deloitte Global is predicting up to 800K machine learning chips will be in use across global data centers this year. Enterprises are increasing their research, investment, and piloting of machine learning programs in 2018. And while the methodologies all vary across the many sources of forecasts, market estimates, and projections, all reflect how machine learning is improving the acuity and insights of companies on how to grow faster and more profitably. Key takeaways from the collection of machine learning market forecasts, market estimates and projections include the following:

  • Within the Business Intelligence (BI) & analytics market, Data Science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021. Data Science platforms will outperform the broader BI & analytics market, which is predicted to grow at an 8% CAGR in the same period. Data Science platforms will grow in value from $3B in 2017 to $4.8B in 2021. Source: An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in).

  • Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted. IBM, Microsoft, Google, LinkedIn, Facebook, Intel, and Fujitsu were the seven biggest ML patent producers in 2017. Source: IFI Claims Patent Services (Patent Analytics) 8 Fastest Growing Technologies SlideShare Presentation.

  • 61% of organizations most frequently picked Machine Learning / Artificial Intelligence as their company’s most significant data initiative for next year. Of those respondent organizations indicating they actively use Machine Learning (ML) and Artificial Intelligence (AI), 58% percent indicated they ran models in production. Source: 2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals (14 pp., PDF, no opt-in).

  • Tech market leaders including Amazon, Apple, Google, Tesla, and Microsoft are leading their industry sectors by a wide margin in machine learning (ML) and AI investment. Each is designing ML into future-generation products and using ML and AI to improve customer experiences and improve the efficiency of selling channels. Source: Will You Embrace AI Fast Enough? AT Kearney, January 2018.

  • Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020. Factors driving the increasing pace of ML pilots include more pervasive support of Application Program Interfaces (APIs), automating data science tasks, reducing the need for training data, accelerating training and greater insight into explaining results. Source: Deloitte Global Predictions 2018 Infographics.

  • 60% of organizations at varying stages of machine learning adoption, with nearly half (45%) saying the technology has led to more extensive data analysis & insights. 35% can complete faster data analysis and increased the speed of insight, delivering greater acuity to their organizations. 35% are also finding that machine learning is enhancing their R&D capabilities for next-generation products. Source: Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

  • McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area. The following graphic illustrates the distribution of external investments by category from the study. Source: McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in).

  • Deloitte Global is predicting machine learning chips used in data centers will grow from a 100K to 200K run rate in 2016 to 800K this year. At least 25% of these will be Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASICs). Deloitte found the Total Available Market (TAM) for Machine Learning (ML) Accelerator technologies could potentially reach $26B by 2020. Source: Deloitte Global Predictions 2018.

  • Amazon is relying on machine learning to improve customer experiences in key areas of their business including product recommendations, substitute product prediction, fraud detection, meta-data validation and knowledge acquisition. For additional details, please see the presentation, Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

Sources of Market Data on Machine Learning:

2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals. MEMSQL. (14 pp., PDF, no opt-in)

Advice for applying Machine Learning, Andrew Ng, Stanford University. (30 pp., PDF, no opt-in)

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

An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in)

Artificial intelligence and machine learning in financial services Market developments and financial stability implications, Financial Stability Board. (45 pp., PDF, no opt-in)

Big Data and AI Strategies Machine Learning and Alternative Data Approach to Investing, J.P. Morgan. (280 pp., PDF. No opt-in).

Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

Hitting the accelerator: the next generation of machine-learning chips, Deloitte. (6 pp., PDF, no opt-in).

How Do Machines Learn? Algorithms are the Key to Machine Learning. Booz Allen Hamilton. (Infographic)

IBM Predicts Demand For Data Scientists Will Soar 28% By 2020, Forbes. May 13, 2017

Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

Machine Learning Evolution (infographic). PwC. April 17, 2017 Machine learning: things are getting intense. Deloitte (6 pp., PDF. No opt-in)

Machine Learning: The Power and Promise Of Computers That Learn By Example. The Royal Society’s Machine Learning Project (128 pp., PDF, no opt-in)

McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in)

McKinsey’s State Of Machine Learning And AI, 2017, Forbes, July 9, 2017

Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution. Forrester, November 2, 2016 (9 pp., PDF, no opt-in)

Risks And Rewards: Scenarios around the economic impact of machine learning, The Economist Intelligence Unit. (80 pp., PDF, no opt-in)

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? Digital/McKinsey & Company. (52 pp., PDF, no opt-in)

So What Is Machine Learning Anyway?  Business Insider. Nov. 23, 2017

The 10 Most Innovative Companies In AI/Machine Learning 2017, Wired

The Business Impact and Use Cases for Artificial Intelligence. Gartner (28 pp., PDF, no opt-in)

The Build-Or-Buy Dilemma In AI, Boston Consulting Group. January 4, 2018.

The Next Generation of Medicine: Artificial Intelligence and Machine Learning, TM Capital (25 pp., PDF, free, opt-in)

The Roadmap to Enterprise AI, Rage Networks Brief based on Gartner research. (17 pp., PDF, no opt-in)

Will You Embrace AI Fast Enough? AT Kearney. January 2018

 

Nov 8

1 Comment

Salesforce On The State Of Analytics, 2015

  • analytics predictions 2015Between 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.

data explosion

  • 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.

lack of automation

  • 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.

analytics is critical to driving business strategy

  • 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.

data drives decisions

  • 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.

analytics every corner

  • 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.

companies track a wide variety 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.

top teams tap mobile analytics

  • 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.

decision factor 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.

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.

Mar 12

Why Cloud Computing Is Slowly Winning The Trust War

Cloud computing Seeing skeptical CIOs agree to cloud-based pilots of Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) and other applications is evidence of how cloud computing is slowly winning the trust war.

Further evidence can be seen from how skeptical many of these CIOs initially were, and how successful pilots led to their gradual trust.

This trust hasn’t come cheap however.

Every one of these CIOs spoken with, across a range of manufacturing companies, learned that Service Level Agreements (SLAs) aren’t sufficient to manage the areas of security, privacy and confidentiality on their own.  Cloud computing vendors have used SLAs as a means to imply security standards are met; one CIO told me he had an audit done to see if the SLA targets promised were realistic.  They weren’t and he moved on to another vendor.  That is the level of skepticism and lack of trust many CIOs initially have about the cloud today.  Add to that how much Europe doesn’t trust the cloud, and any CIO of a manufacturing or services business that has operations globally has ample reason to be skeptical about cloud computing.  The highly visible failures of Amazon, Apple, Google, Microsoft continues to fuel skepticism and distrust of cloud computing as well.

Despite these factors, cloud computing is slowing winning the trust war.  Here are the key take-aways from my conversations and visits with CIOs and their departments over the last two weeks:

  • Service Level Agreement (SLA) claims of security, privacy and confidentiality often only partially cover the unique needs of a given business – rarely all of them.  CIOs complained that the SLAs they were initially given for cloud pilots by vendors lacked any insight into their core business, how it operated, and how the cloud-based applications could contribute greater insight and intelligence.  Only after several revisions and additions of performance measurements tied to business strategies did these skeptical CIOs let the pilots go on.  Model contracts for defining privacy, for these CIOs, are also losing credibility.  These CIOs forced the issue of a highly specific privacy plan from vendors and got them.
  • For global cloud deployments, CIOs viewed the development a roadmap and plan for how to deal with transborder data flow restrictions and in-country compliance for data confidentiality, security and personal information protection as critical.  One manufacturing CIO is setting up a two-tier ERP system throughout Europe has to first define the global privacy regulations across each nation and province.  Depending on the European nation this could include defining the physical location, contents and specific configuration of every server used.  Germany has among the most intensive data protection rules and requirements, which further require intensive roadmap and plan development to stay in compliance.
  • The most skeptical CIOs run scenario tests of full data and record extractions during pilots.  This is a safeguard in case the relationship with the cloud provider goes badly, and also to make sure they can quickly get their data back and avert vendor lock-in.  As part of this many CIOs want to see proof that data deletion has worked correctly on the provider’s servers.
  • The most trustworthy cloud computing pilots quickly move beyond basic analytics including ROI to deliver expertise and knowledge specific to the clients’ business.  This is the most powerful dynamic of all in the victories cloud computing is having in the trust war.  When a cloud pilot moves beyond showing how it can automate a process – say payroll for example – and starts making contributions to the expertise and knowledge of a company, trust grows quickly.   At that point trust becomes an accelerator for cloud computing and the platform and applications become part of the IT strategy of a business.

Bottom line:  Trust is the greatest accelerator there is in cloud computing’s growing adoption, and that’s earned when cloud applications get beyond simple metrics to delivering insights and useful intelligence on secured platforms.

Thank you Cindy Jutras and Lisa Lincoln for your contributions and insights on this as well.

Additional Reading and References:

Demirkan, H., & Goul, M. (2013). Taking value-networks to the cloud services: Security services, semantics and service level agreements. Information Systems and eBusiness Management, 11(1), 51-91.

Khan, K. M., & Malluhi, Q. (2010). Establishing trust in cloud computing. IT Professional Magazine, 12(5), 20-27.

John C. Roberts, II , Wasim Al-Hamdani, Who can you trust in the cloud?: a review of security issues within cloud computing, Proceedings of the 2011 Information Security Curriculum Development Conference, p.15-19, September 30-October 01, 2011, Kennesaw, Georgia

Rodero-Merino, L., Vaquero, L. M., Caron, E., Muresan, A., & Desprez, F. (2012). Building safe PaaS clouds: A survey on security in multitenant software platforms. Computers & Security, 31(1), 96. Link: http://hal.archives-ouvertes.fr/docs/00/65/73/06/PDF/RR-7838.pdf

Mar 1

How Cloud Computing Is Redefining the M&A Landscape

Cloud Computing M&AIn 2013, expect to see the pace of mergers and acquisitions for cloud computing, mobile and analytics technologies accelerate as software vendors look to fill gaps in their product and service strategies. This and other key insights of how cloud computing is reshaping the merger and acquisition landscape can be found in the latest Price Waterhouse Coopers (PwC) report published today.

The US Technology M&A insights: Analysis and Trends in US Technology M&A Activity 2013 provides an excellent overview of merger, acquisitions, private equity, divestures, cross-border transactions across the five key industry sectors.  The report, free for download, covers the Internet, IT Services, hardware and networking, software, and semiconductor sectors.

Enterprise Software Players: In Search of Sticky Revenue and Higher Margins

The major catalysts driving cloud deals forward in 2013 are enterprise software companies’ need to redefine their business models and find sources of sticky revenue that can replace for many of them, dwindling maintenance revenue streams.  Knowing that the annuity model of cloud computing works best with multiyear payments required at the beginning of a customer engagement, enterprise software companies are looking to strengthen this area of their product portfolios.  Third, the faster cloud acquisitions can be integrated into their legacy systems, the more upsell can be achieved with their large installed bases of customers.  The greatest challenge many of them face however is selling entirely new cloud applications to entirely new customers they’ve never sold to before.  The potential of these entirely new markets however is going to be a valuation multiplier in 2013 and beyond.

Here are the key take-aways from PwC’s report:

  • Software and Internet deals represented 57% of transactions closed in 2012, a figure that PwC has seen steadily grow over the last two years. Cumulative value for software and Internet deals represented 53% of total 2012 deal value, an increase from 51% in 2011. Software deals represented over a third of 2012 technology deals, generating 35% of deal volume and 36% of deal value for the year   A comparison of both years and technology sectors are shown in the following graphic:

Figure 1 PWC Report

  • PwC takes a cautionary, conservative tone in this report showing how overall IT spending growth finished the year at an anemic 1.2% while technology deal volumes and values dropped by just under 20% from the prior year.
  • The report cites Gartner and Forrester’s optimistic IT spending forecasts for IT growth predicting a recovery in 2013 followed by accelerating growth in 2014 according to Forrester.
  • PwC is seeing SaaS, mobile devices, analytics and Big Data as the drivers of current and future M&A growth and a fundamental shift in deal volumes to software and Internet deals based on these technologies.  The report says the most promising areas of M&A activity in 2013 are mobile application development start-ups who have the intellectual property it would take years for enterprise software companies to create on their own.
  • Analytics will move from being a differentiator to the cost of doing business, a key point made in the PwC analysis.  PwC claims that analytics M&A will accelerate across all enterprise software vendors as they seek to fill gaps in their product and service strategies, and position themselves for growth in specific areas of the emerging industries using Big Data.
  • PwC reports that monthly deal volumes for software remained relatively even throughout 2012, hovering at 8-9 transactions per month and averaging just over 20 per quarter. The average deal value of $433M for 2012 was slightly lower than 2011 levels of $438M but an increase in the number of deals in excess of $500M helped to keep average deal values high. The report also shows how 2012 saw 18 deals (21% of volume) in excess of $500M closed, the majority of which closed in the latter half of the year. Fourteen deals greater than $1B closed in 2012, an increase of 8 deals (133%) over 2011.  The following is a graphic comparing software sector deals by volume and value:
Figure 2 PWC Report

 Bottom line: The land grab is on for intellectual property in the fields of mobile application development, analytics and cloud computing as enterprise software vendors look to fill gaps in their product and service strategies.

Dec 27

3 Comments

How Cloud Computing Is Accelerating Context-Aware Coupons, Offers and Promotions

Retailers and marketers often face the challenge of getting coupons, offers and promotions delivered at the perfect time and in the right context to their customers.

The rapid advances in cyber foraging, contextual computing and cloud computing platforms are succeeding at revolutionizing this aspect of the retail shopping experience.  Context-aware advertising platforms and strategies can also provide precise audience and segment-based messaging directly to customers while they are in the store or retail outlet.

What makes context-aware advertising so unique and well adapted to the cloud is the real-time data integration and contextual intelligence they use for tailoring and transmitting offers to customers.  When a customer opts in to retailer’s contextually-based advertising system, they are periodically sent alerts, coupons, and offers on products of interest once they are in or near the store.  Real-time offer engines chose which alerts, coupons or offers to send, when, and in which context.  Cloud-based analytics and predictive modeling applications will be used for further fine-tuning of alerts, coupons and offers as well.  The ROI of each campaign, even to a very specific audience, will be measurable.  Companies investing in cloud-based contextual advertising systems include Apple, Google, Greystripe, Jumptap, Microsoft, Millennial Media, Velti and Yahoo.

Exploring the Framework of Me Marketing and Context-Aware Offers

A few years ago, a student in one of my MBA courses in international marketing did their dissertation on cyber foraging and contextual mobile applications’ potential use for streamlining business travel throughout Europe.  As a network engineer for Cisco at the time, he viewed the world very systemically; instead of getting frustrated with long waits he would dissect the problem and look at the challenges from a system-centric view.  The result was a great dissertation on cyber foraging and the potential use of Near Field Communications (NFC) and Radio Frequency Identification (RFID) as sensors to define contextual location and make business travel easier.  One of the greatest benefits of teaching, even part-time, is the opportunity to learn so much from students.

I’ve been following this area since, and when Gartner published Me Marketing: Get Ready for the Promise of Real-Time, Context-Aware Offers in Consumer Goods this month I immediately read it.  Gartner is defining Me Marketing as real-time, context-aware offers in grocery stores. Given the abundance of data on transactions that occur in grocery stores, Gartner is predicting this will be the most popular and fastest-growing area of context-aware offers.  The formula for Me Marketing is shown below:

The four steps of the Me Marketing formula are briefly described as follows:

Me marketing framework for contextual coupons

 

  • Consumer Insight and Permission – The first step of the framework and the most difficult from a change management standpoint, this requires customers to opt in to receiving alerts, coupons, offers and promotions.  The best retailers also have invested heavily in security and authentication technologies here too.
  • Delivery Mechanism and In-the-Moment Context – The real-time offer engine is used to determining which coupons, offers and promotions are best suited for a specific customer based on their shopping patterns, preferences and locations.
  • Select Best Offer – Next, the real-time offer engine next defines a very specific product or service offer based on location, previous purchase history, social media analysis, predictive and behavioral analysis, and previous learned patterns of purchasing.
  • Redemption – The purchase of the item offered.  Initial pilots have shown that less frequent yet highly relevant, targeted offers have a higher redemption rate.  It is encouraging to see that early tests of these systems show that spamming customers leads to immediate opt-outs and in some cases shopping competitors.

A Short Overview of Contextual Advertising and the Cloud

Cloud-based systems and applications are necessary for retailers to gain the full value that contextual advertising can provide.  This includes the social context, with specific focus on aggregation and analysis of Social CRM, CRM, and social media content, in addition to behavioral analytics and sentiment analysis.  It also includes the previous browsing, purchasing, returns and prices paid by product for each customer.  Cloud-based integration architectures are necessary for making contextual advertising a reality in several hundred or even thousands of retail stores at the same time.

Geographical data and analysis is also essential.  RFID has often been included in cyber foraging and contextual advertising pilots, in addition to NFC.  As Global Positioning System (GPS) chip sets have dropped in price and become more accurate, companies including Google, Microsoft and Yahoo are basing their contextual advertising platforms on them.  Finally the activity or task also needs to have a contextual definition.

Combining all three of these elements gives the context of the customer in the retail store.  The figure below is from Three-Dimensional Context-Aware Tailoring of Information.  This study also took into account how personas are used by companies building cloud-based contextual advertising systems.  The taxonomies shown in the figure are used for building personas of customers.

context aware technology

There are many pilot projects and enterprise-wide system tests going on right now in the area of cloud-based contextual advertising.  One of the more interesting is an application suite created entirely on Google App Engine, Android, and Cloud Services.  The pilot is explained in the study Exploring Solutions for Mobile Companionship: A Design Research Approach to Context-Aware Management.  The following figure shows a diagram of the suite.  This pilot uses Cloud to Device Messaging (C2DM) which is part of the Android API to link the Google App Engine server and Android client.  Google will most likely add more depth of support for C2DM as it plays a critical role in contextual system development.

context aware Google Ad Platform

Benefits of a Cloud-based Contextual Advertising Platform

For the customer, cloud-based advertising systems over time will learn their preferences and eventually impact the demand planning and forecasting systems of retailers.  This translates into the customer-centric benefits of products being out of stock less.  In addition, customers will receive more relevant offers.  The entire shopping experience will be more pleasant with expectations being met more often.

For the retailer, better management of product categories and more effective gross margin growth will be possible. Having real-time analytics of each coupon, offer and promotion will also give them immediate insights into which of their selling strategies are working or not.

For the manufacturer, the opportunity to finally understand how customers respond at the store level to promotions, programs including the results of co-op funds investment and pricing strategies will be known.  The manufacturers who partner with retailers using these systems will also have the chance at attaining greater product differentiation as their coupons, offers and promotions will only go to the most relevant customers.

References:

Me Marketing: Get Ready for the Promise of Real-Time, Context-Aware Offers in Consumer Goods Published: 24 December 2012 Analyst(s): Don Scheibenreif, Dale Hagemeyer

Tor-Morten Grønli, Ghinea, G., & Bygstad, B. (2013). Exploring Solutions for Mobile Companionship: A Design Research Approach to Context-Aware Management. International Journal of Information Management, 33(1), 227. http://www.sciencedirect.com/science/article/pii/S0268401212001259

Tor-Morten Grønli, & Ghinea, G. (2010). Three-Dimensional Context-Aware Tailoring of Information. Online Information Review, 34(6), 892-906. http://www.emeraldinsight.com/journals.htm?articleid=1896452

Aug 27

3 Comments

How Google is Driving Mobile Video Market Growth

Google’s top advertising customers are pushing for convergence of mobile and video quickly, which is turning into a strong catalyst of growth of the global mobile video market.  With their largest advertising customers wanting greater flexibility in bringing video to mobile devices, Google will make significant strides this year to make that happen.

During their latest earnings call, Google execs said that Android, Chrome and YouTube are the highest priority areas of their business. I’ve been following the last year of earnings calls closely, and it’s clear that Google’s largest advertising customers are pushing the company to bring video to mobile at a level of performance and usability not accomplished yet.  The Q2, 2012 earnings call transcript makes this point clear which can be accessed here Google’s Management Discusses Q2 2012 Results – Earnings Call Transcript.

 Mobile and Video: Transforming Convergence Into Cash

Over the last year, Google executives have mentioned the growth of YouTube and its quick evolution from a content management system to a profitable advertising platform.   During the Q1, 2012 earnings call held on April 12, 2012 the following points were made:

  • Google reported they had over 800 million monthly users uploading over an hour of video per second
  • U.K. mobile operator O2 used YouTube as the foundation of a brand launch that year with support for 100 new original channels completed and launched
  • Global product launch plans from GM, Toyota and Unilever and several other large advertising accounts are also underway

During the Q2, 2012 earnings call, Nikesh Arora, Senior Vice President and Chief Business Officer started his comments regarding the YouTube business with the statement “I think in 2007 it was when newspapers frequently said YouTube is groping for an effective business model. I think we can declare we found our model.” Immediately after making this statement, Mr. Arora mentioned that yearly account signups have doubled year-over-year and users are uploading over 72 hours of video every minute.  He also mentioned that  “thousands of partners are making six figures and we’re proud to work with major record labels in Hollywood studios on this platform.”

The call continued with the points made of Danish advertisers shifting their television advertising dollars to YouTube and other Google branding solutions.  Additional companies mentioned on the call using YouTube-based advertising include Denon, Shire, and Intel.  Clearly these companies have major product introductions coming up and see mobile video as perfect for reaching more potential customers than ever before.

Google’s Challenge: Keep Content Quality and User Experience Constantly Improving

If Google is going to attain the full revenue potential of YouTube as an advertising platform, they’ll need to focus on the following factors:

  • Create Application Programmer Interfaces (APIs) and easy-to-use programming tools for quickly creating mobile-optimized sites.  As Gartner studies have shown, video on telephones is most often used as a time-filler, with a median length of 2 minutes, 46 seconds.
  • YouTube will need to support more optimized mobile-based video browsers that can support contextual search.  This will be a core requirement for the enterprise, specifically in the areas of mobile customer care, mobile commerce and mobile health.
  • More extensive analytics in YouTube than are available today, specifically tying into to major marketing strategies including product introductions.  It is becoming common knowledge that videos improve viewer engagement and prospects attribute a more positive shopping experience when they are used.  Luxury brands are investing heavily in this technology including BMW, Burberry, Channel, Louis Vuitton and many others.
  • A Google/Ipsos OTX MediaCT smartphone users study completed in April, 2011 shows that 77% of smartphone users said that their most visited site was a mobile search engine.

Mobile Video: The Market YouTube Built

The size of the worldwide mobile video market was comprised of 429 million mobile video users in 2011, projected to grow exponentially to 2.4 billion users by 2016.  Smartphones and tablet sales will contribute 440 million new mobile video users during the forecast period.  These market estimates are from the recently published Gartner report, Market Trends: Worldwide, the State of Mobile Video, 2012.

Additional take-aways from this report include the following:

  • Allot Communication’s reports that mobile streaming grew 93% in the first half of 2011; Allot also reports that the usage of YouTube’s mobile channel grew by 152% and YouTube generated 22% of all mobile video traffic in the first half of 2011.  YouTube reports getting 400 million video views a month globally.
  • Gartner reports from a survey completed in the 4th quarter of 2010 that 32% of mobile enterprise users watch short videos from YouTube and other sites optimized for video streaming.
  • The fastest growth for mobile video will be in Latin America as smartphone adoption continues to accelerate, replacing traditional cell phones in these markets.  Asia/Pacific will have the highest number of mobile video users at 541 million by 2016.  Both of these markets will benefit from low-cost smartphones being produced by contract manufacturers who are becoming the dominant production strategy of brand leaders globally. The following graphic shows the Mobile Video User Forecast by Region, Worldwide, 2008 – 2016.

  • By 2016, close to 60% of professionally developed mobile video content will be delivered via mobile-optimized websites that also have enhanced contextual search functionality included in the content management systems.
  • Mobile customer care, mobile commerce and mobile health will be the three primary industry drivers in the near-term of mobile video market, emerging as growth catalysts of this emerging market.
  • Cisco’s Visual Networking Index study reports that last year, mobile video accounted for 56% of all mobile data traffic.
  • 3G/4G connections are emerging as a powerful catalyst of mobile video growth.  Gartner is forecasting that the worldwide share of mobile video connections on 3G/4G will increase from 18% in 2011 to 43% in 2015.  In more established markets incouding North America and Western Europe, the percentage of 3G/4G connections is expected to be as high as 80% and 96% respectively.
  • Gartner projects that 70% of mobile video users will use only Wi-Fi to view mobile video, with the remainder of the market relying on a mix of cellular and Wi-Fi networks to gain access and also upload content.   The following figure shows the Mobile Video User Forecast by Network Type, Worldwide, 2008 – 2016.

Source: Market Trends: Worldwide, the State of Mobile Video, 2012. Gartner Group. Published: 10 February 2012 ID:G00223693 Author: Shalini Verma.   Link: http://www.gartner.com/id=1920315

Aug 15

Roundup of Big Data Forecasts and Market Estimates, 2012

From the best-known companies in enterprise software to start-ups, everyone is jumping on the big data bandwagon.

The potential of big data to bring insights and intelligence into enterprises is a strong motivator, where managers are constantly looking for the competitive edge to win in their chosen  markets.  With so much potential to provide enterprises with enhanced analytics, insights and intelligence, it is understandable why this area has such high expectations – and hype – associated with it.

Given the potential big data has to reorder an enterprise and make it more competitive and profitable, it’s understandable why there are so many forecasts and market analyses being done today.  The following is a roundup of the latest big data forecasts and market estimates recently published:

  • As of last month, Gartner had received 12,000 searches over the last twelve months for the term “big data” with the pace increasing.
  • In Hype Cycle for Big Data, 2012, Gartner states that Column-Store DBMS, Cloud Computing, In-Memory Database Management Systems will be the three most transformational technologies in the next five years.  Gartner goes on to predict that Complex Event Processing, Content Analytics, Context-Enriched Services, Hybrid Cloud Computing, Information Capabilities Framework and Telematics round out the technologies the research firm considers transformational.  The Hype Cycle for Big Data is shown below:

  • Predictive modeling is gaining momentum with property and casualty (P&C) companies who are using them to support claims analysis, CRM, risk management, pricing and actuarial workflows, quoting, and underwriting. Web-based quoting systems and pricing optimization strategies are benefiting from investments in predictive modeling as well.   The Priority Matrix for Big Data, 2012 is shown below:

  • Social content is the fastest growing category of new content in the enterprise and will eventually attain 20% market penetration.   Gartner defines social content as unstructured data created, edited and published on corporate blogs, communication and collaboration platforms, in addition to external platforms including Facebook, LinkedIn, Twitter, YouTube and a myriad of others.
  • Gartner reports that 45% as sales management teams identify sales analytics as a priority to help them understand sales performance, market conditions and opportunities.
  • Over 80% of Web Analytics solutions are delivered via Software-as-a-Service (SaaS).  Gartner goes on to estimate that over 90% of the total available market for Web Analytics are already using some form of tools and that Google reported 10 million registrations for Google Analytics alone.  Google also reports 200,000 active users of their free Analytics application.  Gartner also states that the majority of the customers for these systems use two or more Web analytics applications, and less than 50% use the advanced functions including data warehousing, advanced reporting and higher-end customer segmentation features.
  • In the report Market Trends: Big Data Opportunities in Vertical Industries, the following heat map by industry shows that from a volume of data perspective, Banking and Securities, Communications, Media and Services, Government, and Manufacturing and Natural Resources have the greatest potential opportunity for Big Data.

  • Big data: The next frontier for innovation, competition, and productivity is available for download from the McKinsey Global Institute for free.  This is 156 page document authored by McKinsey researchers is excellent.  While it was published last year (June, 2011), if you’re following big data, download a copy as much of the research is still relevant.  McKinsey includes extensive analysis of how big data can deliver value in a manufacturing value chains for example, which is shown below:

Apr 21

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What’s Hot in CRM Applications, 2012

Serving the sales force is a mantra and mindset that resonates through the best companies I’ve ever worked with and for.

That priority alone can help galvanize companies who are adrift in multiple, conflicting agendas, strategies and projects.  Uniting around that goal – serving sales and getting them what they need to excel – can turn around even the most downtrodden companies.  And size doesn’t matter, the intensity of focus and commitment to excel  do.

That’s why the latest report from Gartner’s Ed Thompson, What’s “Hot” in CRM Application 2012, published last Thursday resonates with me.  He’s talking about how sales strategies need to be propelled by rapid advances in mobile technology, social CRM, sales content and collaboration, and clienteling to serve the sales force more thoroughly than ever before.  His assessment of what’s hot in CRM is a great foundation for getting behind the mantra of serving the sales force and engraining it into a corporate culture while getting full value from the latest technologies.

Here are the key take-aways from the report:

  • Software-as-a-Service (SaaS) delivery of CRM applications represented 34% of worldwide CRM application spending in 2011.  More than 50% of all Sales Force Automation (SFA) spending is on the SaaS platform.  Gartner clients who are successfully running SaaS are now looking at how to get value from Platform-as-a-Service (PaaS) in the context of selling strategies.
  • CRM spending grew 13% in 2011, fueled analytical, operational and social CRM growth.  Operational CRM represents 80% of all CRM spending and grew 10% in 2011.
  • Analytical CRM, in which Gartner includes predictive analytics and market segmentation analysis, grew a solid 10% in 2011 and is having a very strong year with inquiry traffic.
  • Social CRM grew 30% in 2011 in revenue terms and is 7% of total CRM spending globally as of 2011.   90% of Social CRM spending is originating in Business-to-Consumer (B2C) organizations with the remaining occurring in B2B.
  • Gartner is projecting that CRM will be one of the top three search terms on Gartner.com throughout calendar 2012 based on the trends and volume of calls they are seeing today.
  • CEOs see CRM as their #1 technology-enabled investment in 2012 according the query calls through April, 2012.
  • CRM is ascending rapidly in the priorities of CIOs in 2012, moving from 18th place to eight place  in the latest Gartner analysis.
  • The following table of Highest CRM Application Priorities, 2012 show what’s trending within Sales, Customer Service, E-Commerce and Marketing inquiries Gartner is receiving from its clients.  Consider these as leading indicators of interest.  Over time these areas will need to solidify for forecasts to be completed.
  • Apple iPads are the great maverick buy of 2012 with thousands being purchased by Sales and Marketing management with the immediate requirement of IT integration to these devices.   IT departments are scrambling on the security issues and lack of polices on BYOD.  In enterprise software, iPads are proving to be highly effective as demo platforms for new SaaS-based applications.  They have become the new sales bag of the 21rst century.
  •  High Tech, Life Sciences and Insurance are the three industries with the greatest levels of iPad adoption as of April 2012.  Gartner is predicting that by the end of 2012, 80% of all sales representatives in the pharmaceutical industry will be using iPads for their daily sales tasks.
  • Social or community customer service is the hottest area of growth for post-sales service with high-tech, media, travel, telecommunications, retail and education-based clients dominating client inquiries.

Apr 2

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Gartner’s Hot CRM Applications for 2011 Show SaaS is Accelerating in the Enterprise


Gartner’s report, What’s ‘Hot’ in CRM Applications in 2011 shows clients are moving to the next stage of their strategies for using SaaS in the areas of customer service, marketing and sales.  They’re asking for more analyst time, discussing how to quickly deploy applications company-wide versus just in departments, and most important, how to measure the results. Cross-CRM applications including Business Process Management Systems (BPMS) and Master Data Management (MDM) are two of the more popular areas of inquiry Gartner is getting right now from infrastructure initiatives standpoint to unify CRM data and strategies as well.

Factors Driving Faster Adoption

Escaping high maintenance fees on their legacy CRM applications, facing chronic time shortages that make the traditional lengthy application deployment cycles unaffordable and impractical, and a mindset of measuring results from software spending are fueling greater SaaS adoption.

A local financial services firm is migrating to SaaS-based feedback management and analytics to capture customer satisfaction more effectively than their legacy CRM application could.  Chief Marketing Officers (CMOs) are also driving more technology adoption in the areas of social media for marketing, lead management, mobile marketing and Web analytics as they’re more accountable for delivering measurable results.  The new mindset in many companies about measuring results and continually improving strategies is a powerful catalyst of SaaS application adoption.  A summary table from the report is provided below (please click on it to expand for easier reading) along with key take-aways.

Key Take-Aways from the Report:

  • Software-as-a-service (SaaS) delivery represented approximately 26% of all CRM application spending in 2010. Spending on CRM applications grew by more than 8% in 2010.
  • In sales applications, almost 50% was delivered via SaaS, where it is now widely viewed as a mainstream model.
  • Operational CRM is the automation of processes such as campaign management or case management. It represents more than 70% of all CRM spending and grew at around 4% in 2010.
  • Analytical CRM, which includes predictive analytics and segmentation applications, grew 9% and according to Gartner represents nearly 25% of CRM spending.
  • Social CRM grew at over 50%, but still represents less than 5% of all CRM spending. According to the report, 90% of social CRM spending is by business-to-consumer (B2C) companies and approximately 85% of spending is initiated by companies based in North America.  Gartner expects the social CRM market to reach $1B in revenue by year-end 2012, up from $600M in 2010.
  • In terms of inquiry traffic, Social CRM is the hottest area of interest in customer service and marketing departments, followed by related areas like digital marketing and e-commerce. Gartner points out that Social CRM is used both within and outside an organization and is of equal importance to its clients today based on their inquiries.

Bottom line: This report shows more companies are confronting the need to change their customer service, marketing and selling strategies to be customer driven on an enterprise, not just department basis.  They are relying on SaaS based applications as  the catalyst of changing customer-driven strategies in companies.  CMOs and other senior managers are focused on measuring customer satisfaction, loyalty and profitability instead of just cost reductions as a result.

Source: What’s ‘Hot’ in CRM Applications in 2011 Ed Thompson, Michael Maoz, Kimberly Collins, Michael Dunne Publication Date: 17 March 2011 ID Number: G00211657


Sep 3

1 Comment

How Cloud Computing Is Revolutionizing Marketing

Bottom line: Cloud-based applications combined with the ability to measure program and strategy results are accelerating the efficiency and focus of marketing, bringing an entirely new level of intensity to this area.

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