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Posts tagged ‘Social Media Analytics’

Roundup of Cloud Computing & Enterprise Software Market Estimates and Forecasts, 2013

157989221When the CEO of a rust-belt manufacturer speaks of cloud computing as critical to his company’s business strategies for competing globally, it’s clear a fundamental shift is underway.

Nearly every manufacturing company I’ve spoken with in the last ninety days has a mobility roadmap and is also challenged to integrate existing ERP, pricing and fulfillment systems into next-generation selling platforms.

One of the most driven CEOs I’ve met in manufacturing implemented a cloud-based channel management, pricing, quoting and CRM system to manage direct sales and a large distributor network across several countries.  Manufacturers are bringing an entirely new level of pragmatism to cloud computing, quickly deflating its hype by pushing for results on the shop floor.

There’s also been an entirely new series of enterprise software and cloud computing forecasts and market estimates published.  I’ve summarized the key take-aways below:

  • Enterprise sales of ERP systems will grow to $32.9B in 2016, attaining a 6.7% CAGR in the forecast period of 2011 to 2016.   CRM is projected to be an $18.6B global market by 2016, attaining a CAGR of 9.1% from 2011 to 2016.   The fastest growing category of enterprise software will be Web Conferencing and Team, growing at a 12.4% CAGR through the forecast period.  The following graphic compares 2011 actual sales and the latest forecast for 2016 by enterprise software product category.  Source:  Gartner’s Forecast Analysis: Enterprise Application Software, Worldwide, 2011-2016, 4Q12 Update Published: 31 January 2013

Figure 1 enteprise spending

Figure 2

figure 3 cloud computing

 public cloud forecast

Forrester Wave

  • IDC is predicting Cloud Services and enablement spending will hit $60 billion, growing at 26% through the year and that over 80% of new apps will be distributed and deployed on cloud platforms.  Their predictions also are saying that 2.5% of legacy packaged enterprise apps will start migrating to clouds.  Source: Top 10 Predictions, IDC Predictions 2012: Competing for 2020 by Frank Gens. You can download a copy of the IDC Predictions here: http://cdn.idc.com/research/Predictions12/Main/downloads/IDCTOP10Predictions2012.pdf

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 foragingcontextual 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

Using Search Analytics To See Into Gartner’s $232B Big Data Forecast

By combining search analytics and the latest Gartner forecast on big data published last week, it’s possible to get a glimpse into this areas’ highest growth industry sectors.  Big data is consistently a leading search term on Gartner.com, which is the basis of the twelve months of data used for the analysis.

In addition, data from Gartner’s latest report, Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016 by Mark A. Beyer, John-David Lovelock, Dan Sommer, and Merv Adrian is also used.  These authors have done a great job of explaining how big data is rapidly emerging as a market force, not just a single market unto itself.  This distinction pervades their analysis and the following table showing Total IT Spending Driven by Big Data reflects the composite market approach.  Use cases from enterprise software spending, storage management, IT services, social media and search forecasts are the basis of the Enterprise Software Spending for Specified Sub-Markets Forecast.  Social Media Analytics are the basis of the Social Media Revenue Worldwide forecast.

Additional Take-Aways

  • Enterprise software spending for specified sub-markets will attain a 16.65% compound annual growth rate (CAGR) in revenue from 2011 to 2016.
  • Attaining a 96.77% CAGR from 2011 through 2016, Social Media Revenue Is one of the primary use case catalysts of this latest forecast.
  • Big Data IT Services Spending will attain a 10.20% CAGR from 2011 to 2016.
  • $29B will be spent on big data throughout 2012 by IT departments.  Of this figure, $5.5B will be for software sales and the balance for IT services.
  • Gartner is projecting a 45% per year average growth rate for social media, social network analysis and content analysis from 2011 to 2016.
  • Gartner projects a 20 times ratio of IT Services to Software in the short term, dropping as this market matures and more expertise is available.
  • By 2020, big data functionality will be part of the baseline of enterprise software, with enterprise vendors enhancing the value of their applications with it.
  • Organizations are already replacing early implementations of big data solutions – and Gartner is projecting this will continue through 2020.
  • By 2016 spending on Application Infrastructure and Middleware becomes one of the most dominant for big data in Enterprise Software-Specified Sub Markets.

  • $232B is projected to be sold in total across all categories in the forecast from 2011 to 2016. From $24.4B in 2011 to $43.7B in 2016, this presents a 12.42% CAGR in total market growth.

Search Analytics and Big Data

Big data is continually one of the top terms search on Gartner.com, and over the last twelve months, this trend has accelerated.  The following time series graph shows the weekly number of inquiries Gartner clients have made, with the red line being the logarithmic trend.

Banking (25%), Services (15%) and Manufacturing (15%) are the three most active industries in making inquiries about big data to Gartner over the last twelve months.  The majority of these are large organizations (63%) located in North America (59%) and Europe (19%).

What unifies all of these industries from a big data standpoint is how critical the stability of their customer relationships are to their business models.  Banks have become famous for bad service and according to the American Customer Satisfaction Index (ACSI) have shown anemic growth in customer satisfaction in the latest period measured, 2010 to 2011.  The potential for using big data to becoming more attuned to customer expectations and deliver more effective customer experiences in this and all services industries shows great upside.

Bottom line: Companies struggling with flat or dropping rankings on the ACSI need to consider big data strategies based on structured and unstructured customer data.  In adopting this strategy the potential exists to drastically improve customer satisfaction, loyalty, and ultimately profits.

Why CIOs Are Quickly Prioritizing Analytics, Cloud and Mobile

Customers are quickly reinventing how they choose to learn about new products, keep current on existing ones, and stay loyal to those brands they most value.  The best-run companies are all over this, orchestrating their IT strategies to be as responsive as possible.

The luxury of long technology evaluation cycles, introspective analysis of systems, and long deployment timeframes are giving way to rapid deployments and systems designed for accuracy and speed.

CIOs need to be just as strong at strategic planning and execution as they are at technology.  Many are quickly prioritizing analytics, cloud and mobile strategies to stay in step with their rapidly changing customer bases.  This is especially true for those companies with less than $1B in sales, as analytics, cloud computing and mobility can be combined to compete very effectively against their much bigger rivals.

What’s Driving CIOs – A Look At Technology Priorities

Gartner’s annual survey of CIOs includes 2,300 respondents located in 44 countries, competing in all major industries.  As of the last annual survey, the three-highest rated priorities for investment from 2012 to 2015 included Analytics and Business Intelligence (BI), Mobile Technologies and Cloud Computing.

Source: From the Gartner Report Market Insight: Technology Opens Up Opportunities in SMB Vertical Markets September 6, 2012 by Christine Arcaris, Jeffrey Roster

 

How Industries Prioritize Analytics, Cloud and Mobile  

When  these priorities are analyzed across eight key industries, patterns emerge showing how the  communications, media and services (CMS) and manufacturing industries have the highest immediate growth potential for mobility (Next 2 years).  In Big Data/BI, Financial Services is projected to be the fastest-developing industry and in Cloud computing, CMS and Government.

In analyzing this and related data, a profile of early adopter enterprises emerges.  These are companies who are based on knowledge-intensive business models, have created and excel at running virtual organization structures, rely on mobility to connect with and build relationships with customers, and have deep analytics expertise.  In short, their business models take the best of what mobility, Big Data/BI and cloud computing have to offer and align it to their strategic plans and programs.  The following figure, Vertical Industry Growth by Technology Over the Next Five Years, shows the prioritization and relative growth by industry.

Source: From the Gartner Report Market Insight: Technology Opens Up Opportunities in SMB Vertical Markets September 6, 2012 by Christine Arcaris, Jeffrey Roster

How Mobility Could Emerge As the Trojan Horse of Enterprise Software

Bring Your Own Device (BYOD), the rapid ascent of enterprise application stores, and the high expectations customers have of continual mobile app usability and performance improvements are just three of many factors driving mobility growth.

Just as significant is the success many mid-tier companies are having in competing with their larger, more globally known rivals using mobile-based Customer Relationship Management (CRM), warranty management, service and spare parts procurement strategies.  What smaller competitors lack in breadth they are more than making up for in speed and responsiveness.   Gartner’s IT Market Clock for Enterprise Mobility, 2012 captures how mobility is changing the nature of competition.

Source: IT Market Clock for Enterprise Mobility, 2012 Published: 10 September 2012 Analyst(s): Monica Basso

 

Bottom Line – By excelling at the orchestration of analytics, cloud and mobile, enterprises can differentiate where it matters most – by delivering an excellent customer experience.  Mobility can emerge as an enterprise Trojan Horse because it unleashes accuracy, precision and speed into customer-facing processes that larger, complacent competitors may have overlooked.

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.

SaaS-based Analytics and Business Intelligence Market Update, August 2011

Challenging, uncertain economic times accelerate sales cycles and lead to more closed deals for business intelligence software providers.  Companies get an urgency to reduce costs and risks, relying on the insights gained from these applications.

There’s an interesting dichotomy starting to emerge in how experts and analysts define just how these markets will mature however.  Both agree that economic uncertainty are growth catalysts yet they diverge on adoption rates, roadblocks, and which analytics and BI technology will dominate in the years ahead.

This week I read Balancing Custom And Packaged Apps In Your Application Portfolio Strategy by George Lawrie, Mike Gilpin and Adam Knoll from Forrester and the latest Hype Cycle of Business Intelligence, 2011 by a collection of Gartner authors led by Andreas Bitterer.  I’ve summarized the key points of each below.

Forrester Sees SaaS Applications Overtaking Custom Application Development

Forrester sees SaaS-based applications starting to replace in-house custom application development, gathering momentum through 2013.  Gartner, with their Hype Cycle for Business Intelligence, 2011 just released this week, shows BI platforms having greater near-term benefit than SaaS-based analytics and BI.  Custom application development projects are going to face continued pressure to keep up with business requirements that SaaS applications are proving able to handle more effectively and economically than ever before.

In-house development makes more sense for specific analytics and reporting requirements,  yet will continually be eroded by SaaS-based applications that can meet most requirements at a lower cost.  Forrester has in the past said SaaS-based adoption of analytics applications in general and predictive applications specifically would be very slow due to data integration challenges.  This study points to a potential shift in their mindset, as the data shows SaaS-based analytics beginning to replace custom in-house developed applications.

Here are the key take-aways from the report:

  • Analytics processes are supported 79% of the time with custom application development.  Procure-to-pay (33%) and record-to-report (33%) are the second-most supported.  Multiple responses were allowed in the survey.
  • When asked which process areas they are automating with SaaS, analytics (33%), record-to-report (18%), order-to-cash  (15%), and purchase-to-pay (12%) were the most common responses.  There was a small sample size on the Forrester report and the most startling insight was how quickly respondent companies plan to migrate from custom application development to SaaS-based analytics and BI.
  • Nearly 50% of the respondents to the Forrester survey have between five and 19 SaaS-based applications today with 18% expecting to have 35 or more by 2013.  In addition 63% of respondents expect to deploy between five and 34 SaaS-based applications by 2013, a significant shift in just two years.
  • 36% of survey respondents say their  SaaS applications run completely standalone.  Another 36% mention they use a combination of on-premises Master Data Management (MDM) and process integration tools.  Ironically only 3% are deploying their applications on cloud-based MDM or process integration-based platforms.

Gartner’s Hype Cycle for Business Intelligence, 2011

Unlike the hype cycle for cloud computing, this hype cycle has fewer technology categories (25), a narrative firmly grounded in business process and strategy, and more practical and pragmatic insights versus just theoretical.  At 50 pages it’s  quick read and while there are many excellent points made, I have summarized the key take-aways pertaining to the highest hype points and SaaS adoption below:

  • Mobile Business Intelligence (BI) is the latest entry to the Hype Cycle for Business Intelligence based on the massive hype around analyzing locational and application data.  The hype surrounding the Apple iPad Series, Google Android and other tablet and smartphone platforms has made this one of the most hyped areas of the last year according to the analysis.
  • Consumerization, Decision Support, analysis of non-traditional data and “Big Data” are the areas of the greatest innovation today.  The hype cycle points to search, mobile, visualization and data discovery being the catalyst of Consumerization.  Predictive analytics, which is on the Slope of Enlightenment on this latest hype cycle, is critical to decision support.  The non-traditional and “Big Data” area of innovation is further supported by content, text analytics, in-memory DBMSs and columnar DBMSs.
  • SaaS-based Business Intelligence is at the apex of the Peak of Inflated Expectations yet will continue to have low adoption rates.  Gartner believes that the  lack of trust in third parties managing confidential data, and the inertia and fear many companies have in moving to a new architecture are slowing adoption.  This is in contrast to the survey Forrester released this week showing analytics being one of the most popular SaaS-based applications planned by 2013 in their base of respondents.
  • Gartner sees SaaS-based Business Intelligence of the most value to midsize and smaller organizations who lack IT staff yet have very specific, targeted information needs.  Website analytics, social media monitoring, dashboards, predictive analytics and Excel as a BI front-end all apply.  Both Forrester and Gartner agree on this point and see this type of custom development going away quickly internally.
  • There is a massive amount of hype surrounding in-memory computing, particularly from SAP at its Sapphire conferences .  Gartner believes that SAP’s vision of in-memory computing exceeds  in-memory analytics to include analytical and transactional processing.  As a result, In-Memory Database Management Systems are at the Peak of Inflated Expectations.


Source: Hype Cycle for Business Intelligence, 2011, Published 12 August 2011 | ID:G00216086 By Andreas Bitterer.  Gartner, Inc.

What Both Agree On

Forrester’s survey shows SaaS eventually replacing custom application development while Gartner’s Hype Cycle for Business Intelligence shows the practical, pragmatic technologies including dashboards, predictive analytics combined with the more complex Business Activity Monitoring (BAM), Business Intelligence Platforms, and Data-Mining Workbenches delivering the most value.  Despite these differences, both agree on the following:

  • The overall market for BI, Analytics and Performance Management continues to grow at between 8 to 12% per year depending on the forecast used.  The following forecast is from the report  Market Trends: Business Intelligence, Worldwide, 2011-2014, 7 June 2011 | ID:G00213483 by Dan Sommer and James Richardson.
Source: Market Trends: Business Intelligence, Worldwide, 2011-2014, 7 June 2011 | ID:G00213483 by Dan Sommer and James Richardson
  • 2011 continues to see large, strategic deals for analytics and BI closing more rapidly than they have in the past.
  • SaaS-based analytics and BI continues to gain a greater share of spending in midsize and smaller companies.  Both also agree that the proliferation of smaller SaaS-based analytics and Bi vendors concentrating on a specific niche have successfully displaced in-house custom development of competitive applications.  Trust in the smaller vendor, their track record, customer references and financial viability are what are winning deals for SaaS-based analytics and BI software providers today.
  • The market transition from build to buy is now in full force as budgets become available again.  This is key assumption of both analyses and means that smaller, more niche-oriented SaaS-based analytics and BI vendors stand a chance to get new reference accounts and grow, despite a challenging economy.

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


Data Science Shows Potential To Redefine Cloud-based Analytics

The emerging field of data science is a fascinating one that has major implications on the potential of cloud-based analytics, CRM, search, supply chain management and logistics.

Instead of relying purely on latent semantic indexing or the Google PageRank algorithm to define relevance of a search, data science techniques analyze content and its context to determine relevance.  Google today looks at the content of a page; data science considers its surrounding data and relevance.

Earlier this month TechCrunch published the blog post Marissa Mayer’s Next Big Thing: “Contextual Discovery” — Google Results Without Search.  The techniques of contextual discovery Google is experimenting with rely on a very rapid aggregation and transforming of data, which are part of the methodologies of data science.   When Google moves fully into contextual discovery the potential exists for cloud-based analytics, CRM, search, supply chain management and logistics to be completely revolutionized by solving the big data problems associated with each of these areas.

In CRM, this would mean finally being able to access external and internal content (including the massive amount of data on social networks), aggregate the data, and transform it into meaningful analysis.  The vision of social CRM would be realized once data science serves as the catalyst of contextual search or as Google calls it, contextual discovery.

Exploring Data Science

Two of the best blog posts are both from O’Reilly Radar on the emerging topic of data science.  What is data science? By Mike Loukides and Six months after “What is data science?” by Mac Slocum O’Reilly Radar are worth reading and giving some serious thought to.  O’Reilly also has also created a free report titled What is Data Science, which can be downloaded here.

Authors Mike Loukides and Mac Slocum set the foundation for how transformational data science has the potential of being by concentrating on the nascent area of data products.  A data product is the result of accessing, aggregating and transforming content regardless of its location – and capturing data on its attributes – not just the data itself. Both authors point to reference systems and guided reference engines on e-commerce sites as just the beginning.  Yet after reading their assessments and listening to Roger Magoulas, O’Reilly’s Director of Research, interviewed about data science below there are many more potential uses of this evolving area.

Potential Impact of Data Science on Analytics

The blog posts by Mike Loukides and Mac Slocum go into detail explaining how each area of data science is in varying levels of maturity.  After reading these over and considering the big data problems in cloud-based analytics, CRM, search, supply chain management and logistics, the following methodology starts to make sense:

Access – For data science to realize its full potential there needs to be a technology layer that provides for real-time access to structured and unstructured content both within and outside an enterprise.  More than a traditional Enterprise Application Integration (EAI) layer the technologies driving data access need to selectively pull all available content from every unstructured and structured data source available.  Mike Loukides mentions Google Goggles and how MapReduce has made this application possible.  Hadoop as a means to create greater access across federated content has much potential in this phase as well.

Aggregate – Called data conditioning by Mike Loukides, the aggregation phase is where contextual discovery happens.  This could be accomplished through contextual search filters, taxonomies defined by specific alerts, or the use of the MapReduce and Hadoop query and relevance tools in use today.

Transform – Where Hadoop could be used for driving data analysis and as Mike Loukides calls this level of analysis, data jiujitsu.   Examples are mentioned by both Mike Loukides and Mac Slocum including the Hadoop Online Prototype (HOP), which does real-time stream processing and several others.  The impact of the access, aggregate and transform methodology on visualization is available at Flowing Data, one of the best sites on the Web for seeing how MapReduce, Hadoop and other data science-related techniques are taking on massive amounts of data and delivering insights.

Conclusion

Solving the big data problems of social media monitoring, sentiment analysis, forming a scalable platform for social CRM, integrating CRM, supply chain management and logistics data to demand management – and tying all of these areas to financial performance – is potentially achievable with data science.  Deployed as a cloud-based platform opens up even greater potential for getting the most use of social networks, free data sources, and third-party databases than is possible today.

Be sure to check out the video below of Roger Magoulas, O’Reilly’s Director of Research, where he was interviewed about data science.

Article links:

What is data science? By Mike Loukides  O’Reilly Radar
Six months after “What is data science?”  by Mac Slocum O’Reilly Radar

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