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2017 Mainframe Trends that are Here to Stay for 2018: Docker, Blockchain, z14 and Beyond

Mainframes are still going strong as 2018 begins. Need proof? Keep reading for a look at the biggest innovations and developments to hit the mainframe world in the past year.  These five mainframe trends are expected to continue to impact the mainframe community in 2018.

Ask an everyman to tell you about mainframes, and you’ll likely hear that they went out of fashion decades ago.

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In fact, however, as these mainframe trends show, the ecosystem remains alive and well, and continues to see new ideas and trends on par with innovations that stretch across the IT world as a whole.

Docker Containers Come to Mainframes

Technically, it has been possible to run Docker on mainframes using a DIY approach since Docker debuted in 2013. Docker works on any Linux-based operating system, and Linux can run on mainframes.

In August 2017, however, Docker support for mainframes became official. Mainframes are now a bona fide part of the Docker ecosystem, benefitting from the agility and efficiency advantages that containers offer.

IBM z14 Announced

Mainframe hardware continues to evolve, as IBM showed when it announced the z14 system in July 2017.

Z14 is the latest, greatest version in IBM’s z family of mainframes. It expands upon the features of z13, which debuted in 2015.

Z14 offers a number of innovations in the security realm in particular.

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Z Systems Sales Decline

Notwithstanding the z14 announcement, sales of z Systems overall declined in 2017.

Is this a sign of the impending collapse of the mainframe industry and the irrelevance of mainframe skillsets? Hardly.

The z Systems sales decline was due in part to the fact that previous quarters saw high z Systems sales rates. Maintaining that pace indefinitely was not feasible.

Plus, even if mainframe sales were to decline, people with the skills to migrate mainframe workloads to other types of servers will be in high demand. Any company that chooses to divest itself from mainframes will need to embark on a complicated journey, which will only succeed with the help of seasoned mainframe admins.

Blockchain Comes to Mainframes

The world of blockchain technology evolved significantly in 2017, and mainframes have been a part of the picture. Over the course of the year, vendors like IBM have been increasingly active in promoting mainframes for hosting blockchain workloads.

Expect this trend to continue as blockchain technology — which includes but is certainly not limited to the Bitcoin cryptocurrency platform — continues to expand.

More Interest in Software-Defined Mainframe Environments

One of the biggest trends in the IT world over the past several years has been the shift toward software-defined everything.

Rather than running applications and storing data directly on hardware, organizations are increasingly using virtual environments that abstract the underlying hardware away. This provides more flexibility.

The software-defined everything trend is impacting mainframes, too, by enabling companies to move workloads from physical mainframes to software-defined mainframe environments — as Swisscom started doing in 2017, to cite one prominent example.

Such moves don’t mean the mainframes are going away. You still need to understand mainframe applications and architectures in order to administer these workloads. But the way that mainframe hardware and software interact is becoming more flexible.

Want to learn more about current mainframe trends? Check out our 2018 State of the Mainframe report to see key trends to watch for in 2018 as well as IT professionals’ top objectives for improving performance and saving money over the next 12 months.

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Stay in Touch using Dynamics 365

Are you marketing to all of your Dynamics Accounts, Contacts and Leads? If you are marketing with Dynamics 365, you know it’s very easy to identify which of your records are part of an active marketing campaign. But what about the new people that are added? How about the people that never responded to an old campaign, and are collecting dust? There has to be a better way to stay in touch. Check out this app for Dynamics 365 called Stay In Touch 365.

With a single click, Stay In Touch 365 will search for any active Accounts, Contacts and Leads who are NOT on an active marketing list. Three new marketing lists are created, giving you an easy way to reengage.

Stay In Touch 365

1. Visit Microsoft AppSource and download Stay In Touch 365 by clicking here.

2 .Open the Stay In Touch 365 Managed Solution and click on Configuration.

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3. Click on the Yellow Star that reads “Click Here To Begin.”

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4. You will be redirected to the Parent Business Unit. Click the box labeled “Click Me to Generate New Marketing Lists!”

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5. Your new marketing lists are now generated. Click on one of the links to open a list.

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5 Stay in Touch using Dynamics 365

Beringer Technology Group, a leading Microsoft Gold Certified Partner specializing in Microsoft Dynamics 365 and CRM for Distribution. We also provide expert Managed IT ServicesBackup and Disaster RecoveryCloud Based Computing and Unified Communication Systems.

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The Future Is Now: Machine Learning, IoT, VR, And Microservices Are Here To Stay

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company’s history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers’ viewing habits and determined that the show was likely to become a hit even before they purchased it.

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The wisdom behind Netflix’s sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data.

Machine learning’s talents aren’t limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox? That’s machine learning. Or how you can casually converse with anthropomorphic voices coming from your smartphone? Also machine learning.

But these examples pale when compared to machine learning’s potential for remaking business. Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are converging to power a renaissance in business intelligence.

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The untapped potential of machine learning

Here are some ways that machine learning could transform the core elements of the business ecosystem– and society:

Intelligent business processes. Many of today’s business processes are governed by rigid, software based rules. This rules-based approach is limited in its ability to tackle complex processes. Further, these processes often require employees to spend time on boring, highly repetitive work, such as checking invoices and travel expenses for accuracy or going through hundreds or thousands of résumés to fill a position. If we change the rules and let self-learning algorithms loose on the data, machine learning could reveal valuable new patterns and solutions that we never knew existed. Meanwhile, employees could be reassigned to more engaging and strategic tasks.

Intelligent infrastructure. Our economy depends on infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas. Machine learning has the potential to discover new signals in the data that could allow for continuous improvement of complex and fast-changing systems. That gives humans more time to apply their creativity (something that machines may never learn to duplicate) to new discoveries and innovation.

Digital assistants and bots. Recent advances in machine learning technology suggest a future in which robots, machines, and devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, adapt their behavior to different situations, and interact with humans much more closely. Our devices – already able to react to our voices – will become more interactive, continuously learning assistants to help us with our daily business routines, such as scheduling meetings, translating documents, or analyzing text and data.

Plan for change

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Although machine learning has already matured to the point where it should be a vital part of organizations’ strategic planning, several factors could limit its progress if leaders don’t plan carefully. These limitations include the quality of data, the abilities of human programmers, and cultural resistance to new ways of working with machines. However, the question is when, not if, today’s data analysis methods become quaint relics of earlier times. This is why organizations must begin experimenting with machine learning now and take the necessary steps to prepare for its widespread use over the coming years.

What is driving this inexorable march toward a world that was largely constrained to cheesy sci-fi novels just a few decades ago? Advances in artificial intelligence, of which machine learning is a subset, have a lot to do with it. AI is based on the idea that even if machines can’t (yet) duplicate the actual structures and thought patterns of the human brain itself, they can at least offer a rough approximation of important functions, such as learning, reasoning, and problem solving.

AI has been around since the 1950s, but it didn’t take off until the late 1990s, when Moore’s Law’s true exponential effects on computing power were realized, and researchers reined in their impulses to build a mechanized brain, focusing instead on using algorithms and machine learning to solve specific problems. Highly publicized machine-learning triumphs by IBM, such as Watson’s drubbing of human contestants on Jeopardy, captured the imagination of the public and business leaders.

Machine learning comes in several flavors, sometimes referred to as supervised learning  (the algorithm is trained using examples where the input data and the correct answers are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning  (the algorithm is rewarded or penalized for the actions it takes based on trial and error). In each case, the machine can learn from data – both structured (such as data in fields in a spreadsheet or database) and, increasingly, unstructured (such as e-mails or social media posts) – without explicitly being programmed to do so, absorbing new behaviors and functions over time.

Machines’ ability to learn puts them on an evolutionary path not unlike our own. They are gaining the ability to speak, listen, see, read, understand, and interact with ever-increasing sophistication. In just the last four years, the error rate in machine-learning–driven image recognition, for example, has fallen dramatically to near zero– practically to human performance levels.

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Machine learning as collaborator

As machine-learning–based skills approach those of human beings, it’s tempting to view their evolution as a zero-sum competition with humans that we are destined to lose.

However, there is another view that says that automation will lead more to collaboration rather than outright replacement. Consulting firm McKinsey & Company argues that while 49% of jobs will be subject to some degree of automation, just 5% will be fully replaced anytime soon. In most cases, says McKinsey, automation will take over specific tasks rather than entire jobs.

McKinsey’s argument is compelling, at least when it comes to knowledge work, because it mirrors the way computing has evolved within the organization. Early mainframes were programmed to perform specific tasks, such as tallying up an organization’s daily receipts. When PCs were first introduced in the 1980s, they were dismissed by businesses as expensive typewriters until packaged spreadsheet software came along, allowing organizations to automate some of their manual accounting tasks at the individual employee level. Knowledge work would never be the same.

Today, most organizations have enterprise software that uses rules-based processing to automate many tasks in departments such as finance and human resources and in warehouses. Yet while the task-based automation of enterprise software has brought tremendous productivity improvements, the software could not learn and improve with experience as humans can.

Until now.

Thanks to advances in computer processing power, memory, storage, and data tools, machine learning can be integrated into the enterprise-software systems that form the heart of most organizational IT infrastructures. This means that the software, using the mastery that it develops in individual tasks, will be able to contribute increasing levels of performance and productivity to the organization over time, rather than merely offering a one-time boost, as most software packages do today.

The strength of machine-learning integration

The improvements the software brings to organizations will not be limited to individual tasks. One of the biggest strengths of enterprise software is its integration– the ability of individual applications to share information and be part of process workflows both within individual departments and across the organization. Integration allows organizations to experiment with new combinations of ever-more intelligent and versatile machine-learning applications and, where possible, let the machines learn how to improve the ways they work with each other and with their human colleagues. Together, these applications form the intelligent enterprise.

Just as individual applications will contribute more productivity to the organization as their embedded machine-learning abilities become more sophisticated, so too will the combinations of those applications evolve to bring more intelligence and flexibility to departmental and organizational processes over time.

Here are some concrete examples of how machine learning is creating value in organizations today:

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Personalized customer service. Organizations can use machine-learning to improve customer service while lowering costs by combining natural-language processing, historical customer service data, and algorithms that continuously learn from interactions. Customers can ask the system questions and get accurate answers, lowering response times and allowing human customer service representatives to focus on higher-priority or more-complex interactions.

Financial-exception handling.
A machine-learning system can be trained to recognize payments that arrive without an order number and match them to invoices based on knowledge of customers’ order and payment histories. This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on more strategic tasks.

Improved hiring.
A machine-learning system can learn to pluck the most suitable job candidates from the thousands of résumés that organizations receive. It can also spot biased language in job descriptions that might discourage qualified people from applying and rescue other top candidates who fall through the cracks because they don’t fit with traditional hiring models.

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Algorithmic security.
By building models based on historical transactions, social network information, and other external sources of data, machine-learning algorithms can use pattern recognition to automatically spot anomalies. This identification helps detect and prevent fraudulent transactions in real time, even for previously unknown types of fraud. And this type of algorithmic security is applicable to a wide range of other situations, including computer hacking and cybersecurity.

Image-based procurement. Instead of having to log into a procurement system and search manually, employees can simply use a smartphone app to snap a picture of the item they’re looking for– a particular brand and type of laptop, for example– and the system will use machine learning to hunt through its database to find a match or the nearest equivalent. It will then send a message to the employee, who can launch the ordering process with a single click.

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Brand-exposure measurement. Brands spend billions on sponsorships, often without knowing exactly what they are getting for their money. A machine-learning application can sort through thousands of hours of sports video footage or track the action in real time, for example, to tell marketers how often their logo appears on screen, how large it is, how long it appears, and where it is located on the screen. Brands can then quantify their return on investment in the moment.

Contextual concierge.
Let’s say that your flight is suddenly delayed. A travel app on your smartphone can use context-sensitive machine learning to determine how this delay will affect your other travel plans and prompt you with rescheduling options.

Visual shelf management. Employees can take photos of shelves in a store aisle, kicking off a machine-learning process that automatically senses missing or improperly displayed items and prompts the store manager and the warehouse to fill the shelves correctly.

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Manufacturing quality control. By examining video of an assembly line, a machine-learning system can spot defects that a human might miss and automatically reroute the damaged parts or assemblies before products leave the factory.

Drone- and satellite-based inspection. A machine-learning system can sift through thousands of aerial images
of a pipeline, for example, and automatically spot areas that need maintenance or replacement.

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Machine learning needs a platform

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To be sure, organizations will gain tremendous benefits from individual machine-learning applications, even if they are never integrated into a larger whole. However, the benefits become much greater when these applications are on an integrated platform.

The business press has been discussing the power of platforms a lot lately, with iTunes being a well-known example. By creating a set of common software development tools that are available free to anyone who wants them, Apple has enabled developers to create thousands of applications for the iTunes App Store. Developers win because they can easily reach vast numbers of Apple device owners through iTunes. Apple wins because it takes a cut of the revenues for each app it makes available in the App Store.

Platforms are equally important to enterprises, not necessarily because of the profit motive (though some organizations are launching their own public, for-profit platforms similar to iTunes), but because having a platform gives them a base for quickly and cost-effectively combining different applications together, whether they are from different software vendors or are built in house.

No software vendor will ever be able to claim that it offers every machine-learning–enabled application that an organization needs out of the box. But vendors do offer platforms that organizations can use as bases for building out their entire machine-learning infrastructure.

The core of these machine-learning–enabled platforms is application programming interfaces (APIs). APIs are a kind of software version of those universal electric plug adapters that business travelers lug around with them so they can charge their electronic devices wherever they may be in the world. APIs allow software developers to plug into another software vendor’s applications without having to know anything about the complex code at the heart of those applications.

Another benefit of having a unified software platform is that organizations can use it to create a single point of access to data from across the organization. Data is the sole nutrient in a machine-learning diet. Algorithms need to binge on it constantly to lead a healthy and successful life. The larger and richer the data set, the more accurate the results. Having a single platform helps break down the data silos that exist across the organization so that organizations can make the most of machine-learning intelligence.

Organizations don’t need to go it alone

Inevitably, organizations will want to develop machine-learning–based applications that are not available in the marketplace. However, this does not mean that they need to create large internal machine-learning centers of expertise (although having some internal experts is recommended). Service providers can bring the expertise and perspective from within and across industries to help organizations focus on a small set of highly strategic processes that will benefit from machine learning.

The first step toward developing such applications is to determine where to apply machine learning. Organizations need to ensure that it erects barriers to entry against competitors or provides new ways of capturing and retaining customers by improving repurchase cycles or achieving new levels of win rates.

That means focusing investments on the machine-learning problems that will matter most to the industry’s basic competitive economics. Developing those engines will take considerable effort and time, so focusing the enterprise on those one or two projects that will really make a difference matters.

Here are five criteria to determine how to apply machine learning in a way that will create lasting differentiation.

1. The focus area as an appropriate candidate.

Not every facet of business will benefit from machine learning. The greatest potential is in automating high-volume tasks that have complex rules and large amounts of unstructured data.

Is your focus area big and complex enough for machine learning?

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2. A clearly formulated issue. Machine learning works best on specific, well-defined tasks where the desired output and relevant inputs can be clearly stated: given X, predict Y. While it isn’t a magic bullet that will automatically help organizations learn from all the data in their enterprise, machine learning can be valuable in discovering correlations in large amounts of data that humans could never have deduced for themselves.

3. A sufficient quantity of examples to learn from. Machine learning requires a lot of data to be accurate. There must be enough examples for the machine to learn meaningful approximations of the decisions you want to make. This is discovered through experimentation.

4. Meaningful differences within the dataset. If the data you are trying to learn from does not contain meaningful differences, then the algorithm will fail at its mission. Let’s say that you are trying to identify different types of buyers. If the training data does not contain significant differences in buyer characteristics, the algorithm cannot give you useful results.

5. A clear definition of success. Machine learning is always evaluated by measures of performance on a specific task. Typically, the computer will try to optimize whatever performance measure is defined. Clear evaluation criteria for the algorithm are therefore critical. You also need to be certain that the evaluation criteria are actually helpful for solving your business problem.

Key evaluation criteria for machine learning

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The human factor

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Ultimately, the technical barriers to machine-learning adoption will be easier to solve than the human ones. Predictions of steep job losses due to automation are stoking fear and uncertainty about how these self-learning systems will impact our roles and our livelihoods.

These fears must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems into collaborative human-machine environments.

Indeed, self-learning machines have the potential to become valuable collaborators with humans, augmenting their skills and helping employees become more productive in their current jobs while freeing them from boring, repetitive tasks.

Experts also predict that machine learning will create new roles inside the organization. There is already a shortage of data analysts and those capable of developing the intricate algorithms that machine learning requires. Other new roles will become evident as machine learning integrates deeper into the organization – and not all roles will require a degree in computer science or math. For example, creative thinking, strategy development, quality management, and people development and coaching will be crucial skills in an AI-driven organization, according to a survey by consulting firm Accenture2.

What’s next

When machine learning matures to the point that it can handle unstructured data (still an issue today), when organizations openly share data, and when algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines, and software. That will enable highly context-sensitive insight at both the organizational and individual levels. We can only guess at the level of automation that will result, but the impact on business – and society – will be significant.

Already, commercial machine-learning applications based on these technologies are available, and more are being created all the time. That is why business leaders should engage now with trusted providers that can help them evaluate data structures and availability, free up information from siloed systems, and identify the richest areas for machine-fueled insight and improvement. Together, they can address the cultural and change management challenges to take advantage of this new wave of business intelligence.

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Download the white paper Why Machine Learning and Why Now?

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Daniel Wellers is Digital Futures Lead, Thought Leadership Marketing, at SAP.

Jeff Woods is Vice President, Marketing Strategy and Head of Thought Leadership Marketing at SAP.

Dirk Jendroska is Head of Machine Learning Strategy and Operations, SAP Innovation Center Network, at SAP.

Christopher Koch is Director, Thought Leadership Marketing, at SAP.


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Tips on How to Stay Competitive If You Run a Small Business

CRM Blog Tips on How to Stay Competitive If You Run a Small Business

MS Dynamics 365 for Sales vs Salesforce

Sign up for a free Dynamics 365 for Sales course!

Learn more about Dynamics 365 for Sales features

According to Innovation, Science and Economic Development Canada, there are 1.1 million small companies (1-99 employees) in the country. However, 60% of small businesses fail within the first five years, so there’s a big concern for entrepreneurs on how to stay competitive. Here are three ways you can stay ahead of your competitors if you run a small business.

  1. Be passionate
  2. Use CRM
  3. Partner with other small businesses

Usually, small business owners started their business because of passion. Passion drives motivation, which makes small business owners the busiest people in the world. They don’t finish their work when it’s 5 o clock and rarely think about vacation. This drive and passion are factors that make them successful.

Convert your passion into hard work, and you’ll be one step ahead of the pack.

You may think that Excel is the perfect place to store your clients’ data. It may be true if you have only 20 of them. Once you reach 50 customers, it will be challenging to track them. You’ll need a tool, which will help you generate reports and identify insights.

Try Dynamics 365 for Sales for free to see if it fits your business’ needs. We bet you won’t go back to Excel after it!

If you’ve ever wanted to make your business more reputable and competitive, there’s one more thing that can help you with it: collaboration. Working collaboratively with small businesses allows for you to share your experience and knowledge as well as create new products or services that will bring additional value to your customers.

Find a local small business that shares the same values as you do, and come up with a creative project, that you can implement together. Your customers will love it.

Alina Hura, Digital Content Creator, WebSan Solutions Inc., a 2014 Ontario Business Achievement Award Winner for Service Excellence

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Nutshell CTO Andy Fowler: Good CRM Tools Stay Out of the Way

Andy Fowler is the CTO and cofounder of
Nutshell, a provider of CRM for small businesses.

In this exclusive interview, Fowler shares his thoughts on the importance of effective sales process management.

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Nutshell CTO Andy

CRM Buyer: What are some of the significant trends you’re seeing in the CRM industry now? How is the industry changing and evolving?

Andy Fowler: I’ve been in this industry almost seven years now, and the message that we took to the market seven years ago has evolved over time.

When we launched, mobile was just becoming central, and now mobile is fully expected from any buyer. People also expect intuitive design.

What is changing, and what we’re learning today, is that CRM was originally created for the managers and bosses to report on the pipeline, and it was sold as a value proposition to the manager.

What’s changing now is that CRM is becoming much more about supporting the rep — giving them the tools they need to improve their performance. It’s no longer just a tool for the boss to check in on the salespeople.

CRM Buyer: How can a CRM system motivate salespeople?

Fowler: If they see the intrinsic value of the CRM tool they’re using, that motivates them. We’ve seen trends come and go, but if the tool itself adds value, that motivation happens intrinsically. The ultimate tool is one that doesn’t require motivation to use. It’s sitting in the background and working automatically.

CRM Buyer: What is the key to making CRM user-friendly?

Fowler: The first piece is staying out of the way. A good sales rep knows how to develop a relationship and build rapport. If the tool is getting in the way, that’s a de-motivator. If the tool gets out of the way, that’s the best way to improve the experience.

The other way is delivering value. If people recognize a tool’s value, then they don’t need to be motivated to use it. There are a number of ways that can happen. Part of that is the middle name of CRM — “relationships.” Good sales reps know how to develop relationships and use tools to do that.

A tool can help augment their memory about the person on the other end of the phone or someone in the meeting they’re about to walk into.

CRM Buyer: What kind of information is needed for an effective CRM system?

Fowler: The information stored in CRM is variable, based on the business. As a small business owner, you understand what needs to be known. The most important piece is the time line of information, the back-and-forth of what they said to you last, and what you said to them.

Those conversations — having that transcript or a newsfeed style timeline of who said what to whom is key. That’s the best way to jog your memory and build that rapport with a lead.

CRM Buyer: What is sales process management, and why is it important?

Fowler: Sales process is one of the original problems that we set out to solve. For a lot of small businesses, the process of closing a sale is a long-running deal. Those kinds of sales processes sometimes involve sending demo equipment or proposals that take a long time to develop.

For many customers, they’re building a sale over a long time, and that’s a lot different than the e-commerce world. We think that CRM and managing a sales process are valuable for that long sales process for a high-ticket item.

It varies by company, but we think of it as different stages, from the first time you hear about a lead to the moment a deal is closed. It’s important to measure the velocity of how a sale moves from one stage of the process to another.

In a long-running sales process, information and communication needs to flow back and forth between a lead and a rep, and managing that information is part of managing the sales process, and making sure a ball isn’t dropped in that process.

All of those different transactions that happen along the way need to be monitored. Our tools allow you to track what sales are sitting at different stages.

CRM Buyer: What’s in the future for CRM? How will it continue to evolve?

Fowler: CRM will become increasingly connected to the tools that customers already use. If you’re using Outlook, for example, your CRM is going to become closer to that platform. Companies find and purchase tools, but these tools haven’t yet come together and become fully integrated. The line is going to get fuzzier between CRM and your own personal Outlook.

Account-based marketing is something that businesses have been practicing for a long time, but I think that’s going to continue to change how tools are developed. There won’t just be sales and marketing and support tools; the lines will blur and the pieces will become more integrated with each other.
end enn Nutshell CTO Andy Fowler: Good CRM Tools Stay Out of the Way

Vivian%20Wagner Nutshell CTO Andy Fowler: Good CRM Tools Stay Out of the WayVivian Wagner has been an ECT News Network reporter since 2008. Her main areas of focus are technology, business, CRM, e-commerce, privacy, security, arts, culture and diversity. She has extensive experience reporting on business and technology for a variety
of outlets, including The Atlantic, The Establishment and O, The Oprah Magazine. She holds a PhD in English with a specialty in modern American literature and culture. She received a first-place feature reporting award from the Ohio Society of Professional Journalists.
Email Vivian.

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Stay on the offensive with real-time analytics (VB Live)

analytics.shutterstock 495988867 1 780x468 Stay on the offensive with real time analytics (VB Live)

Don’t just stay on top of real-time data — use it to stay two steps ahead. But if you can’t separate the wheat from the chaff, you won’t just make bad decisions, you’ll look bad, too. Join this VB Live event for insight into finding and turning the right information into actionable insight.

Register here for free.

“If you look at real time analytics, there are two parts,” says Michael Healey, president of Yeoman Technology Group, which focuses on ensuring the technology investments of their clients deliver sales. “It’s defense and offense.”

And it doesn’t even require advanced analytics dashboards that monitor customer movements more closely than the KGB, he adds, but what Healey calls a part of classic real-time analytics — social monitoring.

He offers the example a Yeoman client whose brand-tracking strategy includes following conversations on Twitter. The company discovered that they had mistakenly been added to a boycott list being compiled by consumers — and they were able to catch the error and respond immediately before the rumors spread. They reached out to the protestors to ensure they understood that their company had not been a part of the actions being protested.

“That is some of the best defense ever,” Healey points out. “That was purely on social. There was nowhere else this data was floating around. They were going to get marched and protested against, and they were able to pick it up and knock it out before it ever got the legs that it might have.”

But real time data isn’t just about watching for trends and always reacting — it’s uncovering opportunities to leverage new strategies, and innovative ways to move beyond the information your dashboard is delivering.

“Don’t just think about your own real-time data — think about the real-time data that’s available to you as a marketer that you can use,” Healey says. “Perfect example: weather. What you don’t see all the time is people looking at the real-time weather and using that data as part of their strategies.”

Something as simple as real-time weather information can be the underpinning of an astonishingly effective offensive strategy, he adds. “A ten percent increase in online marketing spend when there’s a storm and nobody’s going out to the stores could have a dramatid impact on your ecommerce,” he says.

The basic formula, Healey says, is deceptively simple. “If there’s something going well in real time, you should try to augment it,” he says. “And if there’s something going poorly, you need to understand, is there an impact or is there not an impact?”

In other words, while real-time data lends itself to immediate pivots and instant reactions, which admittedly help you stay ahead of the brand conversation, overreacting is a real danger, and senior marketers need to tread very, very carefully.

“Real time data should always prompt you to look further,” Healey explains. “What’s going on, should I look deeper, should I do something? It shouldn’t be a kneejerk of ‘Oh my gosh, we’ve got to do something!’ You think of it almost as a canary in the coalmine. The canary’s not dead, but if it’s coughing, what should I look at?”

For more insight into separating the wheat from the chaff, plus using real time analytics to keep stakeholders on board every step of your campaigns, don’t miss this VB Live event!

Register here for free.

In this VB Live event you’ll:

  • Learn how to define the “truth” with metrics, and which version of the truth is meaningful to the business
  • Deal with the unpredictable nature of real time data blips
  • Frame business analytics into actionable solution-focused priorities for stakeholders


  • Stewart Rogers, Director of Marketing Technology, VentureBeat
  • Michael Healey, President, Yeoman Technology Group


  • Wendy Schuchart, Analyst, VentureBeat

This VB Live event is sponsored by Tableau.

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Big Data – VentureBeat

How Market Leader Uses Microsoft Dynamics CRM Online to Innovate, Create Global Visibility & Stay Ahead of Competition

It goes without saying that growth and a positive reputation are good things in business. However, when you organization is in this enviable position, you can bet your competitors won’t be far behind. The challenge then turns to staying out front and continuing to grow—without sacrificing the quality of your products and service.  You need to scrutinize your operations, looking for ways to improve and optimize.

Often, even the most successful organizations find room for improvement when it comes to the technology they’re using to manage critical processes. Just ask Bemis Associates what they were able to accomplish Microsoft Dynamics CRM.

Man in Running Gear 300x200 How Market Leader Uses Microsoft Dynamics CRM Online to Innovate, Create Global Visibility & Stay Ahead of CompetitionBemis Associates is a major manufacturer of next-generation adhesives that replaces sewing in the world’s most recognized performance, luxury and lifestyle brands. With operations around the globe and a reputation built on market innovation, Bemis Associates needs to ensure their increasingly complex business processes run smoothly while they continue to expand.

Upon examination, Bemis Associates realized that their challenges would be mostly in the areas of re-engineering their business processes and updating tools to provide increased visibility and communication. Their ultimate goal was to integrate marketing, sales and development aspects of their global organization to take advantage of opportunities and build loyal customer relationships.

Bemis did their research and after evaluating several CRM solutions, including Salesforce and OnContact, they chose Microsoft Dynamics CRM Online (now known as Microsoft Dynamics 365). Their decision was based on Dynamics’ compatibility and integration with other Microsoft products. This provides a familiar framework for team members around the world, and the online option is ideal for global accessibility and scalability in the face of rapid growth. Bemis was also impressed with the multi-language and multi-currency capability, which is necessary in an international operation.

Working with the CRM experts at AKA Enterprise Solutions, the team at Bemis was able to re-engineer and optimize their business processes and achieve their goals which included:

  • They now have detailed tracking to help team members visualize the connections between customer, factory and brand. This is especially important as a single factory might be processing orders for several customers and numerous brands.
  • Sales and development teams around the globe are now able to track test products for numerous brands. Knowing what is coming next allows the sales team to contact customers proactively to ensure samples are tested, results delivered, and initial orders entered into the opportunity pipeline.
  • Trade show leads are now prioritized and assigned automatically. Identification of potential customers and initial requests for custom samples is now handled in a portal interface.
  • ClickDimensions was integrated right into Dynamics CRM in order to streamline marketing
  • Having access to the same data, teams work together more efficiently and effectively and a fully automated sales pipeline assures that opportunities are not overlooked.
  • As they had intended, Microsoft Dynamics CRM Online increased transparency, collaboration and communication throughout the global organization.

Bemis Case Study Image 245x300 How Market Leader Uses Microsoft Dynamics CRM Online to Innovate, Create Global Visibility & Stay Ahead of CompetitionRead more about Bemis Associates and their positive experience with AKA Enterprises Solutions and Microsoft Dynamics CRM Online.  Contact our team at AKA Enterprise Solutions to discuss how your organization can optimize your business processes and communication with Microsoft Dynamics CRM Online.

By AKA Enterprises Solutions

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CRM Software Blog

Video: How SulAmérica Uses Analytics to Stay Ahead of the Game in Insurance

Fraud & Security Video: How SulAmérica Uses Analytics to Stay Ahead of the Game in Insurance

SulAmerica Analytics Video Image Video: How SulAmérica Uses Analytics to Stay Ahead of the Game in Insurance


Ever wonder how SulAmérica combats fraud, waste and abuse? In the video below, Umberto Reis, IT Senior Manager at SulAmérica, notes that of the multi-billion dollar Brazilian health insurance market, roughly 20% may be waste and abuse. Needless to say, it’s a challenging market in which to be profitable. To tackle this complex problem, the insurer uses a combination of FICO analytics and decision management solutions, including FICO® Blaze Advisor® Decision Rules Management System and FICO® Insurance Fraud Manager. This has allowed SulAmérica, the largest independent insurance group in Brazil, to become much more agile and drive continuous improvement.

For more information on this success story with SulAmérica, read our blog post: How SulAmerica Fights Insurance Fraud, Waste and Abuse.

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Adobe doubles down, but it can't just stay in Vegas

In Las Vegas, as I was waiting to board a flight to Chicago, I overheard this young woman who had been (I think) at the Adobe Digital Marketing Summit, saying something which was at least metaphorically indicative of the conference itself and at the same time, an indicator of the mindset that we are dealing with when it comes to the bigger questions of customer engagement and customer experience(s).

She said (semi-seriously): “I really thought that I was going to win the $ 1 million slot machine prize and if I did, what a great story that would have been.”