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Why Choose Microsoft Business Applications to Solve Your Business Challenges? Part 2

April 5, 2021 Microsoft Dynamics CRM

At HCL-PowerObjects, we are 100% focused on Microsoft Business Applications, including Dynamics 365 and Power Platform. It’s literally all we do. In the sales process, we are sometimes asked why Microsoft? It’s a question we’ve answered a thousand times in hundreds of different ways because there are just so many reasons. We’ve distilled our why Microsoft? answer to nine key reasons. In last week’…

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Qlik makes pulling data from SAP applications simpler

March 12, 2021 Big Data

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Qlik, a provider of data integration and analytics software, this week announced it has made it easier to pull data from SAP applications using a set of accelerators optimized for specific business processes.

The first in what will become a series of accelerators is focused on SAP Order to Cash analytics. The accelerators combine data integration and analytics software from Qlik to reduce the time and effort required to surface insights from specific processes within an SAP enterprise resource planning (ERP) application.

The overall goal is to make it simpler to pull data from any SAP application into the data warehouse of their choice, said Matt Hayes, vice president of SAP Business at Qlik.

While SAP currently provides connectors to those databases, its primary focus is on moving data from its applications to a data warehouse based on the SAP HANA database, Hayes said. In contrast, a set of more agnostic connectors provided by Qlik makes it easier to pull data from an SAP application into data lakes provided by, for example, Amazon Web Services (AWS), Microsoft, Google, or Snowflake, added Hayes. He asserted that Qlik data integration software enables real-time delivery of SAP data from any source to any target.

The focus on data integration and analytics within enterprise IT environments has never been greater. Due to the economic downturn brought on by the COVID-19 pandemic, business leaders are trying to optimize in real time a wide range of processes. Achieving that goal requires increases reliance on analytics applications that are only as useful as the most recent data collected.

As organizations launch various digital business transformation initiatives, many of them are discovering they need to be able to pull data from SAP applications that function as systems of record in the enterprise. The challenge has always been that data from an SAP application has to then be normalized alongside other data to enable end users to surface insight from data created using multiple applications. Many organizations are now investing in data warehouses in the cloud to collect massive amounts of data that can be accessed more easily by a range of analytics applications.

Qlik is making a case for a Qlik Sense analytics application that runs in memory to make it easier to surface insights in near real time as data is continuously pulled from various sources. “You never have to refresh the data,” Hayes said.

However, IT teams can employ the data integration software Qlik provides without having to adopt Qlik Sense. This week Qlik revealed it has developed a unified connector based on SAP BEx/InfoProvider joint connectivity and SAP SQL connector software to streamline the process of pulling data from any SAP application into the Qlik Sense Enterprise edition of its analytics software.

SAP has never been especially focused on making it simpler to pull data from its applications and databases. The company has its own portfolio of analytics applications that are tightly integrated with its data warehouse and associated data virtualization tools. However, many organizations have standardized on a wide range of applications that are employed to analyze data aggregated from multiple data sources. Many of the users of those applications want to be able to access data without IT intervention, Hayes noted. Most IT teams are inclined to enable that access so long as the integration and analytics software employed doesn’t have a material impact on the performance of the SAP applications they are running, added Hayes.

Qlik minimizes that impact by identifying what subset of data in an SAP application is actually new versus constantly pulling all the data in an SAP application into an analytics application, said Hayes.

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How to build AI applications users can trust

December 28, 2020 Big Data

To work effectively, algorithms need user data — typically on an ongoing basis to help refine and improve the experience. To get user data, you need users. And to get users, especially lasting users who trust you with their data, you need to provide options that suit their comfort levels now while still allowing them to change them in future. In essence, to get user buy-in, you need a two-step approach: Let users know what data you want to collect and why, and give them control over the collection.

Step 1: Providing continuous transparency

The first step in finding the balance is to equip your users with knowledge. Users need to know what data is being collected and how that data is being used before they decide to engage with an application. Already, mounting pressure on the industry is steering the ship in this direction: Apple recently announced a privacy label for all of its applications that will promote greater awareness for users around what data is being collected when they use their apps. Microsoft’s CaptionBot, below, is a good example of how to give users an easy-to-understand overview of what’s happening with their data behind the scenes.

Microsoft’s CaptionBot offers clear information about data storage, publication and usage as well as an easy-to-understand overview of the kinds of systems working behind the scenes to make the AI captioning tool work.

Above: Microsoft’s CaptionBot offers clear information about data storage, publication and usage as well as an easy-to-understand overview of the kinds of systems working behind the scenes to make the AI captioning tool work.

Health app Ada, below, is an example of how to avert user confusion over data collection choices.

Above: Health app ada explains the logic behind its input selections at the outset, so users can understand how their inputs affect the application and its ability to perform the desired actions.

Not only does sharing this information upfront give users a sense of empowerment and help build trust with your experience over time, it also gives you an opportunity to help them understand how sharing their data can improve their experience — and how their experience will be diminished without that data. By arming users with information that helps them understand what happens when they share their data, we also arm them with the tools to understand how this exchange can benefit them, bolstering their excitement for using the app.

In addition to these details upfront, presenting users with information as they use the application is important. Sharing information about algorithm effectiveness (how likely the algorithm is to succeed at the task) and algorithm confidence (how certain the algorithm is in the results it produced) can make a big difference when it comes to user comfort in engaging with these technologies. And as we know, comfort plays a major part in adoption and engagement.

Consider the confidence ratings Microsoft offers in some of its products, below.

Above: When an algorithm is making a “best guess”, displaying a confidence rating (in the first image using Microsoft’s Bing Image Search, a rating between 0 and 1, and in the second from Microsoft’s Celebs Like Me, a percentage rating) helps users understand how much trust they should place in the outcomes of the algorithm.

Users should be given insight into some of the operations and mechanics, too. So it’s important to acknowledge when the mechanisms are at work or “thinking”, or when there’s been a hand-off from the algorithm to a human, or when data is being shared to third-party systems or stored for potential later applications. Continually offering up opportunities for building awareness and understanding about your application will lead to higher levels of trust with using it. And the more users trust it, the more likely they will be to continue to engage with it over time.

Step 2: Handing over control

Even when the benefits of an application are compelling enough for users to opt in, users don’t necessarily want to use AI all the time. There may be circumstances when they want to withdraw from or limit the amount they engage with the technology. How can we empower them to choose the amount of AI they interact with at the moments that matter most? Again, a combination of upfront and semi-regular check-ins works well here.

When informing users about what data you’re collecting and how it’s being used, give them the chance to opt out of sharing certain types of data if the use case doesn’t meet their needs. Where possible, present them with a graduated series of options — what you get when you enable all data sharing versus some versus none — to allow them to choose the option that makes the most sense for them.

Consider the example below from food-ordering app Ritual.

Above: Popular food ordering app Ritual allows users to opt of sharing certain data and also informs users of how opting out will impact the application’s functionality.

Whenever you add a new product feature or a user engages with a feature for the first time, prompt them to look at or change their level of data sharing. What may not have seemed relevant to them before could be very compelling with a new use case presented. And if a new type of data is being collected, prompt them again.

One final way to offer up control: Give users the chance to direct the application. This can mean simply checking in with your users from time to time about what features they like, which ones they don’t, and what they want from your application. Or, more importantly, it can be as a part of the application itself. Can users adjust the level of certain inputs to produce different results (e.g. weighting one input over another for a recommendation algorithm)? Can they go back a step or override certain aspects manually? Handing over the controls in as literal a sense as possible helps users feel empowered by the application instead of intimidated by it.

Youper’s AI therapy app provides a good example of how to offer users control.

Above: Youper’s AI therapy app doesn’t require users to set all parameters at the outset but instead offers up regular opportunities for them to refine their experience as they continue to engage with the application (and explains why it may help them to do so).

Every application is different, and every approach to empowering users will be a little different as a result. But when you offer transparency into how and why your system is taking in the information that it is, and you give consumers the chance to opt out of sharing certain pieces of information, you create space for trust. And when your users trust you, they’ll be more inclined to share the data you need to make your products and services come alive.

Jason Cottrell is Founder and CEO at Myplanet.

Erik von Stackelburg is CDO at Myplanet.

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Ditch the Integration Between Dynamics 365 and 3rd Party Marketing Applications

November 20, 2020 CRM News and Info

Digital marketers and salespeople understand the importance of having a database with correct and up-to-date information. Sending an email to an invalid inbox or calling a phone that is no longer associated with a prospect can waste valuable time and even hurt your domain sender score in the case of sending to inactive emails.

Traditionally, CRM and marketing applications have stayed separate making it difficult to manage data effectively between systems and teams. Sure, integrations can be built to allow sales and marketing systems to speak to one another, but these integrations are often patched together and sync limited amounts of information. By utilizing Dynamics 365 Marketing and Dynamics 365 Sales together, all data is stored in the same database meaning no integration is necessary.

Unified Contact Data

The contact entity in Dynamics 365 is accessible and editable from both Dynamics 365 Marketing and Dynamics 365 Sales. This means if a salesperson updates a contact’s address in Dynamics 365 Sales, that address change can be seen from the contact record in Dynamics 365 Marketing. Shared data between Sales and Marketing is especially helpful for marketers that need to maintain marketing lists. Typically, marketers will need to export contact lists from their CRM system into whichever marketing application they are using to send emails or other campaigns.

The issue with this process is that unsubscribes, bounces, opens, link clicks, and other analytical data never sync back to their associated contact records in CRM. Having a system that can conduct all digital marketing campaigns while also maintaining contact data for sales and marketing is a game changer.

In Dynamics 365, the contact record stores all consent information related to marketing activities conducted from Dynamics 365 Marketing. When a contact unsubscribes via the subscription center page, the “Email: Do Not Allow” field on the contact record is updated to reflect that they no longer want to receive emails.

Tracking Marketing Insights

Marketing analytics should be used by both marketing and sales teams, but frequently that data is unavailable to salespeople in CRM. Dynamics 365 stores marketing analytics within the Dynamics 365 Marketing app, but also showcases marketing data related to individual contacts right on the contact record. The contact Insights tab of the contact record includes tables for email interactions, website interactions, event interactions, form interactions, and even email open times. Below is an example of the high-level insights breakdown chart included for each contact.

This data is useful to salespeople who need to see how engaged their prospects are with the company. If a salesperson sees that a prospect they have been speaking with opens every email campaign and visits the website once a week, they know that the prospect may be in a good place to convert to a sale. A sales team can also easily implement a lead scoring model in Dynamics 365 Marketing that tracks contact engagement with marketing efforts and assigns scores based on interactions. Learn more about creating lead scoring models in Dynamics 365 Marketing here. For marketers, tracking contact preferences like unsubscribes means they no longer need to tediously check their marketing list before each email send.

Get started with Dynamics 365 Marketing

Not sure where to start with Dynamics 365 Marketing? Our team can work with you to develop a step-by-step implementation plan to get started quickly, but not overwhelm your team. If you have contact data stored in a 3^rd party system that you would like to sync with Dynamics 365 contacts, we can build an integration to sync that data and ensure it is clean moving forward.

Schedule a call with our Dynamics 365 Marketing team.

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How to Create Microservices-based Applications for AWS

October 28, 2020 TIBCO Spotfire

Reading Time: 2 minutes

Market demands are shifting rapidly, with many disruptive forces in motion. Businesses are reacting in a number of different ways to preserve cash, change the way they operate, and accelerate digital business initiatives to capture new value. Today’s disruptions are planting seeds for broad and permanent changes across all markets, so businesses need to act now in order to prepare for what’s to come in the near future. In order to combat these forces, a business needs to be agile so that it can rapidly adapt its operations as well as its products and services to meet the new market conditions. Either way, the business that is able to react quickly maintains resiliency and has a foundation for rapid growth and innovation

A key starting point for increasing business agility is the digital platform, as businesses are operating more with digital services than manual, rigid, paper-based processes. If you aren’t able to rapidly adapt the services and capabilities of your digital platform to stay aligned with the needs of the business, then your underlying application architecture needs to be evolved so that it becomes more agile. One way to build this agility is by evolving to a microservices architecture.

Microservices are very small units of executable code. The industry has long preached the benefits of breaking down large, monolithic applications into smaller units of execution. But technology has evolved in recent years so that now this strategy creates high performing apps. Microservices can be used to break up monoliths into individual, highly cohesive business services that are deployed in containers and serverless environments. Thus, microservices can each be adapted, deployed, and scaled independently of other microservices. This gives the business a high degree of flexibility to adapt to the digital platform very quickly.

TIBCO Cloud Integration makes it easy to develop and deploy your business logic in event-driven microservices and functions to AWS. You can use pre-packaged connectors for AWS to connect to a wide variety of Amazon services to create application logic. The entire application architecture is highly efficient and cost-effective which will accelerate your adoption of AWS technologies.

TIBCO Cloud Integration simplifies the development and deployment of event-driven applications built with microservices and functions to AWS. Once apps are created, you can package your microservices into a Docker Image, and then deploy them into the AWS container management service of your choice including Amazon EKS, ECS, and Fargate for deployment to AWS, or other container management services. They also can be deployed seamlessly to AWS Lambda.

TIBCO’s extensive experience in intelligent connectivity, combined with AWS’s highly flexible and scalability cloud platform makes for a natural partnership. TIBCO is an AWS Advanced Technology Partner. We partner with AWS in both technology and business development initiatives. We have many solutions that run natively on AWS, and that are also available for purchase through the AWS marketplace, not only for connectivity, but also for analytics and machine learning, and data management.

Microservices can each be adapted, deployed, and scaled independently of other microservices. This gives the business a high degree of flexibility to adapt to the digital platform very quickly. Click To Tweet

To learn more about how to create microservices-based applications for AWS, watch this webinar hosted by BrightTalk. And to learn more about TIBCO Cloud Integration, watch our demos or sign up for a 30-day free trial.

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10 surprising applications of machine learning in everyday life

July 10, 2020 BI News and Info

There’s no doubt about it—machine learning and artificial intelligence have significantly changed our world over the last few decades.

Every day there seems to be a new breakthrough about how AI and ML can be leveraged for positive development, to the point that it’s sometimes hard to tell what’s real and what’s being sensationalized.

With headlines about AI replicating our brains or reading our minds or driving our cars, we often miss the many simple yet genius applications of machine learning that are right in front of us.

Applications of Machine Learning in Everyday Life

To help bring us back down to Earth, we wanted to look at some of the surprising and interesting ways that ML is impacting our daily lives, whether we see it or not.

1. Managing traffic

AI excels at managing logistics, and managing a city’s traffic is one of the most complicated and difficult pieces of logistics to understand. Millions of datapoints and thousands of commuters must be properly accounted for, in effort to ensure the trains run on time, traffic lights are functioning correctly, and maintenance is performed where and when it’s needed.

In many modern cities, machine learning needs to be at the center of the transportation system. Data like rush hour fluctuations, road wear and tear, and existing maintenance schedules can be used to build powerful ML models. Ultimately, these models help improve traffic flow, increase the usage and efficiency of sustainable modes of transportation, and limit real-world disruption by modelling and visualizing future changes.

2. Making beer

Despite traditionally involving only three ingredients (water, barley, and hops), the art of brewing beer is a complicated and precise scientific process. The simplest change can dramatically affect the flavor and even ruin a batch—whether it’s the temperature of which it’s brewed or the amount of time the brew sits for. And that’s without considering the countless ingredients that are used in modern brewing techniques.

With machine learning algorithms, brewers can more easily make and keep track of these micro-adjustments to ensure flavor quality and consistency. ML can also be used to develop and refine new flavors by analyzing past popular and successful brews, as well as brews that failed or were less popular.

Breweries can even make batches specific to one customer’s tastes by tracking their past buying habits and beer preferences. Best of all, this technology is scalable and can be applied to as large or small a batch as the brewer wants.

3. Better Translation

How many times have you pulled out your phone to translate a phrase you don’t understand? Or searched how to properly pronounce a word with more vowels than you thought was possible? Speech and translation technology are nothing new, having been available via various tools for years. But AI and ML are making huge changes in just how effective the technology is.

Translating two related, modern languages like French and Spanish is one thing, but translating a 2,500-year-old, hand-carved dead language into modern English is a painstaking and time-consuming task even for expert archeologists.

Luckily, artificial intelligence-powered computer vision is making it possible to automate the translation of the ancient Persian cuneiform, giving researchers far more material to examine and helping to ensure accuracy. This technology doesn’t just work on ancient tablets though; it can be used on any language to translate even handwritten cursive into easy to read print.

ML isn’t just limited to translating either; researchers are working on technology that translates brain signals into speech with 97% accuracy. While mind-reading AI is still a ways off, this technology could soon help people without the ability to speak to communicate in ways they never could otherwise.

4. Creating video games

The video game industry, more than many others, is constantly pushing the technological standard in order to present the best experience for players. Developers are no strangers to using rudimentary AI algorithms in their games.

Now, AI is even creating games in real-time for players to interact with, with no underlying game engine needed. For Pac-Man’s 40^th anniversary, researchers at NVIDIA trained an AI on 50,000 playthroughs of the game, with the end result being a completely ML-powered edition of Pac-Man.

This technology goes well beyond recreating arcade classics, of course. Game developers can use the underlying technology to add details to environments in real time, create enemies that can dynamically learn from players’ behaviors, or create entire levels that change dynamically based on players’ actions.

5. Redefining music

Music has constantly been evolving ever since a caveman banged two sticks together, and technology has always been the biggest factor in pushing the art form’s boundaries.

Now, machine learning is posed to redefine just what music is. Researchers have developed AIVA (Artificial Intelligence Virtual Artist) and taught it how to compose music in a classical style.

AIVA was built using deep learning and reinforcement algorithms, allowing AI to develop an understanding of what makes music sound pleasing to human ears, while still being able to learn and improve its music over time. This also helps AIVA to insert some variety into its music, avoiding each composition sounding the same.

The most impressive thing about AIVA is that its music isn’t just theoretical. AIVA’s compositions have already began to appear in commercials, film, and video games, with the copyrights to each piece being under AIVA’s name.

6. Moderating social media

Proper moderation, filtering, and routing of messages has been a problem for a variety of businesses that deal with customer service issues, and have been an especially difficult problem for social media platforms, with no real solution proving to be viable—at least not until now.

With the current climate of misinformation around COVID-19 and the pervasive use of hate speech on online forums, Facebook has turned to artificial intelligence to help keep its platform in check. The AI they developed will act in a role like that of the human moderators, some of which it is replacing.

Facebook’s algorithm is trained to recognize hate speech and fact-check misinformation. Some of the key features it looks for are duplicate images, which often indicate a meme or infographic that’s circulated by bots or trolls on the platform. The difficulty with this solution is the risk that images and memes using the same format may get banned, even if they aren’t being used for nefarious purposes.

While researchers are hopeful that the AI will be able to detect when a post is similar but not the same as banned content, this is still a job for humans in many cases. Nevertheless, machine learning is making it much easier for moderators to better police Facebook and other social media platforms.

7. Deciding what to eat

Do you feel like Mexican food for lunch? Italian? Dine in or eat out? Luckily, machine learning can help you make those decisions and find exactly what you want.

Simple forms of this technology exist already in restaurant reviews apps, map apps, and food delivery services that track what kind of food you like to eat and make predictions about what you might want based on your location, history, and time of day. But Kartik Dhawan, writing on Towards Data Science, took it a step further by using text analytics to determine what kind of ramen noodles he wanted, down to the region, ingredients, and flavor profile.

Dhawan compiled data from a ramen reviews site and performed text analysis on the ratings and relative frequency of specific flavors and ingredients to determine which ‘themes’ were most popular among Ramen lovers. By combining this analysis with brand and region ratings, Dhawan was able to pick the best variety of ramen for himself. This technology has much broader applications in the future as well, with services analyzing your reviews, ratings, region, and dining history to determine the best dish for you.

8. Tutoring students

The current COVID-19 pandemic is forcing students and teachers to adapt to new ways of learning and teaching, especially while not in-person. Luckily, AI is proving an invaluable tool in this new teaching environment.

Researchers at Carnegie Mellon University developed an machine learning tutor that can dynamically learn from a teacher by attempting to solve problems on its own, and using corrections from its teacher to extrapolate solutions to other problems, as well as alternate methods for solving the problem that even the teacher may have not thought of.

The AI can then use the different techniques it’s learned to teach students in the same way and adapt to any difficulties that the students might have. For instance, if a student has difficulty in understanding the order of operations in a larger math problem, AI can respond dynamically to the student’s specific question in a way a video or virtual class would be unable to do effectively.

9. Helping to fight COVID-19

Just about every industry—from automotive manufacturers to liquor distilleries—have stepped up to help in light of the current crisis, and the data science community is no exception. Machine learning has been absolutely instrumental in tracking and projecting the spread of the virus, and now it’s helping slow the spread as well.

Workplaces and stores that are carefully reopening have integrated machine learning algorithms to help ensure employees and customers are as safe as possible. Existing security cameras are being equipped with AI to identify when people are not properly social distancing or wearing masks and informing managers accordingly.

While this technology already existed in other forms—for example, monitoring whether or not safety gear is being worn in factory settings—its broader adoption will likely lead to more safety and security developments, even after the epidemic ends.

10. Developing new fragrances

For perfumeries, the choice to integrate AI into their processes is a no-brainer. Perfumes are a delicate balance between art and chemistry, and machine learning is helping to combine the two seamlessly and inexpensively.

One fragrance startup, Scentbird, uses a custom-built ML model with data gathered from customers to develop specialized and unique scents for its human counterparts to test. Another company, Symrise, has its own dedicated AI perfumer, called Phylra. While Scentbird’s ML models generate scents for human approval, Phylra is free to make its own decisions and evaluate how customers respond to fragrances all on its own.

ML also helps identify where alternative ingredients can be used, driving the price of manufacturing down while using more natural, environmentally safe ingredients.

Wrapping Up

When it comes to machine learning, the possibilities are virtually limitless. ML has found its way into nearly every industry – from manufacturing to healthcare, travel, media and so many more. Even so, it’s easy to overlook the myriad ways that we interact with AI and ML in our daily lives, as so much of it happens behind the scenes

There’s still a ton of untapped potential when it comes to implementing machine learning, and we’re guaranteed to see even more applications in our everyday lives as time goes on.

If you’re curious how machine learning and AI can impact your business, we’ve compiled a free ebook with 50 use cases to help you get inspired.

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4 Customer Service AI Applications That Work Today

March 4, 2020 CRM News and Info

The concept of artificial intelligence improving emotional intelligence might sound like a paradox. A coded algorithm is not the most natural supplement to human empathy, after all. However, customer experience (CX) leaders understand that AI-powered technologies can transform the way businesses understand core audiences.

Customer service agents benefit from real-time and historical sentiment analysis that takes the guesswork out of reading emotions — especially on phone calls or text-based digital interactions. This gives businesses the insight they need to deliver hyper-personalized experiences. In fact, AI-driven sentiment analysis data has a bevy of uses that elevate emotional intelligence among CX professionals.

For most companies, the process of measuring customer satisfaction occurs far too late in the journey. Traditional customer feedback surveys typically rate experiences after the fact, resulting in stale data that does not help agents in the moment. Proactive customer service requires real-time analysis for positive resolutions and more impactful connections.

Thanks to AI-driven natural language processing, machine learning and computational linguistics, customer service professionals can see accurate sentiment analysis data before their conversation even begins, promoting a service culture that emphasizes empathy and relationship-building.

Following are a few ways AI-powered technologies can improve a business’ emotional intelligence and, therefore, the overall customer experience.

1. Emotional Intelligence in Self Service

AI-powered self-service options are commonplace. For example, self-service channels such as chatbots and virtual agents heavily rely on AI, and consequently have become more and more dependable.

In fact, consumer research shows that chatbot usage jumped almost 10 percent from the end of 2018 to the end of 2019. Today, virtual agents can hold dynamic conversations while performing routine tasks, giving customers the necessary tools to be resourceful and self-reliant. This translates to quicker resolutions at a lower cost, while allowing contact center agents to focus their attention on more high-impact issues.

Beyond automated-response capabilities, self-service channels are also aided by AI-driven sentiment analysis data. This information can diagnose when a self-service experience is not as satisfying or as useful as an agent-assisted interaction would be.

When a live agent is needed, sentiment analysis can recognize the urgency and route the customer accordingly. The insights this technology provides do not stop there. Sentiment analysis data can be layered on top of intelligent routing, connecting a customer with the appropriate agent in real-time based, in part, on personality.

2. Predictive Behavioral Routing

Traditionally, contact centers route customer queries to the first available agent capable of solving the issue, even if the agent was not the best personality and communication style match for the customer. Back then, routing failed to take into account individual compatibility, reducing customer service to a speed dating process, where interactions could be messy and unpredictable.

Predictive behavioral routing intelligently pairs the right customer with the right agent by matching the personality profile and communication style of the customer with the most compatible agent. This matchmaking tool can even enable contact centers to recalibrate their routing settings based on a specific goal, such as improving CSAT or maximizing upsell rates.

Powerful AI algorithms that have been trained and refined over millions of customer service interactions make predictive behavioral routing possible. This technology can ascertain whether a customer is compassionate and caring, logical and reasonable, creative and playful, or a contrast of these personalities.

Every touchpoint informs a unique profile that accounts for these various characteristics. In essence, behavioral analysis reveals the person behind the text in a social media post or the voice on the other end of the phone. Contact centers not only can determine the emotional makeup of a customer, but also can route that customer to an agent emotionally equipped to supply exceptional customer service.

Because predictive behavioral routing pairs customers with the most suitable agents, less energy is needed for the two to get on the same wavelength. As a result, contact center agents are empowered to create lasting connections with customers, because the baseline communication standard is set.

Gone are the days when proficiency, rather than emotional compatibility, was the single significant factor to contact center routing. Customers now can engage with agents who are simultaneously competent and empathetic enough to handle their queries, helping reduce average handle time by optimizing exchanges.

3. Elevating Agent-Assisted Experiences

Intelligent routing alone does not guarantee perfect NPS and CSAT results. Even the most empathetic of employees can encounter communication gaps that impede excellent service. Presenting customer sentiment, recommending next-best actions, and removing the distractions of mundane tasks for agents at the time of service can help bridge those rifts, mitigating risks of conflict or misunderstanding.

This technology assesses customer input from a variety of touchpoints, including phone calls, text messages, emails, chat sessions and even social media comments. After labeling these communicated emotions “positive,” “neutral” or “negative,” these solutions can create a detailed customer profile, providing contact center agents with a comprehensive understanding of relevant preferences and needs. With this data, agents then can prioritize the more technical aspects of a complex issue to ensure first contact resolution.

For contact centers, AI does more than measure sentiment. It provides value to agents outside the emotional domain. It also can automate responsibilities and procedures that inevitably tack on unnecessary time and energy to each interaction. Routine functions, such as data entry, switching applications and internal communications, are automated for agents.

While the rise in automation has spurred some concern about how it will impact the workforce, AI has proven to be a reliable co-pilot for contact center agents providing customer service. In the digital age, agents might have to juggle multiple customers at once, all while operating their various back-end systems. Eradicating mundane tasks undoubtedly helps.

Even more helpful to contact centers are automated cues that alert agents to advised next steps. By analyzing a massive volume of customer interactions, AI can discern the proper course of action for a specific inquiry and offer suggested responses. In these instances, AI can distinguish what response — established via all of the other queries that concern the same and similar matters — will most likely resolve the issue.

AI brings a push-pull dynamic to contact centers. On one hand, it gently pushes agents into a workspace that values empathy by informing them about a customer’s personality and communication style. On the other, AI pulls agents away from contact center’s back-end systems by automating traditional, day-to-day workloads. In both cases, AI improves the customer-agent experience because relationship building becomes the central priority of CX.

4. Detecting Root Cause

The key to unlocking exceptional customer service is identifying systemic flaws and vulnerabilities. Determining the origins of a customer’s frustration will help resolve not only the issue at hand but also a contact center’s shortcomings going forward.

AI in the contact center is more than just a feelings detector. It can extract powerful insights such as root cause sources of customer frustration. Separate from negative sentiment, customer frustration may be the most accurate detector of customer satisfaction, such as NPS, and any underlying service issues.

As CX leaders know, there are a lot of factors and causes that might induce frustration. For example, a brand’s product or service may fail to meet expectations. AI-perceived root cause — detected from interaction analytics — can single out product defects, thereby opening up the possibility of product redesign.

It also can shed light on macro trends for a company. Removing a root cause issue that exists for many customers will ensure that customer service teams do not need to worry about the predicament in the future, cutting down on contact volume.

In addition to root cause, interaction analytics tools that rely on speech and text understanding are able to unearth compliance issues and observe training opportunities as well. As a result, finding and fixing these aberrations can lead to wholesale changes for customer service teams.

Reconciling a systemic problem naturally ends a pain point for customers, which in turn reduces customer frustration. Contact center leaders cannot ignore the training element as well. Every problem presents an opportunity. Identifying structural issues only makes agents more aware of where the traps are hidden, improving individual agent performance and contact center operations at large.

Better Business Outcomes

With the help of AI-powered technologies available today, CX leaders now can see a holistic picture of each and every customer they deal with, bringing them one step closer to achieving mutual understandings and satisfied customer experiences.

While AI may take some of the load off of agents, it does not dehumanize the experience for customers. On the contrary, AI optimizes the workflow of agents, allowing them to embrace personal and powerful connections with customers.

Better connections with customers lead to more loyal consumers — and loyal consumers are primed to turn into brand advocates. Cultivating a satisfied core audience should be the goal of every company, small business or enterprise, and the latest technologies only make positive engagement easier.

As more and more consumers choose digital and self-service channels, AI has become an essential tool for the customer experience.

Chris Bauserman is VP of Product and Segment Marketing at
NICE inContact.

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CRM Buyer

Announcing Power BI updates at the Microsoft Business Applications October Virtual Launch Event

October 5, 2019 Self-Service BI

The Microsoft business applications virtual launch event is almost here! You don’t want to miss this opportunity to get a first-hand look at the new innovations we’re rolling out for Power BI. Register today for this free event, and then tune in to the live stream on October 10, 2019 from 8:00 AM – 9:30 AM Pacific Time.

You’ll get an in-depth look at the new capabilities announced in the 2019 release wave 2 across Dynamics 365 and the Microsoft Power Platform. James Phillips, Corporate Vice President of the Business Applications Group, and other Microsoft experts will guide you through the new updates, answer common customer questions, and discuss what these releases mean for your business.

Come learn how these new Power BI features will help you:

Build a data culture for your organization by empowering everyone with intuitive experiences, a unified BI platform, big analytics, and pervasive artificial intelligence.
Discover hidden actionable insights in data with new artificial intelligence capabilities that require no code.
Manage growing data volume and complexity with petabyte-scale analytics that make Power BI and Microsoft Azure an unmatched combination.
Benefit from the investments we’re making in simple, intuitive experiences that are deeply integrated with Microsoft Office 365 to provide self-service analytics for everyone.
Create a global, governed, scalable, secure, and unified BI platform that meets the needs of both self-service and centralized BI.

With updates, insights, and demonstrations directly from the experts, you’ll better understand how to leverage these new technologies as part of your organization’s digital transformation initiatives.

Onboard the new capabilities in Power BI with confidence – prepare for the updates, read the release plan, and then tune in on October 10.

Come unlock what’s next for your business, register today!

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Microsoft Power BI Blog | Microsoft Power BI

FY20 Microsoft Business Applications Inner Circle Members List Includes 7 CRM Software Blog Members

July 19, 2019 CRM News and Info

Congratulations to the FY20 Microsoft Business Applications Inner Circle Members, including seven CRM Software Blog members.

According to Microsoft the Business Applications Inner Circle partners represent the top 1% of the total Business Applications ecosystem and drive more than 30% of the FY19 Worldwide Business Applications Cloud Revenue.

Inner Circle Winners – CRM Software Blog Members:

More Inner Circle Winners:

adesso AG
AlfaPeople
Alithya
Alterna
Annata
Arbela Technologies (ERP Software Blog member)
Armanino LLP
Avanade
Avtex Solutions, LLC
Axxon Consulting
Barhead Solutions
Blue Horseshoe
Capgemini
Capita
CEC
Cegeka
Columbus A/S (ERP Software Blog member)
Controles Empresariales
COSMO CONSULT Group
Delegate
Cy
DXC Technology
eCraft Oy Ab
eLogic
EOS Solutions Group
Experlogix, Inc.
EY
Fellowmind
Fusion5
Hitachi Solutions
HSO
Incremental Group
Intergen
ITVT Group
KPMG
LS Retail
MASAO
Mazik Global
MCA Connect
Mint Group
NTT Data & Sierra Systems
ORBIS AG
PowerObjects an HCL Technologies Company
PrenticeWorx
Prodware
PROS
PwC
QBS Group
QUANTIQ
Right Group
SAGlobal
Sikich
Sunrise Technologies (ERP Software Blog member)
Tectura Hong Kong Limited
Thinkmax Consulting Inc.
To-Increase (ERP Software Blog member)
Velrada
VeriPark
XPLUS

For the first time, to select the FY20 members of The Business Applications Inner Circle, Microsoft took into account only Cloud Billed Revenue (with the exception of top Business Central partners) and Dynamics 365 Customer Adds. Microsoft also took into consideration these partners’ adoption of the broader Microsoft Cloud portfolio (IoT, AI, etc.), the importance of these partners in their local market and their investments in differentiated solutions offerings.

We congratulate all of these companies on their accomplishment.

By CRM Software Blog Writer, www.crmsoftwareblog.com

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CRM Software Blog | Dynamics 365

On demand now — full session lineup from Microsoft Business Applications Summit!

June 13, 2019 Self-Service BI

The conference might be over, but there’s so much more in store! Get ready to transform your business with the latest in Microsoft Business Applications. Explore 200+ sessions, workshops, and keynotes from Microsoft Business Applications Summit, available now in the Power BI Community.

You can also watch the opening keynote on-demand.

Build your skills at your own pace.

Learn directly from the engineers behind Dynamics 365, Power BI, Excel, PowerApps, Microsoft Flow, mixed reality, and more. Download presentations to review in-depth and connect with experts in community forums.

Get fresh ideas to solve your toughest challenges.

Dig into game-changing technology — like AI and mixed reality — to amplify the impact of the tools you use every day. Collaborate with the community and share hints and hacks.

Jumpstart what’s next for your business.

Watch visionary keynotes and get a first look at the new features and capabilities coming next for Dynamics 365 and the Power Platform. Learn how to take your data, your insights, and your business to the next level.

Here’s a sample of just some of the sessions available on demand, and be sure to check out the Power BI Community for the full rundown:

Microsoft Power BI: The future of modern BI — roadmap and vision
Microsoft’s BI leadership team shares the Microsoft Power BI vision and strategy. Learn how Power BI brings success to organizations and helps you harness the insights that live within your data.

Microsoft Power BI: AI-powered analytics
Learn how AI can empower you to solve your toughest business problems.

Microsoft Power BI: BI power hour
Filled with fun and informative demos, the BI power hour is designed to show the flexibility and power of Microsoft BI.

Microsoft Power BI: Creating accessible Power BI reports
From accessibility testing checklists to tips for cultivating more inclusive design habits, this session will show you how to leverage Microsoft Power BI’s latest accessibility features and make your reports accessible to everyone.

Get inspired, boost your skills, and power major digital transformation at your organization. It’s just a click away — watch now.

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Microsoft Power BI Blog | Microsoft Power BI

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Announcing Power BI updates at the Microsoft Business Applications October Virtual Launch Event

On demand now — full session lineup from Microsoft Business Applications Summit!

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