Tag Archives: Impact

Expert Interview (Part 1): Wikibon’s James Kobielus Discusses the Explosive Impact of Machine Learning

It’s hard to mention the topics of automation, artificial intelligence or machine learning without various parties speculating that technology will soon throw everybody out of their jobs. But James Kobielus (@jameskobielus) sees the whole mass unemployment scenario as overblown.

The Future of AI: Kobielus Sees Progress Over Fear

Sure, AI is automating a lot of knowledge-based and not-so-knowledge-based functions right now. It is causing dislocations in our work and in our world. But the way Kobielus looks at it, AI is not only automating human processes, it’s augmenting human capabilities.

“We make better decisions, we can be more productive … We’re empowering human beings to do far more with less time,” he says. “If fewer people are needed for things we took for granted, that trend is going to continue.”

It’s anybody’s guess how the world will look in the future, Kobielus says. But he doesn’t believe in the nightmare scenarios in which AI puts everyone out of a job. Why? Basic economics.

The industries that are deploying AI won’t have the ability to get customers if everyone is out of a job.

“There needs to be buying power in order to power any economy, otherwise the AI gravy train will stop,” he says.

blog kobielus quote2 Expert Interview (Part 1): Wikibon’s James Kobielus Discusses the Explosive Impact of Machine Learning

Kobielus is the lead analyst with Wikibon, which offers market research, webinars and consulting to clients looking for guidance on technology. His career in IT spans more than three decades and three-quarters of it has been in analyst roles for different firms. Before going to Wikibon, he spent five years at IBM as a data science evangelist in a thought leadership marketing position espousing all things Big Data and data science.

He talks regularly on issues surrounding Big Data, artificial intelligence, machine learning and deep learning.

How Machine Learning is Impacting Industry Today

Machine learning is a term that’s been around for a while now, Kobielus says. At its core, it’s simply using algorithms and analytics to find patterns in data that you wouldn’t have been able to find otherwise. Regression models and vector machines are examples of more established forms of machine learning. Today, newer crops of algorithms are lumped under what are called neural networks or recurrent neural networks.

“That’s what people think of as machine learning – it’s at the heart of industry now,” Kobielus says.

Brands are using these neural network tools for face and voice recognition, natural language processing and speech recognition.

Applied to text-based datasets, machine learning is often used to identify concepts and entities so that they can be distilled algorithmically to determine people’s intentions or sentiments.

blog banner 2018 Big Data Trends eBook Expert Interview (Part 1): Wikibon’s James Kobielus Discusses the Explosive Impact of Machine Learning

“More and more of what we see in the machine learning space is neural networks that are deeper,” Kobielus says. “[They’re] not just identifying a face, but identifying a specific face and identifying the mood and context of situation.”

They’re operating at much higher levels of sophistication.

And rather than just being used in a mainframe, more often these algorithms are embedded in chips that are being put into phones, smart cars and other “smart” technologies.

Consumers are using these technologies daily when they unlock their phones using facial recognition, ask questions to tools like Alexa or automatically tag their friends on Facebook photos.

More and more industries are embracing deep learning – machine learning that is able to process media objects like audio and video in real time, offering automated transcription, speech to text, facial recognition, for instance. Or, the ability to infer the intent of a user from their gesture or their words.

Beyond just translating or offering automated transcriptions, machine learning provides a real-time map of all the people and places being mentioned and shares how they relate to each other.

Looking at the internet of things market, anybody in the consumer space that wants to build a smart product is embedding deep learning capabilities right now.

Top Examples of Machine Learning: Self-Driving Cars and Translations

Kobielus points to self-driving vehicles as a prime example of how machine learning is being used.

“They would be nothing if it weren’t for machine learning – that’s their brains.”

Self-driving vehicles process a huge variety of input including images, sonar, proximity, and speed as well as the behavior of the people inside– inferring their intent, where they want to go, what alternative routes might be acceptable based on voice, gestures, their history of past travel and more.

Kobielus is also excited about advances in translation services made possible by machine learning.

“Amazon Translate, human translation between human languages in real-time, is becoming scary accurate, almost more accurate than human translation,” Kobielus says.

In the not-too-distant future, he predicts that people will be able to just wear an earpiece that will translate a foreign language in real-time so they will be able to understand what people are saying around them enough to at least get by, if not more.

“The perfect storm of technical advances are coming together to make it available to everybody at a low cost,” he says.

Learn more about the top Big Data trends for 2018 in Syncsort’s eBook based on their annual Big Data survey.

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How the Holidays Can Impact Your FICO® Score

It’s no secret that the holidays mean more spending. But that can become a problem when spending leads to significantly higher credit card balances, missed payments, and a lower credit score.

If this happens to you during the holidays, you aren’t alone. Average revolving debt – the type of debt incurred by using credit cards – was 4.5% higher in January 2017 than in October 2016. Young consumers (age 18-34) exhibited the largest percent increase (5.0%) of any age group, driven by the fact that their average revolving debt levels are the lowest of all age groups.

Even more notable, 33% of Americans increased their total credit card debt by 10% or more between October 2016 and January 2017.

Not only can increased credit card debt feel stressful, but credit card debt can have an impact on FICO® Scores. Credit card debt increases your credit utilization ratio, which impacts your credit score. Credit utilization is a key component of the “amounts owed” category of the FICO® Score, which determines roughly 30% of a consumer’s FICO® Score. “Amounts owed” is the second most important category in the FICO score, behind only whether you make your payments on time, which makes up some 35% of the FICO® Score calculation.

So the amount of debt on revolving accounts is an important driver of your FICO® Score. Racking up large revolving balances may mean that you are overextended, and more likely to miss payments.  Similarly, people with lower debt and credit utilization levels tend to be less likely to miss payments, and as such, are rewarded by the score for their more careful use of credit.

To quantify the impact that a holiday ramp up in revolving debt can have on the FICO score, we analyzed FICO score shifts of those consumers with an increase of 10% or more in revolving debt amount between October and January of the following year.  To analyze the stability of this trend over time, we examined the FICO score impacts of holiday shopping over the last 3 winters (see Figure 1).

  • Of this population, almost twice as many consumers see their FICO® Score decrease vs. increase between October and January
  • 57% of these consumers experience a FICO® Score decrease between October and January, while only 33% see their FICO® Score increase
  • 1 in 5 of these consumers experience FICO® Score decreases of 20 points or more
    • This is higher than the total population, where only 1 in 9 consumers have FICO® Score decreases of 20 points or more

Figure 1

Because of the additional spending during this time, we also saw a small increase (0.35%) in the percent of consumers with one or more missed payments in the last three months. In October 2016, 9.27% of consumers had a recent delinquency, compared to 9.62% in January 2017. Young consumers had the largest increase (0.53%) in recent payment delinquencies of any age group (from 12.45% in October 2016 to 12.99% in January 2017), often because they do not have as much experience with how their credit score works.

For those consumers with recent delinquencies as of January, almost half experience FICO® Score decreases of 20 points or more between October and January (Figure 2). Roughly two out of three of these consumers see their FICO® Score decrease. This is in contrast to the total population, where only 36% of consumers have a FICO® Score decrease.

Figure 2

The score impact of a recent missed payment is greater for high-scoring consumers.  This is intuitive, as higher-scoring consumers have further to fall should payment blemishes suddenly begin appearing on their previously spotless credit record. Roughly 9 in 10 of consumers scoring 750 or higher in October 2016 have a score decrease of 20 or more points when they had a recent delinquency as of January 2017, while this is true for only 3 of 10 consumers for those scoring less than 600. So it is especially important for high-scoring individuals to ensure that they make their payments on time during the holidays.

While you may feel the need to spend more for the sake of holiday cheer, it’s critical to keep your credit score healthy this holiday season. Resist opening store credit cards or consider leaving your credit card at home – opt to shop with cash to keep from overspending. To avoid the post-holiday blues for your credit score, monitor your holiday spending and make sure to pay your bills on time. These two factors are the most important components of the FICO® Score calculation. If you successfully rein in your spending during the holidays, you may not need to make any resolutions around getting your FICO score back in shape in the New Year!

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The impact of self-learning software now and in the foreseeable future

 The impact of self learning software now and in the foreseeable future

We’ve spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived.

Now, when major tech companies give their annual keynotes, they can’t help but pepper the narrative with phrases like “machine learning.” What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw?

The future is here — and it’s equal parts excitingand terrifying. Now that our world is populated with computer programs that can teach themselves new tricks, how will things change? What’s still worth worrying about?

Self-learning software for business and personal use

With 2018 upon us, the worlds of both business and personal software are ramping up to make the next few years something of an artificial intelligence arms race. On the consumer side of things, machine learning and AI make our lives easier in small ways. Case in point: many of us now have a smart speaker like an Amazon Echo or Google Home sitting on our countertops.

While these kinds of AI applications are helpful and entertaining, their self-learning capabilities are limited, to say the least.

In the world of business, there’s more immediate potential for self-learning software.

“We are drowning in information,” says Vita Vasylyeva of Artsyl Technologies. “The biggest bottlenecks in any business process involve the handling of documents and manual input of data from those documents. At the heart of those bottlenecks is the transformation of unstructured content into structured data.”

Nevertheless, both the business and consumer worlds have distinct needs and roles to play, and I fully expect machine learning in both realms to grow more sophisticated and capable.

Briefly, here are three very different applications for self-learning software:

1. Smartphones: Machine learning is turning smartphones into veritable supercomputers. From learning what your face looks like by poring through your photos to delivering more timely and relevant app and location suggestions, our devices are learning who we are and what we want.

More critically, machine learning is also training modern smartphones to become better at identifying and quarantining known threat vectors such as malware and viruses. It’s not all about fun and games.

2. Medicine: Diagnostic medicine is a difficult branch of science. Some types of cancer scans currently require as many as four specialists to study and come to a consensus on treatment.

With machine learning, physicians can practice this type of diagnostic medicine much faster, more accurately, and with fewer people-hours required.

3. Marketing and business management: The marketing applications of self-learning software perfectly marry the promises and the privacy worries of machine learning.

Some industry experts predict that within 10 years, even the humblest small businesses will engage in machine learning to improve their reach.

Another critical application is the promise of easier bookkeeping and organization. Newer document- and data-capture software suites take cues from the user to automatically identify and categorize types of documents and transactions, and in the process, significantly cut down on the labor and expense of staying organized and profitable.

Naturally, this is an abridged version of the emerging opportunities machine learning represents. Nearly every industry will likely come to rely on self-learning software in the future to make modern life more efficient.

The opportunities

So why the controversy? Why are folks like Elon Musk and Stephen Hawking doing their best Chicken Little impressions about AI and machine learning? Whether or not you subscribe to their possible doomsday scenarios, it’s fairly clear by now that the vast opportunity SLS offers is counterbalanced by some legitimate concerns.

For example, a major opportunity available now is the use of smarter machines to allocate resources more efficiently. For a smaller-scale look at what this means, consider the benefits of using self-learning software to make micro-variation adjustments to the way server farms consume electricity.

The result, according to researchers, is something almost eerily alive: a kind of silicon brain switching parts of itself on and off as needed to conserve basic resources. It’s the sort of thing that could help us come to terms with global warming and the sixth mass extinction in progress.

Removing the error-prone human element from the operation of automobiles is another huge opportunity made possible by machine learning. According to firsthand reports, the uncanniness of flying down a highway at 65 mph while an algorithm does the piloting wears off after a short while. Self-driving cars, in other words, are the future.

Alongside improved battery technology, we stand to benefit by dramatically slashing or eliminating our use of fossil fuels by making our commutes and traffic jams more efficient, and nonexistent, respectively. Cars of the future will be able to communicate with each other and pool data on things like road construction, obstructions, weather, and emerging incidents that could affect the drive.

The risks

Every one of the features above represents some type of privacy concern. Siri, Bixby, Cortana, and Google can’t perform their magic tricks without gathering data about their users.

Every tech giant that oversees these virtually intelligent personal assistants seems to take a different tack on user privacy. Your smartphone will send various types of personal data to distant server farms for processing each time you make an inquiry. What that company does with the information from there — and who they sell it to — is the stuff of terms of service fine print.

Beyond privacy, the other very real concerns about self-learning software are all about the consequences of removing human judgment — and in some cases emotion — from critically human experiences and interactions.

Wells Fargo and other major financial institutions wish to use artificial intelligence to dispassionately come to conclusions about their customers’ creditworthiness, for example — an idea that will either eliminate or greatly worsen preexisting cultural biases.

As far as self-driving cars go, a major learning curve is making ourselves comfortable with a world where our cars can solve the grisly “trolley problem” to our satisfaction. Are we comfortable writing software for a car that instructs the vehicle to end a human life to save five others?

Humans have historically had to bear the weight of that moral calculus — or didn’t have time to perform it at all in the vital split-seconds before a car crash. For better and worse, it seems machines can now do some of our ethical moralizings for us.

As you can see, determining the direction of where AI innovation will take us is a complex issue — but one that’s chock-full of potential.

The trick is getting scientists, philosophers, business leaders, citizens, and politicians on the same page.

Kayla Matthews is Senior Writer for MakeUseOf. Her work has also appeared on VICE, The Next Web, The Week, and TechnoBuffalo.

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Oracle + NetSuite Social Impact Launches Buildathon 4Good in Manila

Posted by Debra Askanase, Project Manager, Oracle NetSuite Social Impact

For Hazel May Pajotagana, CFO of HiGi Energy, a social enterprise focused on clean energy and environmentally friendly products, balancing the demands of managing finances for a busy nonprofit organization leaves little time to experiment and optimize HiGi Energy’s ERP system. That’s where the Oracle + NetSuite Social Impact Buildathon 4Good comes in.

The one-day event in Manila, Philippines provided real-time NetSuite customizations for seven local Non-Governmental Organizations (NGOs) and one social enterprise, all Social Impact donation recipients. They included the Philippine Foundation for Science and Technology, UP System Information Technology Foundation, HiGi Consultancy Corporation (HiGi Energy), Bayan Academy for Social Entrepreneurship and Human Resource Development, Ramon Magsaysay Award Foundation, Generation Hope, ChildHope Asia Philippines, and Save the Children Philippines.

The Buildathon is a new effort for the Social Impact group. Unlike the Hackathon 4Good held at SuiteWorld, the Buildathon 4Good focuses on helping multiple nonprofits solve the same challenge, and are deployed the same day. Organizations were invited to participate in either of two challenges: track the donor life cycle, or develop customized dashboards.

Suite Pro Bono 

Through the Suite Pro Bono program, 32 local NetSuite employees worked collaboratively with seven nonprofit organizations and one social enterprise to craft customized solutions to the challenges. Employees represented the Product, Support, and Professional Services departments. Four highly skilled employees served as team advisors who checked solution feasibility. Prizes were awarded to the teams that were the first to deploy, and to the overall challenge winners.

Judges included Hazel del Rosario-Lee (Managing Director of Oracle NetSuite Philippines), Anton Ancheta (Oracle NetSuite Consulting Senior Practice Director), Chester Que (CEO, Achieve Without Borders) and VJ Africa (CEO, Tech4Good).

Buildathon Oracle + NetSuite Social Impact Launches Buildathon 4Good in Manila

Hazel del Rosario Lee, Managing Director of Oracle NetSuite Philippines addressing the Buildathon 4 Good

For the Social Impact organizations, this was a rare chance to receive pro bono consulting. Every nonprofit works with leads, whether they are potential members, donors, or participants. Developing a customization to track the lead-to-donor lifecycle is a big step to increasing efficiencies.

Results 

The organizations left with at least one unique customization enabling them to better utilize NetSuite, and work more effectively. Many teams deployed solutions above and beyond the challenge requirements, adding additional customizations.

Hazel May Pajotagana, of HiGi Energy, learned a lot from the dashboard challenge.

“With the help of the team, I learned a lot about NetSuite…and NetSuite is helping us master the software.”

Bayan Academy for Social Entrepreneurship and Human Resource Development was awarded the overall dashboard winner, and ChildHope Asia Philippines won the lead-to-donor life cycle challenge. Philippines Director, Hazel del Rosario-Lee, in her concluding remarks, praised the team efforts and the Buildathon 4Good: “Thank you for maximizing NetSuite functionality.” The Social Impact group looks forward to many more successful Buildathon 4 Good events in the future.

Buildathon2 Oracle + NetSuite Social Impact Launches Buildathon 4Good in Manila

Learn more about what the Hackathon 4Good did for Found Animals Foundation and the winners of one recent Hackathon 4Good.

For more information on Oracle + NetSuite Social Impact, read our data sheet here and check out our website. For questions about the social donation program, contact socialimpact@netsuite.com.

Posted on Thu, December 14, 2017
by NetSuite filed under

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NCAP Medical Collection Removals are Rare and Have No Material Impact to FICO® Scores

Medical Collections Banner Image NCAP Medical Collection Removals are Rare and Have No Material Impact to FICO® Scores

The National Consumer Assistance Plan (NCAP) is a comprehensive series of initiatives intended to evaluate the accuracy of credit reports, the process of dealing with credit information, and consumer transparency. In a previous post, we showed that July 2017 NCAP public record removals (civil judgments and some tax liens) had no material impact to FICO® Scores. In mid-September 2017, the three consumer reporting agencies (CRAs) are also scheduled to remove the following from credit reports:

  • Medical collections less than 180 days old
  • Medical collections that are ‘paid by insurance’

FICO recently conducted research on a representative sample of millions of US consumers to assess the impact of the NCAP-driven removal of these 3rd party medical collection agency accounts on the FICO® Score.  Our results showed that NCAP-related medical collection removals have no material impact on the aggregate population to the FICO® Score’s predictive performance, odds-to-score relationship, or score distribution.

The removal of medical collections less than 180 days old is based on the date of first delinquency. Since most medical collections aren’t reported to the CRAs until more than 180 days after the first delinquency, we found that only 0.1% of the total FICO scorable population (roughly 200,000 consumers out of ~200 million) has a medical collection less than 180 days old. Medical collections that are identified in the credit file as being ‘paid by insurance’ are even less common.

Of the impacted population, roughly 3 in 4 saw score changes of less than 20 points. Most consumers with these medical collections have other derogatory information on their credit files, resulting in minimal impact to their FICO® Score once these collections are removed.

In 2014, FICO® Score 9 introduced a more sophisticated way of assessing collections, by ignoring all paid collections and differentiating unpaid medical collections from unpaid non-medical collections. Therefore, paid medical collections removed because of NCAP would already have been bypassed from FICO® Score 9.

FICO® Score 9’s enhancements led to improved predictive performance, while ensuring that those with different types of collections would receive a score commensurate with their credit risk. So these enhancements benefited lenders with a more predictive FICO® Score, while benefiting consumers who took a positive step toward credit responsibility and paid off a collection.

In conclusion, since NCAP medical collection removals are so rare, we observed virtually no perceptible impact to the ability of FICO® Scores to rank-order risk, volumes above or below score cut-offs, or bad rates at any given FICO® Score. Lenders can continue to rely on the stability and predictive performance of the industry standard FICO® Score.

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Introducing per-user usage metrics: know your audience and amplify your impact

This June, we released usage metrics for reports and dashboards in the Power BI service. The response has been tremendous. Usage metrics has already been used hundreds of thousands of times by report authors to measure their impact and prioritize their next investments. Since release, by far the biggest request has been for a way understand which users were taking advantage of your content.

You asked, and we delivered.

Today, we are excited to announce that we’re supercharging usage metrics bysurfacingthe names of your end users. And of course, you’re free to copy and customize the pre-built usage metrics reports to drill into the data. The change is currently rolling out worldwide, and should be completed by the end of the week.

This simple change has the potential to magnify your impact like never before. Now, you can understand exactly who your audience is, and reach out to your top users directly to gather asks and feedback.

Excited? Read on for a walkthrough for what’s new in this update of usage metrics.

Feature Overview

Going forward, when you go to dashboard or report usage metrics, you’ll also see a breakdown of number of views by user. The visual includes the display names and login names of your end users.

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Above:new visual in the pre-built usage metrics showing views by user

Note: if you have an existing personalized copy of the usage metrics report, you can continue using it as usual. However, you’ll need to re-personalize a copy to get the new per-user data.

With the “save as” feature, you can copy and customize the pre-built usage metrics report to further drill into how your end users are interacting with your reports. In this release, we’ve augmented the users dimension with display name, login name and the user’s GUID.

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Above: the updated users dimension when customizing the usage metrics report

Once you copy the report, you can remove the pre-set dashboard/report filter to see the usage data – including usernames and UPNs – for the entire workspace.

Tip: the UserGuid (aka Object ID) and UserPrincipalName are both unique identifiers for the user in AAD. That means if you export the usage metrics data, you could join the usage metrics data against more data from your directory, like organizational structure, job title, etc.

For a full overview of usage metrics and its capabilities, read through our documentation.

Administering Usage Metrics in Your Organization

As an IT admin, we understand that you may be tasked with ensuring that Power BI remain compliant with a variety of compliance regulations and standards. With this release, we are giving IT admins further control over which users in their organizations can take advantage of usage metrics.

The usage metrics admin control is now granular, allowing you to enable usage metrics for a subset of your organization. In addition, a new option in the admin portal will allow you to disable all existing usage metrics reports in your organization.

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Above: granular admin controls governing who has access to usage metrics

Together, these features give you full control over who in your organization can use usage metrics, regardless of how it’s currently being used. For example, if you have users taking advantage of usage metrics, yet would like to control its rollout, you could delete all usage metrics content for users to start with a blank slate. From there, you could enable the feature for an increasing set of security groups as the rollout progresses.

Next steps

  • Try out the feature! To get started, head to the Power BI service and go to any pre-built usage metrics report. Note that we’re still rolling out the feature worldwide, so you may have to wait until the end of the week to see it in action
  • Learn more about the feature through our documentation
  • Have comments? Want to share a cool use case for the feature? We’d love to hear it! Please leave comments below or in the community forums
  • Have ideas for where we should take the feature? Create new ideas or vote on existing ones in UserVoice

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How We Have Seen Digital Transformation Impact Three Clients

Digital transformation refers to the introduction of technology into your business processes to help your team work smarter, not harder. We’ve seen companies evolve into paperless offices with better organization of documents, centralized security and the automation of certain processes to reduce costs and improve productivity. We’ve also see how better communication technology, for instance Microsoft’s Skype software, which provides video and voice calls as well as instant messaging, can help a company move faster and maintain accuracy.

Digital technology can help facilitate employee workforce mobility programs, providing a big boost to employee productivity and morale while improving an organization’s culture.

Here are just a few of the ways we have seen our clients benefit from digital transformation:

1) An insurance agency that used to manage their client information on separate spreadsheets. Using Microsoft Dynamics 365 (formerly Microsoft Dynamics CRM) has:

  • provided a consolidated system of all client information across every Broker relationship.
  • significantly reduced the administrative time needed for tracking and updating policy renewal details.
  • resulted in implementation of commission tracking functionality that saves 20 hours a month on what used to be a very tedious manual process.

2) A web design agency wanted to efficiently manage project tasks and budget for individual customer projects. We designed a project management process in Microsoft Dynamics 365 that has:

  • provided Project Templates for each type of project, for quick and consistent setup of new projects.
  • streamlined task management through workflow rules based on project milestones.
  • simplified time entry allowing for real time analysis of project budget vs. project costs.

3) A technology company needed to automate their sales processes based on their newly designed sales methodology. We created a sales workflow in Microsoft Dynamics 365 that:

  • aligns defined opportunity sales stages to the order and completion of relevant sales activities.
  • drives adoption of new sales methodology by providing clear definition of the sales process.
  • reduces sales pipeline subjectivity for better visibility and accuracy of the sales forecast.

Could your business benefit from digital transformation? We think it could, and we’d like to show you how. Download the free whitepaper at www.crmsoftwareblog.com/digital for more examples.

Contact our digital transformation experts at Crowe Horwath at 877-600-2253 or [email protected].

By Ryan Plourde, Crowe Horwath, Microsoft Dynamics 365 Gold Partner, www.crowecrm.com

Follow on Twitter: @CroweCRM

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Timing is Everything: Leveraging Adaptive Sending and Posting to Optimize Impact

insideAO 351x200 Timing is Everything: Leveraging Adaptive Sending and Posting to Optimize Impact

Ever pull into a full parking lot and get a front row spot because someone just happened to be pulling out? Great timing brings great advantages! For marketers, maximizing engagement with your prospects and customers can feel a lot like getting that ideal parking spot – sometimes you get lucky and send out a social post or email to a target contact at the perfect time. Score!

What if you were able to rely less on luck and also improve ‒ and even perfect ‒ your timing? Well, although I’m sorry to say that there’s no app to get you the best parking spot every time, there’s now a better way for you to know how to time your interactions with your target audiences.

Act-On’s Adaptive Journey’s vision is coming to life and I’m excited to be sharing some of our tech and products teams’ great progress on this front. Specifically, I’m referring to the release for all customers of our new Adaptive Social Posting, along with the private beta for Adaptive Sending that begins soon.

For those of you who weren’t able to make it to Act-On’s San Francisco I♥Marketing event (don’t worry, if you’re on the east coast we have one in New York on August 8) to hear about these advancements in person, I thought I’d take a moment to highlight our product’s two very helpful new capabilities.

Adaptive Sending – solving the age-old dilemma of when to send that email

A common outbound marketing dilemma is figuring out when to send that email. You can read one blog post that says the best open- or click-through rates are on a Sunday at 6 pm … and then read another that claims Tuesday at 10 am is best. Most marketers try to take into account these published best practices while also leveraging their own results from past campaigns. They also do a little A/B testing and ‒ let’s face it ‒ guesswork.

The problem, however, is that the buying journey for each individual can easily look like this:

In the above picture, the interaction times are very different for Ben (morning) vs. Beth (afternoon/evening).

Act-On is solving this all-over-the-map buyers’ journey challenge by not only having a platform that tracks, scores, measures, and connects all of these interactions, but also automatically learns from them. What we call “Adaptive Sending” includes the following main tenets:

  1. Assess each individual’s interaction times.
  2. Predict the best time each person should receive an email (within a campaign that may include thousands of contacts).
  3. Do all of this with one-click simplicity.

This sort of capability goes beyond just adjusting send times for time zones ‒ a function we already do today and that some vendors in this space consider “smartsend” functionality. It also goes beyond just looking at past behaviors and engagement in aggregate to provide one generally recommended send time for all recipients; our tailored-to-the-individual approach is something few vendors do. In addition, Adaptive Sending doesn’t just leverage past email interaction data (opens, click-throughs, etc.), it also takes into consideration each prospect and customer’s past engagement times with your company, such as visits to your website and landing pages and social engagement.

It’s easy to see why we think Adaptive Sending will be a big deal: The impact of a 6% vs. a 3% click-through or conversion rate can be monumental across all campaigns.

Adaptive Social Posting solving the common inbound marketing dilemma of when to post

As in the outbound marketing activity of emailing a contact, marketers have the similar challenge of determining the best times to post on LinkedIn, Facebook, or Twitter. They can look across their past campaigns for clues, of course. They can also consult research, but it always seems to tell a different story. However, with the new Adaptive Social Posting capability, which is now available to all of our customers to flip on in Labs, Act-On will be constantly learning when your target audience has engaged with you the most on the social web and when they’ve shared or liked or commented or clicked through your past posts the most. It only takes a simple one-click step to leverage this functionality in our out-of-the-box social capabilities (see example below).

This innovative system takes the guesswork out of social posting and allows our customers to drive more impressions and engagement ‒and ultimately greater conversion rates.

There’s much more to come as we drive forward with our Adaptive Journeys vision. I’m looking forward to seeing customers start leveraging some of these great capabilities … and then reap the gains!

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How a new wave of machine learning will impact today’s enterprise

 How a new wave of machine learning will impact today’s enterprise

Advances in deep learning and other machine learning algorithms are currently causing a tectonic shift in the technology landscape. Technology behemoths like Google, Microsoft, Amazon, Facebook and Salesforce are engaged in an artificial intelligence (AI) arms race, gobbling up machine learning talent and startups at an alarming pace. They are building AI technology war chests in an effort to develop an insurmountable competitive advantage.

Today, you can watch a 30-minute deep learning tutorial online, spin up a 10-node cluster over the weekend to experiment, and shut it down on Monday when you’re done – all for the cost of a few hundred bucks. Betting big on an AI future, cloud providers are investing resources to simplify and promote machine learning to win new cloud customers. This has led to an unprecedented level of accessibility that is breeding grassroots innovation in AI. A comparable technology democratization occurred with the internet in the 1990s and, if AI innovation follows a similar trajectory, the world will be a very interesting place in five years.

First, advances in computing technology (GPU chips and cloud computing, in particular) are enabling engineers to solve problems in ways that weren’t possible before. For example, chipmaker NVIDIA has been ramping up production of GPU processors designed specifically to accelerate machine learning, and cloud providers like Microsoft and Google have been using them in their machine learning services.

These advances have a broader impact than just the development of faster, cheaper processors, however. The low cost of computation and the ease of accessing cloud-managed clusters have democratized AI in a way we’ve never seen before. In the past, building a computer cluster to train a deep neural network would have required access to capital or a university research facility. You would have also needed someone with a PhD in mathematics to understand the academic research papers on subjects like convolutional neural networks.

Although everybody points to improvements in CPU/GPU as the primary driver of AI innovation, this is only half the equation. Advances in AI algorithms in the mid-1980s broke the spell of the AI winter of the 1970s. The work of deep learning pioneers like Geoffrey Hinton and Yann LeCun solved some of the critical shortcomings that plagued earlier algorithms. In many ways, algorithms like Hinton’s backpropagation opened the floodgates for future algorithmic innovations, albeit these improvements happened at a slower, academic pace. DeepMind’s AlphaGo program, for example, combined deep learning with reinforcement learning to enable a computer that beat the world’s highest-ranked Go player in 2017 – a full 20 years later.

Historically, AI has been defined by the ability of a computer to pass the Turing test, which meant the public wasn’t going to be happy with AI until they had a walking, talking robot. Anything less was considered a failure. We’re still far away from creating this kind of general AI, but we’re already solving some types of advanced problems with machine learning, a subset of AI proper. Rather than focus on general intelligence, machine learning algorithms work by improving their ability to perform specific tasks using data. Problems that used to be the exclusive domain of humans – computer vision, speech recognition, autonomous movement – are being solved today by machine learning algorithms.

In fact, machine learning has become such a huge area of focus and, for all practical purposes, the term machine learning has become synonymous with AI. Ultimately this is a good thing. The more consumers and companies start associating the term AI with real-world applications of machine learning like self-driving cars, the more they realize that AI is a real thing. It’s here to stay, and it holds the promise of reshaping the technology landscape over the next several years.

Enterprises should take advantage by aligning their cloud and technology stacks with providers who are leaders in AI. The gap between the AI haves and have-nots will continue to widen, so picking the right technology providers is critical. For example, a non-AI powered CRM system might allow your sales team to find prospective customers based on the last time they were contacted, helping sales reps search for potentially fruitful leads. But an AI-powered CRM system, in contrast, could proactively feed leads to sales reps in real time using algorithms designed to maximize the likelihood of a sale, based on breaking information about the customer, their company, and the sales rep herself. Choosing the right CRM vendor in this case could have a direct and significant impact on revenue.

This year will, if it hasn’t already, bring the realization that if we don’t develop strong in-house machine learning capabilities now, we’ll end up on the wrong side of the future of technology. However, rather than hire teams of AI innovators like the first wave of AI tech giants have done, today’s technology companies must build their AI capabilities using out-of-the-box machine learning tools from AI-focused platform providers like Microsoft and Google.

The increasing demand for AI-driven technology, combined with the dearth of machine learning talent in the labor pool, will force the democratization of data science. Indeed, Google and Microsoft are betting the farm on this trend, which is why they’re making such huge investments in machine learning education and easy-to-use AI tools.

Jake Bennett is CTO of Seattle-based agency POP.

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Microsoft Teams and its Impact with Microsoft Dynamics 365

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Microsoft Teams is a chat-based workspace in the Office 365 suite. Microsoft introduced Microsoft Teams on November 2016, and was designed as a competitor to Slack. It is a platform that combines workspace chat, meetings, notes and attachments. By being able to integrate with the company’s Office 365 subscription office productivity suite including Skype, it features extensions for integrating other technologies. Microsoft Team’s promising features raises a lot of questions heading into the future as everyone is moving to the cloud with Microsoft Dynamics 365. In this blog, we will explore some of the main features Microsoft Teams has to offer and how it ties with Microsoft Dynamics 365.

Microsoft Teams has a goal of bringing everything together – people, conversations and content. It promotes easy collaboration such that a working team can achieve more. The chat space named Conversations is grouped into Teams, which can be further modularised into Channels. This is where persistent and threaded chats are used to keep Team members engaged. Team-wide and private discussions are available within these constraints.

image thumb Microsoft Teams and its Impact with Microsoft Dynamics 365

Conversations is not limited to messaging. Common resources shared within a Team are posted in the Conversations or pinned in Tabs for easy access. This includes shared files, calendars, and other content which encourages collaborative editing. Each Team and Channel can be customized accordingly when adding Tab. Shared and integrated components are publicly available to Team members. This ranges from anything in the Office suite, and other things such as website URLs, PowerBI dashboards, Zendesk and SharePoint.

On launch, Microsoft Teams shipped with over 70 Connectors and 85 Bots which can participate in conversations. For example, within Conversations, you can create polls using the Polly Bot by tagging Polly and adhering to the simple question-answer format. T-Bot is also available in Chat for assistance with anything related with Microsoft Teams. Establishing the Dynamics 365 Connector with Microsoft Teams will be further explained in a later blog post.

Currently, functionality between the Connector is still quite limited. It works similarly like the Office 365 Connector for Groups in which new or existing Office 365 Groups with Dynamics 365 are connected such that the Group is notified when new Activities are posted. This happens similarly with Teams where the connection is made on a Channel within a Team, and Team members get notified for any updates of activities from specific CRM records. In the image below, I established a Dynamics 365 connection in a Channel within a Team. Then in my Dynamics 365 environment, I tested the connection by making an Activity record for the Account record “A. Datum Corporation”. The Meeting details are highlighted below, and Teams handily includes a summary of the monitored CRM record such as Annual Revenue and Number of Employees.

image thumb 1 Microsoft Teams and its Impact with Microsoft Dynamics 365

Dynamics 365 can still be used in Microsoft Teams as it is IFD by adding it in the Channel Tabs as a website. By simply entering in the Dynamics 365 URL, you can use Dynamics 365 within Microsoft Teams as normal. The user would still need to authenticate themselves by entering their username and password (you’d expect this to be done automatically especially since Microsoft Teams already uses Office 365 accounts).

image thumb 2 Microsoft Teams and its Impact with Microsoft Dynamics 365

One thing to note though is that using Dynamics 365 within Teams is not intuitive to use and user friendly. For one, when navigating in and out of the Dynamics 365 Channel Tab would reset wherever you were previously in Dynamics 365 and it would take you back to the landing page. Most Dynamics 365 users also have multiple Dynamics 365 records open in multiple browser tabs or windows which Microsoft Teams does not do as it is not a committed browser. Another interesting thing to note is that a user has to enable Microsoft Dynamics 365 within your pop-up blocker. Notably, I initially could not use Advanced Find within Microsoft Teams because of the following pop-up error:

image thumb 3 Microsoft Teams and its Impact with Microsoft Dynamics 365

It will be interesting to know how further development of Microsoft Teams will affect its integration with Microsoft Dynamics 365. Some things to consider include how Teams and its integration with Outlook items such as Meetings can transfer to Dynamics 365 and its congruent Dynamics 365 Activity records. Adding to these are Skype’s integration with video meetings, and implications of CRM for Outlook. This would of course depend on the demand of users who use Microsoft Teams as their centralized software tool.

Microsoft Teams is a promising tool to tie in multiple integrated technologies including Microsoft Dynamics 365. The use of Dynamics 365 in Microsoft Teams still need refining and tweaking. There is a lot of setting up to do within each Team and Channels, the technologies involved, and most importantly, setting up the actual process of people using the tool to collaborate and share information within a single centralized platform. It’s too early at this stage to know how Microsoft Teams will be accepted by users in conjunction with Microsoft Dynamics 365, but it is exciting to see how it would go about in accomplishing its goal of bringing everything together.

 Microsoft Teams and its Impact with Microsoft Dynamics 365
Magnetism Solutions Dynamics CRM Blog