Orchestrating User Adoption from the Innovators to Laggards

Words like change, transformation, and automation will always generate a wide range of reactions throughout the rank and file of an organization, and our reaction to these words are good indicators as to which group we belong to on the adoption curve.

Innovators and early adopters tend to be motivated by change and late stage adopters and laggards tend to resist it.  Paradoxically, both groups have roles of equal importance in the user adoption process.

Knowing where users and people belong on the adoption curve and organizing them into user groups will enable a phased approach to managing the user adoption process. This approach to user adoption is preferred and will provide a more seamless diffusion of your innovation throughout each stage of the user adoption process. Furthermore, this will ensure that by the time it reaches the laggard group, the bugs will have already been worked out.

Successful innovations will reach a tipping point – which is the point that it is widely accepted and adopted by laggards. However, by the time that happens, count on the next innovation already being in play, and have a plan in place to repeat the cycle of innovation again.

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Documenting business processes, conducting frequent business process reviews, building and running knowledge management and collaboration portals, establishing a talent management program, and investing in the professional development of your people will have a direct impact to continued success of any future business transformation initiatives in the future.

As companies transform, it is important to retain a high level of diversity across the organization. Take into account the tacit knowledge that could be lost when choosing to acquire new talent.

Make the language of change pervasive throughout your organization and create a business culture that is comfortable with change and ready to adapt to it.

On a personal level, be prepared for change, because change will happen. Invest time in your own professional development. Always be learning and be willing to step out of the comfort zone.

For more on this topic, I suggest reading the following two books:

“The Tipping Point – How Little Things Can Make a Big difference” by Malcolm Gladwell.

“Diffusion of Innovations” by Everett Rogers

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Data Driven V. Predictive Marketing: BEWARE JETSON’S MARKETING!

The Big Willowby: Charlie Tarzian

My son came to me one day in early December and said:  ‘Hey, Dad, let’s get Mom one of those robotic vacuum cleaners.  You know, the ones you switch on and they vacuum your whole floor!’  He could not contain his enthusiasm – this was going to be great – no one would have to vacuum our floors ever again!!!

So we went to Amazon (of course) and two days later our round disc of a maid showed up via FEDEX.

Come Christmas Day, the robot fully charged, off we went to the kitchen to marvel at what was certain to be a life changing event.  We turned it on and put it down on the floor and the vacuum swung into action.  It crossed the floor, sensed it was coming to a wall, made a pivot, chugged in another direction…and got stuck on the slight incline by the fireplace…then stuck again on the floor mat by the stove…then got caught between a chair and a table and went into a break dance that would make R2D2 jealous.

I bring this up because a colleague sent me this little snippet from the website of a Predictive Marketing vendor:

“Predictive Marketing doesn’t need to be a services heavy engagement to get you up and running. With CompanyX (name of company withheld) and our push button integration, we can integrate with your existing systems and build your predictive model in under a day. – See more at: http://www.companyx.com/what-we-offer/#sthash.vBzkU18n.dpuf

There you have it: Jetson’s Marketing – just push our one little button and off you go:  great leads, great meetings, great website experiences – in fact all your marketing/sales problems solved in ‘under a day’.  All that is left to do is fire your staff, except for that one person in charge of pushing the button when you run out of leads, meetings and website visitors.

Look, I know what I don’t know, but I can tell you this: whatever you’re thinking the new generation of transformative marketing is – one thing it isn’t is automated bliss.  It takes a fair sized village to make things happen.  And herein lies the huge disconnect between data driven marketers and the shiny new object called Predictive Marketing.  Data driven marketers know that data can and should be utilized across the marketing/sales continuum – but it is more about data orchestration than anything else.  Therefore, one button, add water and stir does not take into consideration any of the cause and effect across all the communications and transactive channels that marketers rely on.

Marketing is services heavy (sorry, Company X) because at the nexus of MarTech, AdTech and Sales Enablement sits quite a bit of cause and effect.  And unless you aspire and build towards using predictive data to positively impact all channels aligning as one – then what you are predicting is a very small sliver of what could be.  In other words, if the connectivity and synapsis among outward facing channels are not orchestrated and optimized using predictive data and you are not feeling good that all channels are working in sync – then how can you predict a scaled outcome?  The predictions you are making will reflect a small percentage of the whole – and so instead of widening your funnel and increasing your opportunities along every step in a buying journey, you are narrowing that funnel based on a flawed assumption that you are predicting against a full boat of reliable data.

On the other hand, Data Driven Marketing  sets up to be based on solving for the cause and effect of what is less than optimized (can anyone say, broken?)  It attempts to determine (not predict) what works and doesn’t and then – as a village – cohesively knits together a response to results that can be repeatable but certainly is not a just add water, one button pushed result.

So – are we starting to see a difference:  Predictive Marketing – a push button approach to a complicated set of executional events and response, or, Data Driven Marketing – a human driven (sorry robots!) approach to the cause and effect of humans communicating to other humans about things that may or may not be important to the recipient (we always hope for the former)?

What do you think?  We would love to know.  Have any stories to share – we would love to hear from you.

by: Charlie Tarzian, Founder, The Big Willow

Google’s Artificial Intelligence Masters Atari Video Games

atari-brainDeep learning, one of the hottest topics today in artificial intelligence (AI), has taken another leap forward with DeepMind’s latest announcement.

Source: Google’s Artificial Intelligence Masters Atari Video Games

Think you’re good at classic arcade games such as Space Invaders, Breakout and Pong? Think again.

In a groundbreaking paper published yesterday in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level.

What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.

It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.

But by playing lots and lots of games many times over, the computer learned first how to play, and then how to play well.

A Machine That Learns From Scratch

This is the latest in a series of breakthroughs in deep learning, one of the hottest topics today in artificial intelligence (AI).

Actually, DeepMind isn’t the first such success at playing games. Twenty years ago a computer program known as TD-Gammon learned to play backgammon at a super-human level also using a neural network.

But TD-Gammon never did so well at similar games such as chess, Go or checkers.

In a few years time, though, you’re likely to see such deep learning in your Google search results. Early last year, inspired by results like these, Google bought DeepMind for a reported $400 million.

Many other technology companies are spending big in this space. Baidu, the “Chinese Google”, set up the Institute of Deep Learning and hired experts such as Stanford University professor Andrew Ng. Facebook has set up its Artificial Intelligence Research Lab which is led by another deep learning expert, Yann LeCun. And more recently Twitter acquired Madbits, another deep learning startup.

The Secret Sauce of Deep Learning

Geoffrey Hinton is one of the pioneers in this area, and is another recent Google hire. In an inspiring keynote talk at last month’s annual meeting of the Association for the Advancement of Artificial Intelligence, he outlined three main reasons for these recent breakthroughs.

First, lots of Central Processing Units (CPUs). These are not the sort of neural networks you can train at home. It takes thousands of CPUs to train the many layers of these networks. This requires some serious computing power.

In fact, a lot of progress is being made using the raw horse power of Graphics Processing Units (GPUs), the super fast chips that power graphics engines in the very same arcade games.

Second, lots of data. The deep neural network plays the arcade game millions of times.

Third, a couple of nifty tricks for speeding up the learning such as training a collection of networks rather than a single one. Think the wisdom of crowds.

What Will Deep Learning Be Good For?

Despite all the excitement about deep learning technologies, there are some limitations to what it can do.

Deep learning appears to be good for low-level tasks that we do without much thinking. Recognizing a cat in a picture, understanding some speech on the phone or playing an arcade game like an expert.

These are all tasks we have “compiled” down into our own marvelous neural networks.

Cutting through the hype, it’s much less clear if deep learning will be so good at high level reasoning. This includes proving difficult mathematical theorems, optimizing a complex supply chain or scheduling all the planes in an airline.

Where Next for Deep Learning?

Deep learning is sure to turn up in a browser or smartphone near you before too long. We will see products such as a super smart Siri that simplifies your life by predicting your next desire.

But I suspect there will eventually be a deep learning backlash in a few years time when we run into the limitations of this technology. Especially if more deep learning startups sell for hundreds of millions of dollars. It will be hard to meet the expectations that all these dollars entail.

Nevertheless, deep learning looks set to be another piece of the AI jigsaw. Putting these and other pieces together will see much of what we humans do replicated by computers.

If you want to hear more about the future of AI, I invite you to the Next Big Thing Summit in Melbourne on April 21, 2015. This is part of the two-day CONNECT conference taking place in the Victorian capital.

Along with AI experts such as Sebastian Thrun and Rodney Brooks, I will be trying to predict where all of this is taking us.

And if you’re feeling nostalgic and want to try your hand out at one of these games, go to Google Images and search for “atari breakout” (or follow this link). You’ll get a browser version of the Atari classic to play.

And once you’re an expert at Breakout, you might want to head to Atari’s arcade website.

How Big Data Will Shape Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield

How Big Data Will Shape the IT Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield

Big Data Trends | Strategies driving investments in data

imgresLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

Big Data Trends | Strategies driving investments in data

IDG Enterprise logoLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

The Best BPM Platforms for Digital Business

In the last quarter of 2015, Forrester Research identified Pegasystems, Appian, and IBM as leading the field of suppliers of business process management platforms for today’s digital business.

This post focuses on the value that Pegasystems delivers to today that is transforming how their clients engage with their customers throughout each stage of engagement.

photoSource: Pega.com The complexity of today’s business makes it hard to truly know a person across marketing, sales and service. There are too many customers, too many permutations of what they need and too many obstacles. Your customer base has grown, and so has your need for more sophisticated technology that not only understands prospect and customer demands but also helps accomplish your business objectives. Initially, your systems did what you needed: track customers and help you market and sell to them. But as your enterprise acquires companies and systems, technology becomes a barrier to how you engage with customers — across departments, time zones and geographies. Complexity has also brought inflexibility, making it hard for systems to adapt to changing needs, changing markets and changing regulations. It also makes it difficult to train employees because they’re battling systems, not servicing customers..Click here to read more.

The 8 white boarding videos that follow will help you visualize the unique value that Pega is delivering value today to their clients customer engagement management initiatives around the customer experience…

Build for Change: Directly Capture Objectives (DCO)

With Pega 7, you capture the policies and procedures that define your business – including rules, data models, UIs, integrations, reports, and organizational structures – in the model. Pega 7 automates the code generation. As the requirements change, a change in the model equates to an immediate system change.

Build for Change: Situational Layer Cake

Situational Layer Cake™ (SLC) architecture enables organizations to differentiate, specialize, and reuse their business applications. Pilot projects can grow into enterprise transformation programs overnight. Instant reuse dramatically accelerates the time to value for organizations seeking to be more agile in response to changing market and regulatory demands.

Build for Change: Case Lifecycle Management™

When a business person starts explaining their needs for an enterprise, they don’t generally dive into process details. And they certainly don’t describe “transactions.” They think in terms of the case and its stages. Rather than drawing an end-to-end process, Pega 7 provides tools for business people to define the major steps of how work gets done – essentially building the skeleton on which you hang the more detailed process. You establish a business view of the data before debating the details.

Build for Change: Mashup

Traditional service or API-based architectures result in hard-coding the UX logic into each channel independently. Process changes must therefore be made in multiple places, making it impossible to deliver a consistent customer experience. By embedding the Pega UX directly into the mobile or web channels, all of the intelligence and capability of Pega 7’s Case Management is brought directly to the customer touchpoint.

Build For Change: Omni-Channel UX

Pega’s Omni-Channel UX delivers an optimized and consistent user experience in every channel. Learn more at http://www.pega.com/platform

Build For Change: Event Strategy Manager

Pega’s Event Strategy Manager gives you the tools you need to turn streams of customer data into valuable business decisions and actions. Learn more at http://www.pega.com/platform

Build for Change: Pega Live Data

Pega Live Data allows users to quickly and easily define the data required to build the apps they need, and then access that data in their running application – all without having to worry about how and where the data is actually stored and accessed.

Build for Change: Next Best Action

The real value from Big Data and analytics comes when every customer conversation delivers exactly the right message, the right offer, and the right level of service to both give the customer a great experience and maximize the customer’s value to the organization. With Pega’s Next Best Action, business experts develop decision strategies that combine predictive analytics,
adaptive analytics, traditional business rules.

 

The Forrester Wave™: BPM Platforms For Digital Business, Q4 2015

Key Takeaways: Pegasystems, IBM, and Appian lead the pack

Forrester’s research uncovered a market in which Pegasystems, Appian, and IBM continue to lead the pack. Software AG, Oracle, Newgen Software, OpenText, Bizagi, K2, and DST Systems offer competitive options. Red Hat and TIBCO Software lag behind. The BPM Platforms Market Is Growing As EA Pros Accelerate Digital Transformation The BPM platforms market is growing because more EA professionals see BPM as a way to address emerging challenges for customer experience and digital business. This market growth is, in large part, because EA pros increasingly trust BPM platform providers to act as strategic partners, helping them transform how they use technology to win, serve, and retain customers in the digital age. Differentiators Include Rapid Development, UX Design, And Case Management As legacy BPM technology becomes outdated and less effective, improved delivery speed and process flexibility will dictate which providers will lead the pack. Vendors that can provide fast ramp-up, flexible mobile experiences, and dynamic case management position themselves successfully to deliver speed and business agility to their customers.

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Bogus Web Traffic Continues to Plague the Ad Business – WSJ

Marketers continue to waste billions–an estimated $7 billion, at least, this year–on buying online ads that people do not see, according to the Association of National Advertisers.

Source: Bogus Web Traffic Continues to Plague the Ad Business – WSJ

The Ecosystem CMOs Need To Build Now – CMO Nation

The marketing world has undergone a dramatic shift: digital now touches nearly every customer interaction. Marketing has become a technology-powered discipline, with the two areas so interwoven that chief marketing officers are projected to spend more on technology than chief information officers by 2017.

The rise of digital has led to the emergence and explosion of marketing technology (MarTech) applications and platforms. Marketers can now collect and analyze large and disparate volumes of data—and make their insights actionable with a degree of precision just years ago was only a dream. This gives more power to the CMO, who constantly aims to address the basic question of marketing: how to engage and acquire customers for the long term by making engagement and acquisition more attainable and measurable.

Source: The Ecosystem CMOs Need To Build Now – CMO Nation