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5 Ways B2C Companies Can Leverage Email for Better Results

February 8, 2020 CRM News and Info

4) New and Updated Product Launch Emails

Good businesses are constantly releasing new products, services, and features. Great businesses are able to disseminate that information to their customer base in fun and innovative ways that lead to better engagement and more sales opportunities.

Whenever your company releases a new product or updates an existing product, you should include a strong email marketing campaign as part of your holistic go-to-market strategy. Think about it: your existing customers are already invested in some aspect of your company and offerings, so there’s a very good chance that they’d also be interested in what’s coming next!

New and updated product launch emails are designed to do more than generate sales; when done properly, they help foster connection to your entire brand. You’re advertising your bright, shiny, and new offering, so you want to create a real sense of enthusiasm that you can transfer to your existing customers. This means using big, sharp images and clever, concise copy. Most importantly, you want to make sure your branding elements jump off the page while also tying seamlessly to your new product.

Use the subject line and preview text to let your audience know that they’re about to view or learn about something new and exciting, being as explicit as possible with the limited characters at your disposal. Include a prominent CTA that clearly states the desired action, and make it easy for your customers to access the new or updated product by redirecting them to a clean and simple landing page where they can request a free trial or purchase the new item outright.

5) Use Email to Gather Customer Feedback

Successful B2C companies are constantly innovating and improving upon their product and service lines. And one way they can do that is by understanding what is and isn’t working. Sending emails prompting customers to take a survey or leave product feedback can help your company gather the data you need to develop and deliver the best possible offerings and experiences.

Obviously, you want to make sure you’re sending survey emails to customers who have actually purchased something from you in the past. So, a common best practice is to send these emails as part of a trigger campaign following a sale or interaction with customer support. These emails empower your customers to voice their opinion of your products and services, which helps to increase customer satisfaction and loyalty. Further, it allows you to understand where you’re succeeding and where you might have missed the mark, so you can continue to improve your offerings with each new iteration.

In addition to surveys, which usually limit the interaction to objective yes/no or multiple-choice questions, you should encourage your customers to give more detailed feedback by inviting them to leave a review or testimonial. You can leverage great reviews by sharing them on your website, product pages, or social media platforms. Or, you could even follow up with the customer directly to see if they’d like to participate in a more involved case study or success story.

Even negative reviews can be extremely helpful as they give you the chance to improve your product and reach out to the reviewer directly to help make the situation work to your advantage. In some cases, these reviews (and your responses) can help your other customers solve their problem, which could save you from additional negative reviews in the future. For example, a customer trying to decide if they want to buy a pair of jeans might notice a negative review suggesting to size up, which would prevent them from making the same mistake.

If you aren’t getting much traction with your customer feedback emails, consider adding additional value to incentivize your customers to participate. This might include a standard discount with a simple user code or maybe a free trial to your next product release. Make sure the offer is included in the subject line to inspire your audience to click through to the email.

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Meet the new twist on data encryption that promises better privacy and security for AI

January 16, 2020 Big Data

Presented by Intel

AI and privacy needn’t be mutually exclusive. After a decade in the labs, homomorphic encryption (HE) is emerging as a top way to help protect data privacy in machine learning (ML) and cloud computing. It’s a timely breakthrough: Data from ML is doubling yearly. At the same time, concern about related data privacy and security is growing among industry, professionals and the public.

“It doesn’t have to be a zero-sum game,” says Casimir Wierzynski, senior director, office of the CTO, AI Products Group at Intel. HE allows AI computation on encrypted data, enabling data scientists and researchers to gain valuable insights without decrypting the underlying data or models. This is particularly important for sensitive medical, financial, and customer data.

Meet the new twist on data encryption that promises better privacy and security for AI

Above: Casimir Wierzynski, Senior Director, Office of the CTO, AI Products Group at Intel

Wierzynski leads Intel’s privacy preserving machine learning efforts, including HE work and in developing industry standards for the technology’s use. In this interview, he talks about why HE is needed, its place as a building block of improved privacy, and how the breakthrough technology helps create new business opportunities and data bridges to previously “untrusted partners.”

What and why

Q: What’s does “homomorphic” mean?

In Greek, homo is the same, morphic is the shape. So it captures the idea that if you do encryption in the right way, you can transform ordinary numbers into encrypted numbers, then do the same computations you would do with regular numbers. Whatever you do in this encrypted domain has the same shape as what you’re doing in the regular domain. When you bring your results back, you decrypt back to ordinary numbers, and you’ll get the answer you wanted.

Q: What big problem is the technology is solving?

We live in amazing times, when we have all these new AI products and services, like being able to unlock your phone with your face, or systems that enable radiologists to detect diseases at earlier stages. These products rely on machine learning systems, which are fed and shaped by data that are very sensitive and personal. It’s important for us as an industry — and I would argue as a society — to figure out how we can keep unlocking all the power of AI while still helping protect the privacy of the underlying data. That’s the overarching problem.

Q: Why is HE creating buzz now?

The technique itself has been around for more than 20 years as a theoretical construct. The criticism has been, okay, you can operate on encrypted data, but it takes you a million times longer than using regular data. It was an academic curiosity. But in the last five years, and especially the last two years, there have been huge advances in the performance of these techniques. We’re not talking about a factor of a million anymore. It’s more like a factor of 10 to 100.

Q: Certainly, there’s no shortage of data privacy and protection technologies…

We’ve all gotten good about encrypting data at rest, when it’s on our hard drives, or sending data back and forth over encrypted channels. But when you’re dealing with ML and AI, at some point those data have to be operated on. You need to do some math on those data. And to do that you need to decrypt them. While you’re using the data, the data are decrypted, and that creates a potential issue. We work to provide better protection on both these fronts, anonymization and encryption.

Above: Simple model of secure inference with HE.

Q: In ML with multiple partners, trust is a big issue. How does HE help here?

Whenever you’re dealing with digital assets, you have this phenomenon: When you share a digital asset with another party, it’s completely equivalent to giving it to them, then trusting they’re not going to do something unintended with it.

Now add the fact that ML is fundamentally an operation that involves multiple stakeholders. For example, one entity owns the training data. Another entity owns data they want to do some inference on. They want to use ML service provided by a third entity. Further, they to use an ML model owned by another party. And they want to run all this on infrastructure from some supply chain.

With all these different parties, and because of the nature of digital data, all must trust each other in a complex way. This is becoming harder and harder to manage.

HE in action

Q: Can you give an example of homomorphic encryption at work?

Say you have a hospital doing a scan on a patient, working with a remote radiology service. The hospital encrypts the scan and sends it over to the radiologist. The radiologist does all the processing in the encrypted domain, so they never see the underlying data. The answer comes back, which is also encrypted, and finally the hospital decrypts it to learn the diagnosis.

Q: In the example above, where does HE actually happen? At the scanner?

It could live in multiple places. There are two major components: encryption and decryption. But then there’s also the actual processing of the encrypted data. Encryption typically happens where the sensitive data are first captured, for example, in a camera or edge device. Processing encrypted data happens wherever the AI system needs to operate on sensitive data, typically in a data center. And finally, decryption happens only at the point where you need to reveal the results to a trusted party.

Powerful with other emerging techs

Q: When you speak and write on HE, you also talk about adjacent technologies. Please explain briefly the roles these other building blocks play in preserving privacy.

There are several very promising and rapidly developing technologies that use tricks from cryptography and statistics to do operations on data that seem magical.

All these technologies can be further accelerated and enhanced by hardware security measures called trusted execution environments, such as Intel SGX.

New bridges to partners in AI and ML

Q: So what opportunities are created when these technologies are combined?

One of the questions we’ve been asking at Intel is, what would happen if you could enable ML in these multi-stakeholder operations between parties that don’t necessarily trust each other, like we described earlier? What would that enable?

You could have banks that normally are rivals. But they may decide it’s in their interest to cooperate around certain risks that they all face in common, like fraud and money laundering. They could pool their data to jointly build anti-money laundering models while keeping their sensitive customer data private.

Another example is in the retail sector. Retailers want to make the most out of the data that they’ve collected on shoppers, so they can personalize certain experiences. What if there were a way to enable that and still provide quantifiable protections around the privacy of that data?

New business opportunities

Q: Are there new revenue models and opportunities being created?

One thing that’s exciting about this area is that you start to enable new business models around data that would have previously been impossible. For example, (HE) can be a way to monetize data while helping maintain security. At the same time, it addresses one of the biggest problems in ML, namely, access to large, diverse data sets for building models. So you can imagine a whole ecosystem that brings together people who hold data with people who need data. And very importantly, it’s all done in a way that preserves security and privacy of the data. That’s an exciting possibility.

Q: How advanced is that concept in implementation? Any other or many real-life instances like that?

I would say it’s beyond theory, and in early stages of commercial deployment.

Data is becoming a much bigger asset class than ever before, sometimes in surprising ways. For example, in corporate bankruptcy cases, creditors can end up owning large data sets unrelated to banking in any way. They’re just leftovers of a loan that went sour. So they’re looking for a way to monetize these assets and provide them to data scientists who seek additional training data to make their AI systems more reliable, all while keeping the underlying data private and secure.

Or imagine a bunch of hospitals that have patient data. For various reasons, they can’t share it. But they know if they could, and could get lawyers in the room to hammer out an agreement, they would potentially be able to jointly build a model with much more statistical power than one any could build individually. Using privacy-preserving ML techniques, they can essentially form a consortium and say: “In exchange for all of us owning this improved model that will improve our patients’ outcomes, we’ll be a part of this consortium, and we can still keep all of our patient data private and secure.”

Key role of developers

Q: Where do developers fit in?

As it is now, if you’re an AI data scientist and you want to build a machine learning model that operates on encrypted data, you have to be some magical person, simultaneously an expert in software engineering and data science and post-quantum cryptography.

One of the major efforts that my team has been working on is making these technologies much more accessible and performant for the data science and developer communities so that they can scale up. This is a priority for Intel. Today we’re offering tools like The Intel HE transformer for nGraph, which is a Homomorphic Encryption (HE) backend to Intel’s graph compiler for Artificial Neural Networks.

Above: The nGraph-HE software stack supports the SEAL encryption library and underlying cryptosystems.

Standards and Intel

Q: Some people will ask: Why is Intel active here? The answer is…

For starters, homomorphic encryption is very compute-intensive. This is an area where Intel can really shine in terms of building optimized silicon to handle this fundamentally new way of computing.

But more broadly, those examples from health care, retail, and finance — sectors representing about a quarter of GDP in the United States. These are very economically important problems. At Intel, we are obsessed with helping customers solve their problems around data. And privacy is at the heart of any data-centric business.

We are in a unique position, because Intel works closely with hyperscale, data-centric users of hardware who are building all kinds of exciting AI applications. At the same time, we are a neutral party with respect to data. To make these technologies perform at scale is going to require the kinds of complex software and hardware co-design that Intel is uniquely positioned to provide. We get to collaborate actively with a fascinating range of players across industry.

Above: The 2019 HomoEncyption.org standards group meeting gathered top private and public sector participants from around the globe in Santa Clara, CA.

Q: Intel has taken a leadership role in developing HE standards. Why are they important? Status?

As with any crypto scheme, people will use it when there’s interoperability and trust in the underlying math and technology. We recognize that to really scale up, to bring all the homomorphic encryption goodness to the world, we need to have standards around it.

As interest in privacy preserving methods for machine learning grows, it’s essential for standards to be debated and agreed upon by the community, spanning business, government, and academia. So in August, Intel co-hosted an industry gathering of individuals from Microsoft, IBM, Google, and 15 other companies interested in this space.

We’ve identified many points of broad agreement. Now we’re trying to figure out the right approach to bring it to standards bodies, like ISO, IEEE, ITU, and others. They all have different structures and timelines. We’re strategizing on how best to move that forward.

Final word

Q: Any thoughts you’d like to leave ringing in people’s ears?

Privacy and security technologies for machine learning like homomorphic encryption are ready for prime time. These are not academic exercises anymore. These are real market-ready ideas. The day is coming when the idea of doing machine learning on someone else’s raw data will seem quite strange. We’re at the start of a new and exciting era where machine learning will enable us to explore new opportunities unlike anything before.

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AI Weekly: Autonomous cars need better safety metrics to move the industry forward

January 10, 2020 Big Data

On Monday, Waymo — the subsidiary of Google parent company Alphabet that’s developing a full-stack driverless vehicle platform — announced that its cars have driven a combined 20 million autonomous miles to date, up from 10 million miles in October 2018. The metric signifies Waymo’s logistical and technological superiority, implied CEO John Krafcik, who equated the miles driven to 1,400 years of driving experience for an average American.

But some experts assert that measuring driverless systems’ progress by miles is a flawed approach.

This week in a conversation with VentureBeat at the 2020 Consumer Electronics Show (CES), Dmitry Polishchuk, the head of Russian tech giant Yandex’s autonomous car project, said that miles aren’t very meaningful without context to accompany them. “It’s tough to directly compare miles driven,” he said. “Obviously, the more miles [you] have, the better, but we believe that the environments that you’re in have a huge impact.”

Yandex isn’t without a horse in the race — its over 100 autonomous cars in Innopolis and Skolkovo, Russia; Las Vegas; and Tel Aviv have driven 1.75 million miles as of January, up from 1.5 million miles and 1 million miles last December and October, respectively. But policymakers as well as competitors in the nearly $ 41.25 billion global autonomous car segment have expressed similar sentiments.

Noah Zych, head of system safety at Uber’s Advanced Technologies Group, told Wired in an interview that mileage critically omits details like situations encountered, obstacles, and accidents. “You need to know … ‘What was the objective of the testing in [any given area]?” he said. “Was it to collect data? Was it to prove that the system was able to handle those scenarios? Or was it to just run a number up?”

And at a conference organized by Nvidia in Washington two years ago, Derek Kan, U.S. secretary for policy at the U.S. Department of Transportation, stressed the need for objective and agreed-upon measures of driverless systems performance. Separately, David Friedman, former acting administrator of the National Highway Traffic Safety Administration (NHTSA) and vice president at Consumer Reports, recently urged Congress to direct the NHTSA to implement privacy protections, minimum performance standards, and accessibility rules for self-driving cars, trucks, SUVs, and crossovers.

Disengagements — or deactivations of cars’ autonomous modes when failures occur or when drivers are forced to take over — have been adopted by agencies including California’s Department of Motor Vehicles as an alternative to miles driven. (By law, companies actively testing self-driving cars on public roads in the state are required to publish disengagement reports.) But Polishchuk argues that this, too, is an imperfect metric.

“We have kind of been waiting for some sort of industry standard,” he said, noting that Yandex hasn’t yet released a disengagement report. “Self-driving companies aren’t following the exact same protocols for things. [For example, there might be a] disengagement because there’s something blocking the right lane or a car in the right lane, and [the safety driver realizes] as a human that [this object or car] isn’t going to move.”

For its part, whenever Yandex deploys new code into production, the company conducts real-world tests to ensure that systems performance (and by extension, safety) isn’t degraded. It takes 10 cars — five equipped with the codebase from half a year ago and five with the latest code — and it runs them for a day on the same route such that they encounter identical obstacles and weather conditions. It even switches up the safety drivers behind the wheel to prevent bias from influencing the results.

“We look back at the numbers and check the correlation … using hundreds of different parameters,” said Polishchuk. “The absolute number of disengagements doesn’t matter.”

Unfortunately for companies like Yandex, less regulatory guidance — not more — seems the likelier near-future path, at least in the U.S. At CES on Wednesday, Transportation Secretary Elaine Chao announced Automated Vehicles 4.0 (AV 4.0), new guidelines regarding self-driving cars that seek to promote “voluntary consensus standards” among autonomous vehicle developers. It requests but doesn’t mandate regular assessments on self-driving vehicle safety, and it permits those assessments to be completed by automakers themselves as opposed to by a standards body.

Advocacy groups including the Advocates for Highway and Auto Safety criticized the policy for its vagueness. “Without strong leadership and regulations … [autonomous vehicle] manufacturers can and will continue to introduce extremely complex supercomputers-on-wheels onto public roads … with meager government oversight,” said president Cathy Chase in a satatement. “Voluntary guidelines are completely unenforceable, will not result in adequate performance standards, and fall well short of the safeguards that are necessary to protect the public.”

Indeed, regulation could go a long way to convincing a skeptical public.

Two studies — one published by the Brookings Institution and another by the Advocates for Highway and Auto Safety (AHAS) — found that a majority of Americans aren’t convinced of driverless cars’ safety. More than 60% of respondents to the Brookings poll said that they weren’t inclined to ride in self-driving cars, and almost 70% of those surveyed by the AHAS expressed concerns about sharing the road with them. Elsewhere, a study conducted by think tank HNTB found that 59% of people expect self-driving cars will be “no safer” than cars driven by humans.

In the U.S., legislation remains stalled at the federal level, unfortunately. More than a year ago, the House unanimously passed the SELF DRIVE Act, which would create a regulatory framework for autonomous vehicles. But it has yet to be taken up by the Senate, which in 2018 tabled a separate bill, the AV START Act, that made its way through committee in November 2017.

Polishchuk predicts that legislation will only emerge when some “reasonable amount” of self-driving cars hit public roads. Optimistic projections peg the number at 10 million by 2030. “When this happens, we would have statistics, and basically, statistics will push regulators,” he said.

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers and AI editor Seth Colaner — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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Big Data – VentureBeat

Coat Giveaway Becomes a Powerhouse Nonprofit Making Life Better for Thousands of Economically Disadvantaged Students

January 8, 2020 NetSuite

Posted by Morgan St. Clair, Social Impact Communications Manager

Clearly, Rose Hanley was onto something when, after handing out coats at an inner city elementary school in St. Louis, Mo., in 2001, she decided she’d found her mission.

In fact, she’d found even more than that: She’d discovered the seed for a trailblazing nonprofit, The Little Bit Foundation, that would grow to provide support in the form of everything from clothing and dental hygiene kits to books, STEM curriculum and career discovery for thousands of economically disadvantaged students.

But as the foundation gradually expanded its reach over its first 15 years, complexity caught up with it. With only QuickBooks to run an operation that was supplying students at more than 20 St. Louis schools, things grew chaotic.

Despite having $ 250,000 worth of inventory consisting of 400 different SKUs, the nonprofit had no inventory management capabilities, and orders were being managed by hand. The result was a lot of inaccuracy.

“We’d run out of stuff and for us that’s an issue,” said Tim Hydar, director of distribution. “We don’t want to tell a student we’re going to have a coat and then next week, when we come, we don’t have that coat.”

Add with managing more than 100 volunteers, it was clear the foundation needed a more sophisticated technology platform, one that would allow it to scale the operation by providing abundant access to data and powerful reporting capabilities.

Eventually, The Little Bit Foundation chose to deploy NetSuite after engaging with the Oracle NetSuite Social Impact team. During the fall of 2016, the foundation managed the implementation itself, although it has turned to the Suite Pro Bono and Suite Capacity teams for help solving specific problems.

Opening the Floodgates

The move to NetSuite has opened things up in a big way. In the three years since, the foundation has expanded to 42 schools, now counts 250 weekly volunteers, and is able to seamlessly process 250 orders each day. Hydar believes the nonprofit could handle 1,000 daily orders.

Operationally, the foundation is able to ensure that schools are more prepared than ever by verifying every item on every order, and providing school administrators with lists of what to expect for each student.

To accommodate the scale NetSuite helps make possible, the foundation moved out of the 3,500-square-foot donated space it occupied and into a 33,000-square-foot building it purchased. That has enabled it to expand its inventory to more than $ 1 million worth of goods representing 900 SKUs. And those goods are more varied than ever. The foundation has added a junior category for girls, and begun distributing dental and feminine hygiene kits.

The larger facility has also made it possible to create pick aisles, which has helped shrink order assembly from an average of eight minutes per order to just two minutes. Meanwhile, the foundation received two donated handheld scanners from the Schnuck’s grocery chain, and it’s deploying NetSuite’s Warehouse Management System Lite, which will enable it to take full advantage of those scanners for more efficiency.

The foundation also has been able to easily support two different distribution models. For short-term needs, the foundation establishes a stocked boutique at each of its partner schools, which students can visit if they need clothes that day, whether it’s because their clothes are tattered or they have an accident at school. For meeting longer-term needs, the foundation places orders for new items that are delivered the following week by volunteers.

The foundation’s expanded reporting capabilities are enabling it to provide schools with detailed summaries of its activities. It tracks all programming, distribution of goods, and the growing list of services it delivers, which now includes medical screenings, eye exams, and hygiene instruction. (The foundation even places washers and dryers in each school so that students and parents can do laundry free of charge, using donated detergent, which is also tracked.)

The foundation also has begun establishing mentorship programs, such as the one it’s formed with Riverview Gardens School District in which it tracks each student’s history, year by year, and provides guidance to help high school students transition into the world.

“That is just an example of how a district said, ‘Here’s an issue of ours. Our students aren’t ready. When they graduate, they don’t know what to do next,’” said Hydar. “And so we came up with a program.”

The foundation also uses NetSuite to perform voluntary audits, which it uses to demonstrate its financial and operational health to potential partners and donors.

Diving Deeper

Next summer, the nonprofit plans to revisit its CRM situation. It currently uses an application called DonorPerfect to manage information on donors and donated items, but it’s not integrated with NetSuite. Hydar said the plan may be to engineer a full integration, or simply standardize on NetSuite CRM.

In the meantime, as The Little Bit Foundation expands its NetSuite use, it constantly discovers opportunities to realize additional benefits. And while it managed the initial deployment itself, Hydar said the help the foundation has received from NetSuite and one of its partners Allied Cloud Solutions, has been invaluable as it seeks to add advanced modules and create complex reports.

“We’re able to ask NetSuite Pro Bono team and the Suite Capacity people, ‘Hey, this is a specific donor we need to report for. These are the criteria we need. Can you help us create this?’ And by the end of the Pro Bono project, we have that,” said Hydar.

“Without the partnership that NetSuite has given us, we would not have had all the success we’ve had.”

Posted on Tue, January 7, 2020
by NetSuite filed under

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Trump as underpants gnome is no better in the German: “Trumpregierungsschlamasselschmerz.”

January 1, 2020 Humor

The NewYorker’s Susan Glasser finds a single word to describe the Trump era that really isn’t about underpants gnomes, even if it applies to most Trumpian policies, such as they are. The short version is Trumpschmerz.

There must be one of those long German words for all that soul-sickening worry, right? Some tortured mouthful of consonants that captures the ceaseless anxiety and absurdity of Washington in the age of Trump? I asked my friend, the German scholar and writer Constanze Stelzenmüller, an astute observer of Trumpism at the Brookings Institution and especially of its toxic effect on the troubled transatlantic relationship. She said that, even in Trump-skeptical Berlin, there was no single, widely accepted word that describes this phenomenon but gamely offered up her own stab at it. The word she came up with is “Trumpregierungsschlamasselschmerz.”

My German is nonexistent, but a quick Internet search suggests that Constanze nailed it. In thirty-three letters, she managed to capture the whole damn mess. Her word has pretty much everything that has come to characterize this uniquely dysfunctional moment in America’s troubled capital: Trump and his Administration (“regierung” means government); the mess of constant controversies (“schlamassel”); and the continuous pain or ache of the soul that results from excessive contemplation of it all (“schmerz”). Sure, it’s a mouthful, but that’s the point: there should be one word that sums up the Trumpian disruption we are experiencing, not merely a jumble of different ones. It’s the tweets and the other stuff, too: the endless attacks on enemies, real and imagined; the torrent of lies; the eroding of the basic functions of government; and the formerly unimaginable assault on our institutions. It’s impeachment and the Mueller Report and migrant children in cages, the bullying of allies, and the lavish praise of adversaries. It’s the uncertainty and worry that comes with all of the above.

www.newyorker.com/…

Disinformation campaigns continue the mess, as Russia tries to rewrite history in the US and in Poland.

The Soviet Union signed a non-aggression pact with Nazi Germany on 23 August 1939. In addition to stipulations of non-aggression, the treaty included a secret protocol that divided territories of Romania, Poland, Lithuania, Latvia, Estonia, and Finland into German and Soviet Union “spheres of influence”, anticipating potential “territorial and political rearrangements” of these countries.

en.wikipedia.org/…

Both Poland and Russia had accused each other for their historical revisionism. Russia has repeatedly accused Poland for not honoring Soviet soldiers fallen in World War II for Poland, notably in 2017, in which Poland was thought on “attempting to impose its own version of history” after Moscow was not allowed to join an international effort to renovate a World War II museum in Poland^[29] and destroyed monument honoring Soviet soldiers fallen in the war.^[30] Meanwhile, Poland also accuses Russia for its unlimited historical distortion, notably back to 2014 when Putin signed a bill using any comparison of Nazi to Soviet crimes as a punishment, as the Poles were also treated brutally by the Soviets; although Russia’s historical revisionism might have influenced Poland’s Andrzej Duda over its Nazi war crime laws;^[31] and Poland also has concerned that Russia’s political and historical revisionism might put Poland at risk.^[32]

en.wikipedia.org/…–Russia_relations

x

Whenever something is solidly ridiculed, as Putin’s New Russia speech on April 17, 2014, it dies quietly. Putin has presumably some direct aggressive idea with this aggressive speech, as Munich February 2017 led to war in Georgia in August 2018. Get prepared!

— Anders ÃÂslund (@anders_aslund) December 29, 2019

Trump is pointing the US into a possible mess at the waning of neoliberalism with increasingly limited democratic choices.

Neoliberal policies created gaping inequality, unleashing the economically powerful to reshape politics, markets, and society to serve their own interests. Neoliberalism’s radical individualism sapped society of community and solidarity, leaving people lonely and isolated, ultimately pushing us to retreat into tribal identities.

The central question of our time is what comes after neoliberalism. New political paradigms emerge in response to the challenges and failures of the preceding era, and today, four possibilities for the future are emerging.

The first possibility is reformed neoliberalism. It preserves the old ideology’s individualism and cosmopolitan sensibilities and keeps the basic structures of neoliberal capitalism in place, while reversing the worst extremes of its upwardly redistributionist economics. Some in this camp have a nostalgic wish to get things back to “normal,” though they recognize that incremental reforms are essential. Others, like those who see the Universal Basic Income as a paradigm for the future, want to correct the dislocations that neoliberal policies created—but they are hesitant to attack the root causes of inequality head-on. The real danger of this path is that it threatens more of the same: persistent disaffection, further erosions of trust and social solidarity, and demagogues waiting in the wings.

The second possibility is nationalist populism, which combines ethnic, religious, or cultural nationalism with economic populism. This approach, most associated with Steve Bannon, may be viable as a campaigning strategy, but it seems unlikely as a governing strategy, as political and economic elites oppose both the economic and social tenets of the framework. Indeed, candidate Trump campaigned in 2016 on this agenda only to abandon it as president. Because it is unlikely as a governing strategy, it is unlikely to define the next era of politics.

The third possibility, which many refer to as authoritarianism, has gotten the most attention. Scholars and commentators have argued that there is a global rise in autocracy. Political insurgents around the world are channeling popular unrest to win surprising victories. Strongman regimes are breaking constitutional constraints and norms. Meanwhile, constitutional democracies are on the ropes.

[…]

The final possibility is that a new era of democracy will follow the age of neoliberalism. Just as it is a mistake to reduce nationalist oligarchy to authoritarian politics, it is wrong to think that preserving elections, voting, the free press, and constitutional norms are sufficient for democracy. Democracy has always demanded much more of societies and individuals.

www.thenation.com/…

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Free vs. Paid CRM Solutions: Which Is Better for Your Organization?

December 19, 2019 Microsoft Dynamics CRM

Thinking of acquiring or changing your customer relationship management solution? The sheer number of CRM solutions available can be overwhelming, and it may be tempting to ignore paid products in favour of the free options. But if it sounds too good to be true, it probably is. Here are some of the factors you should consider before selecting a CRM solution for your organization, whether free or paid.

Not so free after all

Most free CRM solutions offer basic functionality, allowing you to manage marketing, sales, and customer service. This may be all your organization needs for the moment, but as more advanced features are usually paid, keep in mind that you will likely have to pay for additional functionality, such as marketing automation or advanced data analysis. As your business grows and your needs evolve, you may find paying just as much in the long run, if not more.

Before implementing any new solution, it’s important to analyze your current needs, and also to have a long-term vision of your business. Think about how quickly you may need these additional features, and consider how pushing back their implementation at a later time may end up causing more disruption in terms of change management, configuration time, and data migration.

Think long term

For this reason, settling for a free option may not be the smarter move in the long run, as you may find yourself chafing under the constraints and limited functionality sooner rather than later. Even paid CRM solutions are flexible and versatile, and offer different pricing plans to meet various needs and budgets. An implementation specialist can help you figure out the different pricing structures and select the best option to meet your current and long-term needs.

Moreover, the process of selecting and implementing a new solution is a substantial investment of both time and money, not to mention the change management and disruptions of normal operations result from the implementation. It may be better in the long run to adopt the solution that best meets your organization’s needs right off the bat rather than settling for the cheapest option and having to replace it later.

Look for expert guidance

Free CRM solutions are also designed to respond the needs of as many businesses as possible. If you have any specific needs, it may be difficult to find the features you are looking for, not to mention that you won’t have the guidance of a professional implementation partner. This is why we recommend looking for a CRM partner in your industry, who will be familiar with your processes and realities, and make recommendations based on your long-term vision, budget and current environment.

A wide variety of CRM solutions such as Microsoft Dynamics 365 Customer Engagement are available, each offering different functionalities and price points to meet the needs of various businesses. While these may not be free, you may also be able to start off with a customer relationship management solution that is less expensive than you might think.

By JOVACO Solutions, Microsoft Dynamics 365 implementation specialist in Quebec

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Better Data Requests = Better Data Results

December 1, 2019 Sisense

Building a Data-Driven Future

It’s important for everyone at a company to have the data they need to make decisions. However, if they just work with their data team to retrieve specific metrics, they are missing out. Data teams can provide a lot more insights at a faster rate, but you will need to know how to work with them to make sure that everyone is set up for success.

Data teams can be thought of as experts at finding answers in data, but it’s important to understand how they do that. In order to get the most value out of your collaboration, you need to help them understand the questions that matter to you and your team and why those questions need to be answered. There are a lot of assumptions that get built into any analysis, so the more the data team knows about what you are looking for, the more knowledge they may find as they explore data to produce their analysis. Here are four tips to make more productive requests from members of your data team:

sisense blog Reducing Insight Erosion in Collaborative Data Analysis 20191105 banner 770x250 Better Data Requests = Better Data Results

Approach data with an open mind

It’s important to treat the data request process as an open-ended investigation, not a way to find data that proves a point. A lot of unexpected insights can be found along the way. Make your goal to ask questions and let your data team search for the answers. This approach will allow you to get the best insights, the type of unknowns that could change your decision for the better. If you put limitations on what you’re asking the data, you’ll end up putting limitations on the insights you can get out of your inquiry.

To really dig into this, think about how questions are answered scientifically. Scientists treat any bias as an opportunity for the insight to be compromised. For example, let’s say you are looking to improve customer satisfaction with your product. Requesting a list of customers with the highest and lowest NPS scores will give you a list of people who are happiest or most frustrated, but it is not going to let you know how to improve customer satisfaction. This request puts too much attention on the outliers in your customer base rather than identifying the key pain points. That’s part of the picture, but not all of it. If you’re trying to create a program that targets your goal, let your data team know the goal, give them a few optional starting points, and see what they come back with. They might surprise you with some sophisticated analysis that provides more insight and helps you launch a fantastic program.

Start with a conversation, not a checklist

The single biggest mistake a line-of-business professional can make when requesting data is to present a data expert with a list of KPIs and tell the data team to just fill in the blanks. This approach misses so much of the value a data team can provide. Modern data teams have technology and abilities that allow them to go much further than just calculating numbers. They can guide analytical exploration through flexible, powerful tools to make sure you’re getting the most valuable insights out of your data.

Instead of a list of metrics, think about starting your data request as a meeting. You can provide the business context needed and a list of questions that you want answered. You can even present some initial hypotheses about what those numbers may look like and why they might move in one direction or another. This is a great way to kick off the conversation with your data counterpart. From there, you can benefit from their experience with data to start generating new and more informed questions from their initial inquiries. The data team’s job is to get you information that helps you be more informed, so give them as much context as possible and let them work as a problem solver to find data-driven recommendations.

Data should recommend actions, not just build KPIs reports

A lot of standard business KPIs measure the results of company efforts: revenue, lead conversion, user count, NPS, etc. These are important statistics to measure, but the people tracking them should be very clear that these numbers track how the company is moving, not why it is moving that way. To make these data points actionable, you need to take analysis further. Knowing that your NPS is going up or down is useless if it doesn’t inform a customer team about the next step to take.

A good data team will map important KPIs to other data and find connections. They’ll comb through information to find the levers that are impacting those important KPIs the most, then make recommendations about how to achieve your goals. When you get a list of levers, make sure to understand the assumptions behind the recommendations and then take the right actions. You can always go back to those KPI reports to test if the levers are having the intended effect.

Data requests are iterative, give the data person feedback

Communication about data should not end when the data has been delivered to you. It’s important to dig into the analysis and see what you can learn. Instead of reporting that data or taking action on it right away, you should check with your dashboard creator to make sure that he or she can verify that you’re reading all of the data properly and that the next steps are clear. There are a lot of ways to misinterpret data; a good way to prevent mistakes is to continue communicating.

Even if you’ve gotten the right takeaways from the data, it’s still good to consult with your dashboard creator and go over your interpretation of the information so they know how you read data. You may need a follow-up meeting to restart with the overall question you want to answer, then see what additional data needs to be collected or what modifications are needed to make the report or dashboard work best for your intended use-case.

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new poll: 53% of Republicans think Trump is a better POTUS than Abraham Lincoln

November 30, 2019 Humor

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Trump probably made him wear it.

Yes, that is a Space Force hat.

— Tom Brenner (@tombrennerphoto) November 29, 2019

From new YouGov poll….

A Majority (53%) of Republicans think Donald Trump was a better President than Abraham Lincoln. pic.twitter.com/CrsiYeLUdJ

— Jesse Ferguson (@JesseFFerguson) November 29, 2019

new poll: 53% of Republicans think Trump is a better POTUS than Abraham Lincoln

Twitter has banned the Republican candidate seeking to unseat Rep. Ilhan Omar after she made making multiple posts about killing the congresswoman, HuffPost reports. https://t.co/Ztl3mABqvg

Ã¢ÂÂ Kyle Griffin (@kylegriffin1) November 29, 2019

HereÃ¢ÂÂs a pic I was sent of boxes of Ã¢ÂÂTriggeredÃ¢ÂÂ sitting in the offices of Turning Point USA, a student group with close Trumpworld ties. pic.twitter.com/7Isaj3YYcM

Ã¢ÂÂ Nick Confessore (@nickconfessore) November 28, 2019

moranbetterDemocrats

Good, better, best for sales quotes in Dynamics 365

October 2, 2019 Microsoft Dynamics CRM

There’s more than one way to create a sales quote in Dynamics:

Good: You can make one with Word or Excel.
Better: You can use some built-in tools to create a PDF quote (but it’s still basically built on Word templates).
Best: You can add configure, price, quote (CPQ) software to Dynamics.

Why is adding CPQ best? Three reasons: CPQ creates 1) a simplified process to create quotes that 2) have optimized product and pricing configurations and also 3) look great (making a better impression on prospects and a better case for your business).

Simplifying sales proposals in Dynamics

First, I must admit that I’m biased. I work for an ISV (iQuoteXpress) that creates a single sign-on solution for CPQ integrated with Dynamics 365. And the fact is that what comes standard in Dynamics for creating proposals may be enough for individual sales reps with the time and talent to create professional-looking, accurate sales quotes.

Most of us, though, simply don’t have time (let alone the expertise) to quickly and easily make sales proposals that stand apart from the crowd.

But CPQ simplifies that process, even for someone who has never made a sales quote before. You simply select a proposal template (not a Word template, but a sales proposal template with preconfigured fields, engaging imagery, branding, vertically targeted info, etc.), point-and-click to add products/services, and send. Simple.

Optimized products & pricing

I said “point-and-click to add products” and that’s what’s at the heart of the best CPQ systems: a product and pricing configuration engine that’s centrally administered to ensure reps are always and only selling your best products at the best possible pricing.

A sales manager can create and control bundles of products and services, pricing, upsells, and upgrades, and can also manage discount structures — and customize all of the preceding for each individual rep.

And a rep never has to think about what the best pricing is or how low they can go with discounts or other incentives, because products and discounts are all essentially hard-coded into the CPQ system (as far as a rep is concerned, anyway). A sales manager controls the pricing in the system, a sales manager controls what’s shown in the system, and a rep just clicks the products they want to add to their proposal. An optimal solution for all!

Looking good, closing often

Appearances matter. You wouldn’t pitch a prospect on a new Dynamics implementation while wearing shorts and a t-shirt, right? So why would you send that same prospect a sales proposal that looks like it was made in 1984 on a Commodore 64?

Any CPQ system worth its salt comes preloaded with professionally designed sales proposal templates that capture attention, speak to vertical markets, and build a brand.

But I recommend a small investment in a professional designer. For a few hundred dollars (or less) you can populate the CPQ solution integrated into your Dynamics system with sales proposal templates that were professionally designed for you.

Imagine a proposal that looks less like a Word template and more like an advertisement for your company. More importantly, put yourself in your prospect’s shoes and imagine a stack of sales proposals in front of you: which are you going to read first? The black and white one that looks like a spreadsheet, or the full-color proposal that looks like a work of art (ok, slight exaggeration: that looks like a glossy, customer-friendly brochure)?

Send your best, always.

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University Book Store Goes Back to School, Upgrading Ecommerce, POS, Back-Office Solutions to Better Serve UW Students and Community

September 26, 2019 NetSuite

180824 1719%20(1) University Book Store Goes Back to School, Upgrading Ecommerce, POS, Back Office Solutions to Better Serve UW Students and Community

Posted by Ian McCue, Content Manager

As the name indicates, University Book Store is a campus bookstore, but it’s much more than that. The independent bookstore for the University of Washington sells not only the standard lineup of textbooks, school supplies and Husky apparel, but also trade books that would be found at any local bookstore, non-school-themed apparel, technology and cosmetics.

University Book Store serves not just students, faculty and staff, but the local community as well, with five stores sprinkled throughout Seattle and the Puget Sound area. Its flagship store is located just off-campus in Seattle’s U District. That 100,000-square-foot store on “The Ave” accounts for about 85% of the retailer’s sales.

“I’d say we’re more like a Macy’s or Nordstrom than we are a Barnes & Noble, just because we have so many different departments,” University Book Store CIO Erin Olinick said. “We’re really known as being the one-stop campus shop.”

The Book Store sells textbooks for not only the University of Washington’s main campus but UW’s Tacoma and Bothell locations, two other nearby colleges and 14 private high schools.

Struggling with Omnichannel Demands

While University Book Store’s business model is unique, it faces the same challenges as just about any retailer. Its customers’ expectations are higher than ever before as they demand a combination of convenience, speed and highly competitive prices. Meeting those expectations is imperative as the Book Store faces intense competition from Amazon, book publishers selling direct to students and other online shops.

University Book Store did its best to adapt to a rapidly changing retail landscape, but a legacy RATEX solution was an unavoidable roadblock. The 35-year-old system, built on Linux and locally hosted, managed accounting, point-of-sale and inventory. Employees had to decipher the RATEX system’s black-and-white, MS DOS-like user interface (you’re forgiven if you’re unfamiliar with all of that; suffice to say, the technology was archaic). Only one employee knew how to code the system and became the gatekeeper for any software modifications.

Additionally, University Book Store’s homegrown ecommerce solution involved a complex layering of servers that received point-of-sale and inventory information through an API that updated every 24 hours. The result was often inaccurate inventory data that frustrated shoppers and staff.

“The system was absolutely impeding our ability to move forward and remain relevant in a really quickly moving and competitive industry,” Olinick said.

Moving Beyond Industry Solutions

After the Book Store determined it needed to overhaul its technology stack, it first turned to long-standing industry solutions from MBS Textbook Exchange, Nebraska Book Co. and Visual RATEX (a newer version of its current system).

It was not until Olinick’s team heard about The Duck Store at the University of Oregon using NetSuite that it considered the cloud-based, unified business management platform. University Book Store eventually chose NetSuite over Visual RATEX because it could provide a unified source of data from across the company that could help break down departmental siloes and improve communication.

“We really felt that NetSuite had the potential to push us to evolve and think in a much broader way,” Olinick said. “It really made us uncomfortable in a lot of ways, but that was something that we actually embraced, because we firmly believed that NetSuite would finally allow us to compete with other retailers, and our competition is not other bookstores, it’s other retailers.”

The versatile solution could support every aspect of the business, including ecommerce, point-of-sale, email marketing and back-end operations like financials, inventory management, order management and fulfillment.

Creating a Better Customer Experience

When customers visit the retailer’s online store, they can now add textbooks, clothing and headphones, for example, to a single cart instead of checking out multiple times for different departments. That streamlined process has helped increase order size and allows the company to cross-sell by taking advantage of product recommendations.

Today, shoppers can pick up their online orders in any University Book Store location same-day or pay for express shipping and preorder products before the store even receives them. Students can also sign up for a textbook rental account on the ecommerce site.

In-store, students can pay with their school-issued Husky Card. If a textbook is out-of-stock, the store can offer students an e-book version immediately instead of potentially losing the sale like in the past. About 12% of the retailer’s course materials revenue now comes from digital products like e-books and access codes, up from 3% with the old system.

Internally, NetSuite ERP provides a single source of data with automated reports on every aspect of the business, a vast improvement over manual reports in spreadsheets that required data entry. For example, it’s now easy for the Book Store to see the profitability of items or track expenses by location or department instead of trying to dig up high-level information in a massive spreadsheet. On the vendor side, it can view credit balances by supplier and outstanding checks to better manage cash flow. Those insights help the retailer make informed decisions about pricing and promotions.

Access to more detailed customer data, including where they shop and how much they spend, allows the store to send more personalized emails to various customer segments through the Bronto marketing platform. That’s led to a big bump in the ROI of University Book Store’s email campaigns.

Additionally, the Book Store can look at how many customers are purchasing digital copies of textbooks to reduce procurement of physical copies. That has lowered both shipping and handling costs and inventory carrying expenses.

But those quantitative benefits are second to the increased collaboration and employee empowerment provided by a system that everyone can access.

“My favorite thing about NetSuite is that my staff has really kind of become their own business owners, job owners,” said Matt Schleede, course materials manager. “They all have responsibilities on a daily, weekly, monthly basis and they’ve been able to run their own reports and kind of relearn how to do their jobs in a way that makes themselves more efficient. When they’re working to their best of their abilities … it makes the business more efficient.”

University Book Store continues to recognize new gains as it takes advantage of more aspects of the platform’s capabilities. It sees this as only the beginning of a long journey.

The nearly 120-year-old company is ready for the future and the ever-changing expectations of its customers with a solution that has the flexibility and backing to evolve with it.

“It’s pretty amazing to know our systems and processes are not stuck in one place anymore,” Olinick said. “The collaborative environment we now have with staff asking each other questions is helping us to crack open the areas of our business that we didn’t have visibility into before, because things were so automated and so hidden in our old system that we weren’t able to make changes to them.”

Learn more about NetSuite’s software for campus bookstores.

Posted on Wed, September 25, 2019
by NetSuite filed under

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Free vs. Paid CRM Solutions: Which Is Better for Your Organization?

Better Data Requests = Better Data Results

Building a Data-Driven Future

Approach data with an open mind

Start with a conversation, not a checklist

Data should recommend actions, not just build KPIs reports

Data requests are iterative, give the data person feedback

new poll: 53% of Republicans think Trump is a better POTUS than Abraham Lincoln

Good, better, best for sales quotes in Dynamics 365

Simplifying sales proposals in Dynamics

Optimized products & pricing

Looking good, closing often

University Book Store Goes Back to School, Upgrading Ecommerce, POS, Back-Office Solutions to Better Serve UW Students and Community

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