DJ Envy currently co-hosts Power 105’s The Breakfast Club, but the syndicated radio show won’t be the only place you can see Envy in the coming months. According to a May 21 report from Variety, cable network Bravo has ordered 11 new shows, including a new docu-comedy about the radio personality and his family titled Gold With Envy.
According to the official description for the upcoming show, the series will follow Envy and his wife Gia as they attempt to balance their hectic lives. The show will also feature a cast that includes friends, extended family and their five children, as the couple “works to keep up the street cred that has made them a household name.”
Gold With Envy will be produced by Truly Original, while Steven Weinstock, Glenda Hersh and Lauren Eskelin will serve as executive producers. Envy and his wife will also work as co-executive producers.
“Bravo offers a wide scope of programming that gives viewers many options to escape their normal reality mixing humor and fun with layered storytelling resulting in addictive series that offer unique worlds and characters,” said Rachel Smith, senior vice president of development for Bravo Media.
“With this new slate of development, we’re testing unexpected environments and loud formats that aim to attract a wide audience while staying true to what makes Bravo so distinct,” she continued.
The DJ celebrated the news on Instagram, posting a screenshot of the report and writing, “@bravotv + TheCaseyCrew = A Real Family Show… CoExecutive Producer – Raashaun & Gia Casey. Envy’s real name is Raashaun Casey.
The Dynamics 365 Spring 2018 release coincides with the new release of Power Apps and the Common Data Service. This release brought about a significant change to the Common Data Service – dividing them into two different areas. Common Data Service for Apps and Common Data Service for Analytics. Our upcoming webinar Spring Release: The Common Data Service and Dynamics 365 will go through all of the changes and enhancements in this new release and how the new Common Data Service for Apps interacts with the Dynamics 365 application platform.
In this blog, we will touch on some of the enhancements that will be discussed in the webinar. Be sure to register to learn about all the new features!
Integrations – Field Service
There is now data integration support between Dynamics 365 Finance and Operations and Dynamics 365 for Field Service. This new functionality includes:
Enabling invoicing of Dynamics 365 for Field Service work orders and agreements in Finance and Operations.
Integration of warehouse information with on-hand inventory, item reservations, usage, adjustments, and transfers.
Support for purchase order integration with synchronization of vendors, purchase orders, and receipts.
Integrations – Dynamics 365 for Project Service Automation
An integration solution using the Common Data Service enables synchronized data between Dynamics 365 Finance and Operations and Dynamics 365 for Project Service Automation. The solution enables the flow of data for several scenarios including the following:
Maintain project contracts in Project Service Automation and synchronize them directly from Project Service Automation to Finance and Operations.
Create projects in Project Service Automation and synchronize them directly from Project Service Automation to Finance and Operations.
Maintain project contract lines in Project Service Automation and synchronize them directly from Project Service Automation to Finance and Operations.
Server-Side Logic for Validation
The addition of Business Rules on the Common Data Service for Apps entities now enables you to build business logic for Apps. You can use the Business Rules to:
Set default values for fields based on specific parameters.
Create validation logic for any entity field or a combination of fields.
Trigger workflows and processes from Business Rule logic.
Additional Sorting Release highlights include:
27 new connectors for PowerApps – connectors for Excel Online, Microsoft To-Do, ServiceNow, Workday and Azure SQL Data Warehouse, along with many more
Import data into Common Data Service for Apps with Power Query. Now you can use Power Query on the web to directly import data into the Common Data Service for Apps from different data sources
Support for additional data types. Additional data types support more complex entity definitions. New types include Multi Select Option Sets
Editor’s note: This article on data bias written by Syncsort’s Harald Smith was originally published on Infoworld.
Biased data and decisions represent significant risks to your organization both monetarily and ethically and ultimately may impact your ability to achieve revenue goals and maintain brand reputation
On March 27, 2018, amid other recent scandals, the National Fair Housing Alliance and three other organizations filed a lawsuit against Facebook alleging that Facebook’s advertising platform enables landlords and real estate brokers to discriminate against several classes of people, preventing them from fairly receiving relevant housing ads. The outcome, and potential cost, is yet unknown.
We also do not know yet whether this lawsuit stems from deep issues with data bias, poor (or unethical) business decisions, or both. But organizations looking to increase data literacy across their staff and make data-driven business decisions must raise awareness of data bias and its costs.
What data bias looks like
You may think that information collected by sensors and applications must be free from data bias by definition—after all, billions of data points are being collected in a neutral way. But that isn’t necessarily true. Consider what happened when the city of Boston released a smartphone app to help locate and fix potholes. As reported, there was a hidden bias in the data. Penetration of smartphone usage among the elderly and low-income populations was only around 16 percent, significantly skewing reports of issues away from those areas and residents who needed services most. Cases such as tweets during Hurricane Sandy or Google Flu Trends are further examples of how data bias can negatively impact public services.
Consequences of biased data
Discriminatory practices are one consequence of biased data. This may occur in cases such as skewing who sees job ads, as a recent study demonstrated. Google’s facial recognition software has also been highlighted as producing significant racial bias. As in the case of the lawsuit against Facebook, the consequences of biased data can be more serious than making the wrong decision or allocating resources improperly: You may be violating the law without knowing it. Sprint and Time Warner have both incurred multi-million-dollar fines for such issues from the FTC.
A brief look at different types of bias
There are too many types of data bias to list here. However, a couple of the most common types of bias are useful to highlight.
Selection bias: In this case, you are working with a subset of the data instead of a valid sample across the whole population. A data set that only includes male customers in Boston, for example, will not provide insight into the population of potential customers across New England. Similarly, using only tweets to assess a product’s success will not give you insight into the broader population that uses the product but doesn’t tweet.
Cause-effect bias: We’re trained to look for correlations and patterns. However, these embedded data relationships may not only be superfluous, but may skew subsequent analysis. I heard a good example at a conference last year relating to data on the Titanic. In that case, the data had highly correlated columns connecting those who survived to those who got onto lifeboats. But neither variable gives any insight into better factors of what cause may have improved chance of surviving. Customer data that we regularly use has many such correlated pieces, such as city and postal code.
How can you address data bias?
You need to understand and communicate to your teams what bias is and how it may impact their work with data. Not only is it a foundational part of implementing a data literacy strategy, and critical to effective and ethical data-driven business decisions, but not understanding it could mean serious consequences for your business. Part of this strategy is to teach people working with data how to use data profiling, data preparation, or BI tools to identify potential areas of bias. For instance:
When profiling data, think about “completeness,” not just in terms of whether a field is populated, but whether the data set is complete relative to the target population.
When evaluating a set of codes (age, gender) or dates, look for unexpected skews to the information out of line with broad sources such as census or demographic data.
If assessing associations between multiple fields, look for pieces of data representing a similar concept (city and state vs. postal code), or that highlight highly correlated variables.
Asking questions about gaps in data collection is key. It often helps to understand who gathered the data and what their goals were, particularly if it’s third-party data.
These are core data-quality practices that need to be incorporated when helping establish data literacy in your organization. Further, you want to incorporate basic scientific methods. Ensure your teams understand the different types of bias and watch for bias in the questions asked or targeted goals. Alternate hypotheses need to be raised, evaluated, and presented in testing algorithms and reviewing analysis before leaping to specific decisions.
Protecting your company from data bias
Biased data and decisions represent significant risks to your organization both monetarily and ethically and ultimately may impact your ability to achieve revenue goals and maintain brand reputation. To prevent biased decisions, you must understand what your business goals are and be able to review the data in use and test different hypotheses. The results need to be incorporated into a review process that assesses biases, risks, and ethical considerations.
This is not something to delegate to an overworked governance or risk and compliance team, but needs to be embedded into, communicated through, and practiced throughout your organizational culture. Incorporating such an approach, and asking these types of questions, may well make the difference in preventing damage to your company—whether through fines, reputation management, or both.
In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.
To be successful, organizations need to become more human than ever.
Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.
You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.
AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.
The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.
AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.
Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.
In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.
This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.
Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.
Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.
This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.
That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).
The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.
For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.
But we need to start.
From Self-Help to Self-Skills
Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.
Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.
These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:
Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.
The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.
The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.
Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.
One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.
Being More Human Is Hard
However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.
Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).
Human Skills 101
As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.
Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.
Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse
Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion
Problem solving: Finding solutions to problems and understanding the steps used to solve the problem
Decision-making: Weighing the evidence and options at hand to determine a specific course of action
Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future
Coordination: Working with individuals and other groups to achieve common goals
Social perceptiveness: Inferring what others are thinking by observing them
Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity
Creativity: Generating ideas, concepts, or inferences that can be used to create new things
Curiosity: Desiring to learn and understand new or unfamiliar concepts
Imagination: Conceiving and thinking about new ideas, concepts, or images
Storytelling: Building narratives and concepts out of both new and existing ideas
Experimentation: Trying out new ideas, theories, and activities
Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness
Empathy: Identifying and understanding the emotional states of others
Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project
Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal
Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.
Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.
Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.
In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.
Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.
Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.
How Knowing One’s Self Helps the Organization
Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.
Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.
It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.
There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.
Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.
Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.
For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.
Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.
Human Skills Are the Constant
Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.
The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.
Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.
While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!
About the Authors
Jenny Dearborn is Chief Learning Officer at SAP.
David Judge is Vice President, SAP Leonardo, at SAP.
Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.
Neal Ungerleider is a Los Angeles-based technology journalist and consultant.
The word “analytics” means different things to different people. Depending on the analytical maturity of your organization, analytics could mean reports on your performance, analytics could mean predictive models, or it could mean fully optimized analytic decisions.
No matter where you are on that spectrum, many organizations report that while they have many different analytical systems or models, they don’t know how well they are performing. Many times organizations implement expert or predictive models with the expectation of enhanced operational performance, but they don’t measure the results, and don’t assess if the model is delivering the business value needed and expected.
Measuring and tuning models as is important as implementing models. Without ongoing monitoring they can fail to achieve the desired results. If you, as a leader of an organization wants to assess your analytics, there are a number of steps you can take.
1) Take an Inventory of your Analytics
One of the first challenges may be identifying all of the analytics in place within your organization, or even within a single department. Often there is no well documented inventory of models. You may find that some of the analytics that are used aren’t even well understood. You may also find that some analytics that you thought were in use are no longer being executed.
As part of this effort, document what each model was intended to do, if it is still being used, who is using it, if its performance is being measured, and what its current effectiveness is.
2) Measure analytical accuracy
Even when an organization has a good inventory of their models, most don’t have an updated measurement of the effectiveness for each of their analytical tools. For example, if a model is supposed to predict whether a customer will self-resolve an issue, an effectiveness score could show that the model made the correct prediction 89% of the time. Depending on the model, that may show that the model is at peak condition, or could show that it has degraded from its expected performance.
Traditional models were built at a point in time based on available data. They were static, meaning they did not change, and in fact degraded over time as the underlying business conditions changed. Often models would only be re-calibrated infrequently, and in the interim they became less and less effective. In contrast, newer models utilize machine learning, where the model utilizes its own results and self-calibrates based on those results to become more accurate over time.
If an organization has models that are more than a few years old and hasn’t measured their current accuracy, it is highly likely that their performance has degraded and they could increase their performance through a tuning process, or the model could be improved by upgrading to one which is self-calibrated using machine learning.
3) Are your models being used as intended?
Models are predictors of future events. However, if that future event does not match the business process you are executing, the model is unlikely to achieve the designed result.
One good example is when an organization uses a credit score to prioritize their delinquent collection cases. A credit score is built to predict which people are most likely to re-pay a specific grant of credit (credit card, loan, auto, etc.). A credit score is not desired to predict which of a specific subset of individuals who later become delinquent, were most likely to re-pay their debt. While it likely would be more predictive than a coin flip, it is unlikely to produce the results that a purpose-built model could achieve. Similarly, a collections re-payment model built for one client, or one debt type may not achieve its same level of precision with a different debt pool.
Another example shows where a model could lead to less than expected results. Let’s say an organization has a model that was built to predict if a brand new collection case will result in a payment in full within 60 days. Operationally, if the case management system only holds a low risk case for only 30 days, the model won’t achieve maximum effectiveness. The model will be identifying cases where they will pay during days 31-60 (in addition to days 1-30), but those cases aren’t being given time to resolve themselves. Either the model needs to be adjusted to predict payment within 30 days, or the case management system needs to be adjusted to hold onto low risk cases for 60 days. If the two are not in sync, the model predictably under-performs.
Conclusion
Many organizations that have made significant investments in analytics view that investment as one-time projects. However, analytics need continual monitoring and tuning. If your organization hasn’t reviewed your analytics recently, then a small project to review your analytics, assess their current accuracy and usage will help you assure you are achieving your goals, and point to areas for improvement.
Where your analytics have degraded over time, you can tune your models to improve their performance, or your can upgrade your models to ones which use machine learning. These models utilize the performance of the model to update itself to maximize effectiveness over time. The following graphic shows the FICO approach to continual learning in analytic management.
Amazon’s rise to power is no secret – but recently, Amazon has proven that its influence goes beyond just the B2C world. Through Amazon Business, the company has successfully entered the B2B marketplace. This move is having a profound impact on manufacturers, distributors and retailers alike.
In a recent webinar “From B2B to B2C: Amazon’s Entry into the B2B World,” NetSuite hosted Colin Puckett, Amazon Business’s Head of Seller Marketing, along with distribution industry leader Scott Costa, publisher of tED Magazine and ecommerce expert Brian Beck, SVP, Ecommerce and Omnichannel Strategy at Guidance, a retail services business for a discussion on Amazon Business. Here are the key takeaways.
Three Approaches to Amazon Business
The panelists categorized manufacturers, distributors and retailers into three groups as it relates to Amazon Business:
Those who are willing to partner with Amazon Business to expand their reach into new markets or acquire new customers using Amazon’s established channels.
Those who view Amazon Business as a direct competitor – this group tends to lag behind when it comes to an ecommerce strategy – either they do not have their own website, or their online experience is difficult to navigate.
Those who recognize the impact of Amazon Business and are choosing to compete by establishing their own website and competitive online buying experience.
Rather than taking a hard stance on whether to partner with Amazon Business, Scott and Brian agreed that Amazon Business’ biggest impact is in driving the need for an ecommerce strategy. The rise of Amazon in both the B2C and B2B space shows that buyer preferences and habits have shifted. Consider the startling stat Beck shared – 50 percent of product searches now start on Amazon. Now more than ever it is important to recognize the importance of establishing and executing on an ecommerce strategy. Refusing to do so means falling behind in a dynamic world.
Strategically Partner with Amazon Based on Your Business Model
For those who are considering, or choose to partner with Amazon Business, it is important to recognize the differences in partnership options available.
Vendor Central/ 1st Party/ 1P: Amazon sells your products on a wholesale relationship. You send your inventory to Amazon, they control your pricing and your listing displays as “Ships from and sold by Amazon.com”.
Seller Central/ 3rd Party/ 3P: You sell your products on Amazon’s marketplace.
With this, you either have Amazon fulfill your orders from their fulfillment centers (Fulfillment by Amazon, FBA) or you can fulfill orders from your own warehouse or 3rd party warehouses (Fulfillment by Merchant, FBM).
Each option comes with pros and cons and will ultimately depend on the level of control and responsibility management is looking for. Many companies choose to partner with an agency to help them identify the best Amazon strategy, as well as to directly manage their Amazon partnership.
Amazon has shown that it is not shying away from competing in the B2B marketplace. As a business, it is up to you to determine whether partnering with Amazon Business is the right strategy for your organization. No matter your decision, it is important to recognize the influence Amazon Business is having on the B2B industry – and make the necessary adjustments to your business model now.
Access the “From B2C to B2B: Amazon’s Entry into the B2B World” webinar recording here.
Posted on Tue, May 22, 2018
by NetSuite filed under
I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon
One of the four pillars at PowerObjects is Education. We know business results depend on rapid adoption and effective usage of Microsoft Dynamics 365, which is why we have such a strong focus on training teams to support the technology long term. Our Dynamics 365 University offerings focuses on out-of-the-box features and functionality training to help bring success to your system implementation. When you choose education at PowerObjects, you have a choice to attend a regularly scheduled offering at any of our global training centers, or we can bring a trainer on site to a location of your choice. Watch this D365 In Focus video to learn more about PowerObjects’ Dynamics 365 University!
In this exclusive interview, Lee discusses the importance of high-quality data to CRM success.
ProsperWorks CEO Jon Lee
CRM Buyer: What are some of the current trends you see in the CRM space?
Jon Lee: You’re seeing the trend of AI and machine learning. There’s this notion that software is primarily something you have to work for. There’s a lot of data entry, a lot of navigating screens and moving between different windows.
Software as it was designed in the 1980s was a database for recording information. The trend you’re seeing today is that instead of working for my software, what if my software works for me? You see companies trying to use machine learning and AI to record calls and help them sell. Rather than being just a recording device, it’s now a coaching device. That can include recommendations about what to do next, and what to do on your sales calls.
The key is, does the existing software have really good data? AI and machine learning rely on lots of different types of data. That’s the future — software that’s able to take all your data and ultimately provide you with a recommendation for what you should do next, what you should focus on, how you should conduct your sales calls.
The other theme we’re seeing is that CRM is becoming increasingly more important. What’s happened on the macro level is you’re seeing the relationship actually change between the buyer and the seller.
You largely relied on the sales rep before, but now there’s so much information online, and you build a relationship with the community, and the community can tell you want you should buy and purchase. What’s happening is that the original sales person is being intermediated by technology. Companies are moving away from middle people and going direct. We’re seeing it with Tesla, which doesn’t use third-party dealerships.
We live in the relationship era, where human beings still are influenced by a connection with other human beings. It’s more important than ever to differentiate yourself in the market by building a relationship with the customer. Being able to provide a better relationship through personalization is key.
CRM can now help you customize that customer relationship, which can build loyalty and increase sales. CRM as a concept is now bleeding into the tools that we use on a daily basis. The concept of staying within one CRM tab is going away. It will ultimately become transparent and work with the tools you already use. Wherever you communicate with your customer, you’ll have context-rich information.
CRM Buyer: Why is it important that CRM is easy-to-use?
Lee: If you want to get value out of your CRM, it requires great data and the ability to extract that data. If it’s easy to use, it’s easy to create records and find information. When things take a lot of time, you lose productivity, and you’re less likely to do it because the friction is really high.
If you don’t have the data, CRM becomes useless. The purpose of CRM is data and automation — being able to establish a repeatable sales process based on data, so you can make decisions about who’s your target customer, who’s your best-performing salesperson.
All those decisions are based on data. If it’s not easy to use, people won’t use it, and if people don’t use it, you’re not going to be able to get the data.
CRM Buyer: Do businesses of any size need CRM? Why?
Lee: I think all businesses can benefit. The value of CRM is organization, and having a single source of truth. If you’re a productive person with a lot of contacts, you’re going to have to track all of that.
Once you get larger, you have a team, and you have to provide a platform for them to collaborate. As you get larger, you really need to see the larger picture. As you get larger, every CEO loses sleep over numbers and hitting revenue targets.
CRM can help you better understand your customers, so you can market to them better. The utility goes up substantially for larger businesses, but there is real utility for smaller business.
CRM Buyer: How is the CRM industry evolving and changing? What’s in the future?
Lee: You’ll see the future is AI. It really is. It’s being able to make recommendations about what you do next. CRM’s been a backwards-looking tool, since hasn’t been telling you what you need to do. That’s part of the problem, and that’s why people often don’t use it. It’s difficult to use, clunky, and requires data entry. Does it actually help you sell more? Sort of, but not really.
Technology that lets you analyze data and recommend data-driven decisions on the fly before you walk into a meeting, and after you step out of a meeting: That’s the future.
Vivian Wagner has been an ECT News Network reporter since 2008. Her main areas of focus are technology, business, CRM, e-commerce, privacy, security, arts, culture and diversity. She has extensive experience reporting on business and technology for a variety
of outlets, including The Atlantic, The Establishment and O, The Oprah Magazine. She holds a PhD in English with a specialty in modern American literature and culture. She received a first-place feature reporting award from the Ohio Society of Professional Journalists. Email Vivian.
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