Tag Archives: Value

Modeling Deposit Price Elasticity: Where’s the Value?

Deposit Price Elasticity Modeling FICO Modeling Deposit Price Elasticity: Where’s the Value?

The ability to model deposit price elasticity is becoming a core component of deposit portfolio management. In my previous posts on this topic, I discussed:

This post focuses on benefits once models have been completed and are in use. What should you expect to gain from deposit price elasticity models and what can you do with them to maximize benefit to the business?

The main function of a deposit pricing team is to forecast the future performance of their portfolio and a substantial amount of time is spent answering questions such as: How much new money will this rate bring in? How will this cannibalize my more profitable back book? What is the impact of the new rate on my portfolio P&L? What if the market changes rates?

Scientifically derived deposit price elasticity models streamline the answering of these and other questions. Moreover, as the core of a deposit forecasting solution, these models improve the accuracy, efficiency and accountability of the entire pricing process.


Price elasticity models are not affected by qualitative bias and provide a level of accuracy not achievable by gut instinct alone. Rather than focusing on individual products, the modeling suite should have the ability to create simulations of the entire portfolio, incorporating balance movements into and out of the bank as well as the effect price changes have on other products and the resulting impact on the bottom line. Simulations of future behavior should have the ability to predict the impact of price changes and allow the user to flex assumptions around competitor pricing, changes to the macroeconomic environment and internal profit assumptions.

Using this approach we achieve a better understanding of customer behaviors and the associated sensitivities of the bank’s liquidity as a whole. The best system of models tracks the impact of price changes so that previous decisions can be reviewed, appraised and the results fed back into the model calibration. This closed-loop process ensures models are continuously learning and adapting to changing market sentiment.


Another significant benefit of pricing analytics is that they accelerate the decision making process. Pricing managers can rapidly generate a number of different scenarios to study alternative pricing strategies, changes in competitor pricing assumptions or wider market factors. Providing the business with appropriate forecasting levers allows them to focus their expertise on pricing. Generation of evidence to justify pricing decisions becomes an automated process and this makes it possible to quickly iterate towards a pricing strategy that achieves the desired portfolio outcome.


When recommendations are based on transparent model drivers, conversations with internal stakeholders or senior management become easier as forecasted balances can be directly related back to internal or external modeling factors. The demonstrable action-effect behavior of pricing models also extends beyond the organization as they facilitate conversations with regulators.

In recent times, the regulatory burden on bank executives has grown such that transparency of the underlying pricing models is paramount. Pricing decisions must be explainable and the inputs and assumptions that sit behind them thoroughly documented. An ideal price-elasticity solution provides transparency to the underlying models, automates much of this governance process and provides an auditable structure for the entire pricing process.

Towards Optimization & Beyond

As I have discussed, price elasticity models that predict flows into, out of and between products are key to gaining a full understanding of a deposits portfolio. They serve as part of a broader analytic infrastructure underpinned by a strong data management system and a highly skilled analytic workforce. Ultimately, they empower the pricing team to make better decisions that are more accurate, quickly identified, and easily explained. These improved, data-driven processes have myriad benefits for everyone from the pricing analyst through senior management, partner stakeholders such as treasury, finance, marketing, and even external auditors.

However, the full value of a deposits portfolio can only be completely unlocked through the application of optimization, which is the final frontier in the construction of a comprehensive pricing solution. Full price optimization has the ability to discover revenue where a simple forecasting tool cannot. It optimally trades off balances and revenue across every product in the portfolio and finds the most efficient path to achieving the desired business outcome.

In my next post, I’ll discuss price optimization in more detail and how it can directly generate value to the deposits business.

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Get our latest eBook – Mainframe Challenge: Unlocking the Value of Legacy Data

If your organization is running legacy systems, you’re likely struggling to fully integrate your legacy data into new data analytics platforms and your business intelligence deliverables. That siloed data can also compromise data governance and security.

In our latest eBook, Mainframe Challenge: Unlocking the Value of Legacy Data, we review ways to help you tackle these obstacles so you can unlock the value of your mainframe data.

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Download the eBook to learn more.

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How to Use C# 7 Value Tuples in ASP.NET MVC

With the release of Visual Studio 2017, I wanted to use the new Value Tuples that were introduced in C# 7 in a MVC project I was working on. However, I ran into some issues which I discuss below.

Firstly, Value Tuples are included with .Net 4.7, but if the project’s target framework is lower than that, they can still be used by installing the System.ValueTuple nuget package.

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After either using .Net 4.7 or installing the nuget package, when attempting to use Value Tuples you may find that the project fails to build, with no errors in the error log, and errors in the output log like error CS1026: ) expected. This is due to the Microsoft.Net.Compiliers nuget package that is included by default when creating a new MVC project is version 1.3.2. This needs to be updated to at least version 2.0.1.

There is also an error that occurs when using the Value Tuples in Razor views:

The type ‘ValueType’ is defined in an assembly that is not referenced. You must add a reference to assembly ‘System.Runtime, Version=, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a’.
This can be resolved by adding a reference to System.Runtime in the compilation section of the web.config e.g:



However even after fixing this error, the use of Value Tuples in Razor views is still partially limited. Trying to directly set the model of a view to be a Value Tuple or using the new instantiation syntax (e.g. (Name: “John Smith”, Age: 25)) in a Razor view gives a “Feature ‘tuples’ is not available in C# 6.  Please use language version 7 or greater” error.

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However, if the tuple is a property of the model, that it can be used without errors.

For an example of how the Value Tuples can be used I have created a small test application. That allows a user to update their current Business Unit.

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In this method we are first deconstructing a Value Tuple that has been returned by the GetLoggedInUser method, this allows us to use the results as normal variables since in this case we only need the name. We are also retrieving the users current Business Unit and a List of all Business Units that are returned as Value Tuples.

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Here you can see that we can still use the named parameters of the Value Tuples in the view, including the ones in a List. This is much nicer then the Item1… syntax of the regular tuples.

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Magnetism Solutions Dynamics CRM Blog

Leading with Customer Experience, Value, Technology, and Credibility

rsz bigstock golden trophy cup on table 155670149 1 Leading with Customer Experience, Value, Technology, and Credibility

When I was an industry analyst, I always felt that the most enlightening and valuable research came from first hand, end user feedback. Who better to hear from than practicing professionals doing real world work? Pleasing customers with enterprise software isn’t for the faint of heart and if you really want unvarnished insights, end users should be the core of your critical feedback loop.

It’s for this reason the team at TIBCO is so excited to see the results of The Wisdom of Crowds® Business Intelligence and Enterprise Planning Market Studies delivered by Dresner Advisory Services, LLC. This research speaks directly to the end user community on a wide variety of categories to unearth a complete view of market realities, plans, and perceptions from users in all roles and across industries.

This month Dresner Advisory Services announced its 2017 Industry Excellence Awards based on high vendor ratings in their most recent research. TIBCO Spotfire achieved awards as a Customer Experience Leader and a Value Leader, while TIBCO Statistica received the Technology Leader and Credibility Leader awards. Both solutions were acknowledged for their overall strength in sales, support, consulting services, and more. Vendors who are awarded Customer Experience and Technology leaders are executing at a high level for sales and service, as well as product and technology. Credibility and Value leaders have customers who reflect a high level of confidence and sense of value for the price paid.

Dresner’s Wisdom of Crowds research started in 2010 and dives deep when appraising vendors performance by tracking 33 different criteria across 7 topic areas that include, Sales Experience, Value for Price Paid, Technology/Product, Technical Support, Consulting Services, Customer Recommendation, and Vendor Integrity.

The Wisdom of Crowds research examines the details of our industry and surfaces positive trends that point to great progress for Business Intelligence and Analytic consumers. When reviewing the the Value dimension of the Dresner report a positive trend emerges: Since 2012, respondents to the survey are scoring the vendors with progressively higher value scores year over year. Keeping up with this competitive landscape puts pressure on solution providers, making it harder to compete and in the case of Spotfire even more satisfying to be among the leaders in this area.

Dresner tracks 12 different criteria to score product quality and usefulness, which includes robustness/sophistication of technology, completeness of functionality, reliability of technology, scalability, integration of components within product, integration with third-party technologies, overall usability, ease of installation, ease of administration, customization and extensibility, online training, forums and documentation, and ease of upgrades and migration to new versions. All saw increases in 2017 except ease of upgrades again. This trend points to increased competition and maturity in the market, making it more difficult to rise to leadership positions in the research.

The vendor credibility model employed by Dresner combines the value for price paid as scored by the user along with a vendor’s integrity score (honesty and truthfulness in all dealings) and recommendation score (customers willingness to recommend the vendor) to create an overall confidence dimension. The value and confidence dimensions position where a vendor is placed in the overall rankings. TIBCO Statistica placement among credibility leaders is an award to be proud of considering the competition and the scoring criteria.

The 2017 Industry Excellence Awards speak highly of TIBCO’s analytic strategy and our Connected Intelligence approach to digital transformation. To differentiate and maintain competitive advantage, smart companies should rely on solutions that lead in Customer Experience, Value, Technology, and Credibility.

Read more about the 2017 Excellence Awards here.

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The TIBCO Blog

Are Chemical Companies Focusing Digital Initiatives On The Highest Value Opportunities?

When it comes to buying things—even big-ticket items—the way we make decisions makes no sense. One person makes an impulsive offer on a house because of the way the light comes in through the kitchen windows. Another gleefully drives a high-end sports car off the lot even though it will probably never approach the limits it was designed to push.

We can (and usually do) rationalize these decisions after the fact by talking about needing more closet space or wanting to out-accelerate an 18-wheeler as we merge onto the highway, but years of study have arrived at a clear conclusion:

When it comes to the customer experience, human beings are fundamentally irrational.

In the brick-and-mortar past, companies could leverage that irrationality in time-tested ways. They relied heavily on physical context, such as an inviting retail space, to make products and services as psychologically appealing as possible. They used well-trained salespeople and employees to maximize positive interactions and rescue negative ones. They carefully sequenced customer experiences, such as having a captain’s dinner on the final night of a cruise, to play on our hard-wired craving to end experiences on a high note.

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Today, though, customer interactions are increasingly moving online. Fortune reports that on 2016’s Black Friday, the day after Thanksgiving that is so crucial to holiday retail results, 108.5 million Americans shopped online, while only 99.1 million visited brick-and-mortar stores. The 9.4% gap between the two was a dramatic change from just one year prior, when on- and offline Black Friday shopping were more or less equal.

When people browse in a store for a few minutes, an astute salesperson can read the telltale signs that they’re losing interest and heading for the exit. The salesperson can then intervene, answering questions and closing the sale.

Replicating that in a digital environment isn’t as easy, however. Despite all the investments companies have made to counteract e-shopping cart abandonment, they lack the data that would let them anticipate when a shopper is on the verge of opting out of a transaction, and the actions they take to lure someone back afterwards can easily come across as less helpful than intrusive.

In a digital environment, companies need to figure out how to use Big Data analysis and digital design to compensate for the absence of persuasive human communication and physical sights, sounds, and sensations. What’s more, a 2014 Gartner survey found that 89% of marketers expected customer experience to be their primary differentiator by 2016, and we’re already well into 2017.

As transactions continue to shift toward the digital and omnichannel, companies need to figure out new ways to gently push customers along the customer journey—and to do so without frustrating, offending, or otherwise alienating them.

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The quest to understand online customers better in order to influence them more effectively is built on a decades-old foundation: behavioral psychology, the study of the connections between what people believe and what they actually do. All of marketing and advertising is based on changing people’s thoughts in order to influence their actions. However, it wasn’t until 2001 that a now-famous article in the Harvard Business Review formally introduced the idea of applying behavioral psychology to customer service in particular.

The article’s authors, Richard B. Chase and Sriram Dasu, respectively a professor and assistant professor at the University of Southern California’s Marshall School of Business, describe how companies could apply fundamental tenets of behavioral psychology research to “optimize those extraordinarily important moments when the company touches its customers—for better and for worse.” Their five main points were simple but have proven effective across multiple industries:

  1. Finish strong. People evaluate experiences after the fact based on their high points and their endings, so the way a transaction ends is more important than how it begins.
  2. Front-load the negatives. To ensure a strong positive finish, get bad experiences out of the way early.
  3. Spread out the positives. Break up the pleasurable experiences into segments so they seem to last longer.
  4. Provide choices. People don’t like to be shoved toward an outcome; they prefer to feel in control. Giving them options within the boundaries of your ability to deliver builds their commitment.
  5. Be consistent. People like routine and predictability.

For example, McKinsey cites a major health insurance company that experimented with this framework in 2009 as part of its health management program. A test group of patients received regular coaching phone calls from nurses to help them meet health goals.

The front-loaded negative was inherent: the patients knew they had health problems that needed ongoing intervention, such as weight control or consistent use of medication. Nurses called each patient on a frequent, regular schedule to check their progress (consistency and spread-out positives), suggested next steps to keep them on track (choices), and cheered on their improvements (a strong finish).

McKinsey reports the patients in the test group were more satisfied with the health management program by seven percentage points, more satisfied with the insurance company by eight percentage points, and more likely to say the program motivated them to change their behavior by five percentage points.

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The nurses who worked with the test group also reported increased job satisfaction. And these improvements all appeared in the first two weeks of the pilot program, without significantly affecting the company’s costs or tweaking key metrics, like the number and length of the calls.

Indeed, an ongoing body of research shows that positive reinforcements and indirect suggestions influence our decisions better and more subtly than blatant demands. This concept hit popular culture in 2008 with the bestselling book Nudge.

Written by University of Chicago economics professor Richard H. Thaler and Harvard Law School professor Cass R. Sunstein, Nudge first explains this principle, then explores it as a way to help people make decisions in their best interests, such as encouraging people to eat healthier by displaying fruits and vegetables at eye level or combatting credit card debt by placing a prominent notice on every credit card statement informing cardholders how much more they’ll spend over a year if they make only the minimum payment.

Whether they’re altruistic or commercial, nudges work because our decision-making is irrational in a predictable way. The question is how to apply that awareness to the digital economy.

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In its early days, digital marketing assumed that online shopping would be purely rational, a tool that customers would use to help them zero in on the best product at the best price. The assumption was logical, but customer behavior remained irrational.

Our society is overloaded with information and short on time, says Brad Berens, Senior Fellow at the Center for the Digital Future at the University of Southern California, Annenberg, so it’s no surprise that the speed of the digital economy exacerbates our desire to make a fast decision rather than a perfect one, as well as increasing our tendency to make choices based on impulse rather than logic.

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Buyers want what they want, but they don’t necessarily understand or care why they want it. They just want to get it and move on, with minimal friction, to the next thing. “Most of our decisions aren’t very important, and we only have so much time to interrogate and analyze them,” Berens points out.

But limited time and mental capacity for decision-making is only half the issue. The other half is that while our brains are both logical and emotional, the emotional side—also known as the limbic system or, more casually, the primitive lizard brain—is far older and more developed. It’s strong enough to override logic and drive our decisions, leaving rational thought to, well, rationalize our choices after the fact.

This is as true in the B2B realm as it is for consumers. The business purchasing process, governed as it is by requests for proposals, structured procurement processes, and permission gating, is designed to ensure that the people with spending authority make the most sensible deals possible. However, research shows that even in this supposedly rational process, the relationship with the seller is still more influential than product quality in driving customer commitment and loyalty.

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Baba Shiv, a professor of marketing at Stanford University’s Graduate School of Business, studies how the emotional brain shapes decisions and experiences. In a popular TED Talk, he says that people in the process of making decisions fall into one of two mindsets: Type 1, which is stressed and wants to feel comforted and safe, and Type 2, which is bored or eager and wants to explore and take action.

People can move between these two mindsets, he says, but in both cases, the emotional brain is in control. Influencing it means first delivering a message that soothes or motivates, depending on the mindset the person happens to be in at the moment and only then presenting the logical argument to help rationalize the action.

In the digital economy, working with those tendencies means designing digital experiences with the full awareness that people will not evaluate them objectively, says Ravi Dhar, director of the Center for Customer Insights at the Yale School of Management. Since any experience’s greatest subjective impact in retrospect depends on what happens at the beginning, the end, and the peaks in between, companies need to design digital experiences to optimize those moments—to rationally design experiences for limited rationality.

This often involves making multiple small changes in the way options are presented well before the final nudge into making a purchase. A paper that Dhar co-authored for McKinsey offers the example of a media company that puts most of its content behind a paywall but offers free access to a limited number of articles a month as an incentive to drive subscriptions.

Many nonsubscribers reached their limit of free articles in the morning, but they were least likely to respond to a subscription offer generated by the paywall at that hour, because they were reading just before rushing out the door for the day. When the company delayed offers until later in the day, when readers were less distracted, successful subscription conversions increased.

Pre-selecting default options for necessary choices is another way companies can design digital experiences to follow customers’ preference for the path of least resistance. “We know from a decade of research that…defaults are a de facto nudge,” Dhar says.

For example, many online retailers set a default shipping option because customers have to choose a way to receive their packages and are more likely to passively allow the default option than actively choose another one. Similarly, he says, customers are more likely to enroll in a program when the default choice is set to accept it rather than to opt out.

Another intriguing possibility lies in the way customers react differently to on-screen information based on how that information is presented. Even minor tweaks can have a disproportionate impact on the choices people make, as explained in depth by University of California, Los Angeles, behavioral economist Shlomo Benartzi in his 2015 book, The Smarter Screen.

A few of the conclusions Benartzi reached: items at the center of a laptop screen draw more attention than those at the edges. Those on the upper left of a screen split into quadrants attract more attention than those on the lower left. And intriguingly, demographics are important variables.

Benartzi cites research showing that people over 40 prefer more visually complicated, text-heavy screens than younger people, who are drawn to saturated colors and large images. Women like screens that use a lot of different colors, including pastels, while men prefer primary colors on a grey or white background. People in Malaysia like lots of color; people in Germany don’t.

This suggests companies need to design their online experiences very differently for middle-aged women than they do for teenage boys. And, as Benartzi writes, “it’s easy to imagine a future in which each Internet user has his or her own ‘aesthetic algorithm,’ customizing the appearance of every site they see.”

Applying behavioral psychology to the digital experience in more sophisticated ways will require additional formal research into recommendation algorithms, predictions, and other applications of customer data science, says Jim Guszcza, PhD, chief U.S. data scientist for Deloitte Consulting.

In fact, given customers’ tendency to make the fastest decisions, Guszcza believes that in some cases, companies may want to consider making choice environments more difficult to navigate— a process he calls “disfluencing”—in high-stakes situations, like making an important medical decision or an irreversible big-ticket purchase. Choosing a harder-to-read font and a layout that requires more time to navigate forces customers to work harder to process the information, sending a subtle signal that it deserves their close attention.

That said, a company can’t apply behavioral psychology to deliver a digital experience if customers don’t engage with its site or mobile app in the first place. Addressing this often means making the process as convenient as possible, itself a behavioral nudge.

A digital solution that’s easy to use and search, offers a variety of choices pre-screened for relevance, and provides a friction-free transaction process is the equivalent of putting a product at eye level—and that applies far beyond retail. Consider the Global Entry program, which streamlines border crossings into the U.S. for pre-approved international travelers. Members can skip long passport control lines in favor of scanning their passports and answering a few questions at a touchscreen kiosk. To date, 1.8 million people have decided this convenience far outweighs the slow pace of approvals.

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The basics of influencing irrational customers are essentially the same whether they’re taking place in a store or on a screen. A business still needs to know who its customers are, understand their needs and motivations, and give them a reason to buy.

And despite the accelerating shift to digital commerce, we still live in a physical world. “There’s no divide between old-style analog retail and new-style digital retail,” Berens says. “Increasingly, the two are overlapping. One of the things we’ve seen for years is that people go into a store with their phones, shop for a better price, and buy online. Or vice versa: they shop online and then go to a store to negotiate for a better deal.”

Still, digital increases the number of touchpoints from which the business can gather, cluster, and filter more types of data to make great suggestions that delight and surprise customers. That’s why the hottest word in marketing today is omnichannel. Bringing behavioral psychology to bear on the right person in the right place in the right way at the right time requires companies to design customer experiences that bridge multiple channels, on- and offline.

Amazon, for example, is known for its friction-free online purchasing. The company’s pilot store in Seattle has no lines or checkout counters, extending the brand experience into the physical world in a way that aligns with what customers already expect of it, Dhar says.

Omnichannel helps counter some people’s tendency to believe their purchasing decision isn’t truly well informed unless they can see, touch, hear, and in some cases taste and smell a product. Until we have ubiquitous access to virtual reality systems with full haptic feedback, the best way to address these concerns is by providing personalized, timely, relevant information and feedback in the moment through whatever channel is appropriate. That could be an automated call center that answers frequently asked questions, a video that shows a product from every angle, or a demonstration wizard built into the product. Any of these channels could also suggest the customer visit the nearest store to receive help from a human.

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The omnichannel approach gives businesses plenty of opportunities to apply subtle nudges across physical and digital channels. For example, a supermarket chain could use store-club card data to push personalized offers to customers’ smartphones while they shop. “If the data tells them that your goal is to feed a family while balancing nutrition and cost, they could send you an e-coupon offering a discount on a brand of breakfast cereal that tastes like what you usually buy but contains half the sugar,” Guszcza says.

Similarly, a car insurance company could provide periodic feedback to policyholders through an app or even the digital screens in their cars, he suggests. “Getting a warning that you’re more aggressive than 90% of comparable drivers and three tips to avoid risk and lower your rates would not only incentivize the driver to be more careful for financial reasons but reduce claims and make the road safer for everyone.”

Digital channels can also show shoppers what similar people or organizations are buying, let them solicit feedback from colleagues or friends, and read reviews from other people who have made the same purchases. This leverages one of the most familiar forms of behavioral psychology—reinforcement from peers—and reassures buyers with Shiv’s Type 1 mindset that they’re making a choice that meets their needs or encourages those with the Type 2 mindset to move forward with the purchase. The rational mind only has to ask at the end of the process “Am I getting the best deal?” And as Guszcza points out, “If you can create solutions that use behavioral design and digital technology to turn my personal data into insight to reach my goals, you’ve increased the value of your engagement with me so much that I might even be willing to pay you more.”

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Many transactions take place through corporate procurement systems that allow a company to leverage not just its own purchasing patterns but all the data in a marketplace specifically designed to facilitate enterprise purchasing. Machine learning can leverage this vast database of information to provide the necessary nudge to optimize purchasing patterns, when to buy, how best to negotiate, and more. To some extent, this is an attempt to eliminate psychology and make choices more rational.

B2B spending is tied into financial systems and processes, logistics systems, transportation systems, and other operational requirements in a way no consumer spending can be. A B2B decision is less about making a purchase that satisfies a desire than it is about making a purchase that keeps the company functioning.

That said, the decision still isn’t entirely rational, Berens says. When organizations have to choose among vendors offering relatively similar products and services, they generally opt for the vendor whose salespeople they like the best.

This means B2B companies have to make sure they meet or exceed parity with competitors on product quality, pricing, and time to delivery to satisfy all the rational requirements of the decision process. Only then can they bring behavioral psychology to bear by delivering consistently superior customer service, starting as soon as the customer hits their app or website and spreading out positive interactions all the way through post-purchase support. Finishing strong with a satisfied customer reinforces the relationship with a business customer just as much as it does with a consumer.

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The best nudges make the customer relationship easy and enjoyable by providing experiences that are effortless and fun to choose, on- or offline, Dhar says. What sets the digital nudge apart in accommodating irrational customers is its ability to turn data about them and their journey into more effective, personalized persuasion even in the absence of the human touch.

Yet the subtle art of influencing customers isn’t just about making a sale, and it certainly shouldn’t be about persuading people to act against their own best interests, as Nudge co-author Thaler reminds audiences by exhorting them to “nudge for good.”

Guszcza, who talks about influencing people to make the choices they would make if only they had unlimited rationality, says companies that leverage behavioral psychology in their digital experiences should do so with an eye to creating positive impact for the customer, the company, and, where appropriate, the society.

In keeping with that ethos, any customer experience designed along behavioral lines has to include the option of letting the customer make a different choice, such as presenting a confirmation screen at the end of the purchase process with the cold, hard numbers and letting them opt out of the transaction altogether.

“A nudge is directing people in a certain direction,” Dhar says. “But for an ethical vendor, the only right direction to nudge is the right direction as judged by the customers themselves.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

About the Authors:

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Sam Yen is Chief Design Officer and Managing Director at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.


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Digitalist Magazine

TBI Streamlines a Complex Value Chain in Telco Services

Posted by Emily Houghton, Industry Marketing

logo ct tbi TBI Streamlines a Complex Value Chain in Telco ServicesOperating in a complex industry, with an intricate, multi-party value chain, TBI (Telecom Brokerage Inc.), was looking to introduce simplicity and efficiency across their organization.

The 26-year-old company looked to streamline internal processes so they could continue to provide a “white glove” approach to their selling partners. To do that, TBI needed to update its own processes across that value chain.

TBI works on a model similar to how a consumer might work with an insurance agency to find the best auto, home or health insurance. They can compare premiums, deductibles and coverage from multiple insurers to select the ideal policy. As a telecommunications master agent, TBI’s customers are systems integrators, value-added resellers and other IT consultancies that need a communication technologies for an end-customer.

TBI helps those selling partners select the best voice, data, network and managed services from more than 85 vendors, including Verizon, Rackspace, Comcast, AT&T, Spectrum and CenturyLink. Its vendor-agnostic services include sourcing, advising, training, back-office support and commissioning partners paid by vendor solutions.

NetSuite plays a pivotal role in simplifying and introducing efficiencies in that process. Deployed in 2014, NetSuite has helped TBI increase sales productivity by 133 percent in less than three years. Commissionable revenue has soared 100 percent, while the workforce has grown from 80 to 180 employees.

“Our business has seen explosive growth that’s a byproduct of the hot technology sector — but also our ability to adapt with real-time business intelligence in NetSuite,” said Jeff Newton, VP of Enterprise Sales and IT at TBI, based in Chicago.

“NetSuite has given us a level of visibility we never envisioned,” Newton added. “We can better manage our customers’ experience based on how efficiently orders move through our system.”

Initially, TBI looked to NetSuite to graduate from QuickBooks and to supply CRM functionality. In short order, the company rapidly expanded the NetSuite footprint to cover all key operational processes, including its mission-critical commissioning function.

For years, TBI had relied on an industry-standard solution called RPM Telco for commissioning. As it mapped out its strategy, TBI foresaw the benefits of running financials, CRM, project management and commissioning on a unified platform. That would streamline the full lead-to-cash workflow and heighten visibility across the organization.

“We were effectively putting our business on the line by moving commissioning into NetSuite,” Newton said. “It’s a very complex and mission-critical workflow to get commissions to agents.”

Summing up the results, Newton said: “I can say our migration from RPM Telco to NetSuite has made this the most successful technology move TBI has ever undertaken.”

TBI worked to accomplish the commissioning transition, initially. Later hiring NetSuite Solution Provider Gurus Solutions for further customizations needed in using the SuiteCloud development platform. And it utilized the NetSuite Advanced Partner Center to let more than 2,000 partner users track commissions, orders and financial data in NetSuite. These partners can now run reports, open tickets and troubleshoot issues.

That interactivity and transparency improves selling partner satisfaction and repeat business, helping drive TBI’s double-digit revenue increase.

In selecting NetSuite after evaluating Microsoft Dynamics, Salesforce, Sage and SugarCRM, TBI’s initial idea was to streamline its ordering and operational processes.

As TBI soon experienced, NetSuite delivers flexible, on-demand reporting for real-time insights into key metrics. TBI has gained groundbreaking visibility to continuously optimize the business through its operational support department.

“One of the biggest surprises is that the analytics, business intelligence and reporting we have with NetSuite is the best we’ve ever seen,” Newton said.

With an agile, scalable foundation in NetSuite, TBI has its sights set on continued growth and diversification. Newton said one particular opportunity is to partner with NetSuite Solution Providers to complement NetSuite deployments with telco infrastructure.

“As we look to grow our business in new sectors and verticals, NetSuite is not a limiting factor whatsoever,” Newton said. “It scales with the business.”

Posted on Wed, August 9, 2017
by NetSuite filed under

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Stop Trying to Win Against Robo-Advisors: Change the Game by Providing Value they Can’t with Today’s “Predictive” CRM

CRM Blog Stop Trying to Win Against Robo Advisors: Change the Game by Providing Value they Can’t with Today’s “Predictive” CRM

Morgan Stanley recently announced it is putting a machine learning-based system in an effort to make its financial advisors (the human ones) more effective. And this means what? That Morgan Stanley is ahead of the pack in understanding that robo-advisors are diluting the entire market, and simply having a “relationship” is not enough to retain clients and keep them loyal. Human advisors today need the ability to offer something more—something robo-advisors cannot: a crystal ball.

But here’s the problem: Human advisors, as far as we know, don’t have one, either.

What Morgan Stanley is doing, though, is s helping its advisors become more proactive by adding predictive technology—which provides more value to their existing clients. The theory they are promoting is that having a human advisor with an “algorithmic assistant” would be preferable to basic software that lumps the clients together using extremely limited information, and allocating assets wholesale within each category, based on how they are profiled.

We think Morgan Stanley has got this right, but do the rest of us necessarily need to have highly customized, complex technology (which is not cheap, I might add) to get us closer to that crystal ball? Or, could your firm achieve this goal with—hmmm—say, your CRM system?

Traditional CRM: It did what it needed to do before, but it’s no longer fighting the fight

If you have worked with CRM in a financial services capacity, you already know what it can do. The typical role for CRM has been that of a control tool—primarily taking care of managing relationships, asset aggregation, and reporting. At AKA, we have been implementing these Microsoft Dynamics CRM systems for many years, and we could easily argue that, when it comes to the integration of data from transfer agents, the systems that manage portfolios, trade settlement systems, and other such programs, we are the go-to experts.

Here’s the problem with traditional CRM systems:  They cannot provide enough value for the financial advisors when it comes to predictive relationship management. The reason for this is that traditional CRM systems are housed on premises, which limits them to using internal data such as roll-up information and account information. These systems have not been able to tap into any external sources, and they also lacked the capability to provide predictive analytics and machine learning. They basically were just not built that way.

Super-charged CRM: Machine learning is the new crystal ball

But hold on: If you’re thinking you’ve lost this war with the robo-advisors, you haven’t. Right now, advisors have the perfect opportunity to show clients that they are not just portfolio managers. They can help their clients reach their goals and realize their dreams. But to do this, they must get in front of the information so they can start providing such an unbelievable client experience that their client begins to think their advisor does indeed possess the ability to see into the future. Today’s CRM can aid them in doing just that. In fact, what CRM can offer now is pretty amazing.

Taking advantage Cloud capabilities, the CRM functionality in Microsoft Dynamics 365 can instantly integrate with other systems along with their information sources. Microsoft also features Relationship Insights, which basically super-charges CRM, turning it into a predictive tool that adds value through a proactive approach. Relationship Insights offers Microsoft’s capacity for managing external data as well as data that you have typically integrated within your CRM system. That takes CRM—and client relationships—to a new, exciting level by leveraging AI and machine learning along with the Cloud.

With the parameters and triggers you establish in the system, CRM reaches out to advisors well in advance—allowing them to make smarter, more predictive decisions that will benefit their clients. Beyond just monitoring their clients’ portfolios, advisors can now ensure that action is being taken on trends in the earliest stages. In addition, by monitoring social media channels, the advisors can provide a more personalized type of guidance. For example, if an advisor begins noticing that a client is posting lots of photos of sailboats on Instagram, he (the advisor) can then reach out to discuss how the client can work the purchase of a sailboat into their financial plan.

With this new layer of technology, a reactive CRM changes to a predictive tool, thereby allowing for greatly improved outreach to your clients and a considerably higher relationship score. The very minute you give a financial advisor the ability to make better decisions in the limited time that they have, you have added more worth to their client relationships. You’ve allowed them to focus more on those clients who are more valuable, increasing the chances for retention.

I’d like to see a robo-advisor do that.

Change the game!

To summarize, the CRM of today is equipped with predictive technology. Programs like Relationship Insights give your advisors the ability to build upon those precious, human relationships they have with their clients, allowing them to focus on helping them achieve their goals and live the dream. And that, my friends, is how you get a client for life.

So, stop playing the robo-advisor game–and learn how to change it. Check out our recorded webcast, Competing with Robo Advisors: How to Carry a Bigger Book of Business While Providing Clients with a High-touch Experience.

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21 Facts On The Value Imperative To Embrace The Digital Economy

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

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

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

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

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The untapped potential of machine learning

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

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

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

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

Plan for change

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

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

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

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

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

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Machine learning as collaborator

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

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

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

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

Until now.

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

The strength of machine-learning integration

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

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

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

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

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

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

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

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

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

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

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

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

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

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Machine learning needs a platform

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

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

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

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

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

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

Organizations don’t need to go it alone

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

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

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

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

1. The focus area as an appropriate candidate.

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

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

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

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

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

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

Key evaluation criteria for machine learning

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The human factor

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

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

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

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

What’s next

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

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

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Download the white paper Why Machine Learning and Why Now?

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

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

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

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


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future of business – Digitalist Magazine

The Value Wedge: What Data IS, DOES, and MEANS in Data Governance and Data Quality Initiatives

If you’ve followed the enterprise software industry over the past ten years, you’ve probably picked up on a key trend that has led successful vendors to prominence and others to their demise – making data “fit for purpose.” The successful vendors have offered solutions that are simple to deploy, easily accessible for IT to manage and administer, while empowering its line of business users to leverage data to fully benefit their unique departmental goals.

You may be asking what I’m getting at or how this relates to the Data Governance, Lineage, and Data Quality, much less our recent webinar with ASG Technologies, and I don’t blame you for struggling to connect the dots. I do, however, have a very good answer for you, and I’d invite you to join me for the next 820 or so words to find out.

Survey Says….

We recently polled customers and asked them to identify which type of organizational user is most interested in accessing data lineage and data quality indicators, and got a somewhat surprising result.

“Business users,” followed closely by the “Reporting and Analytics” team, topped the vote totals. This wasn’t a complete shock, as we’ve witnessed data ownership and usage shift toward the business. However, the “technical users” group came in next to last.

This is a very interesting result when you consider that the data flows directly into critical enterprise applications, and often undergoes transformations (ETL), that these IT users are directly involved with. And, up until recently, the clear majority of data access and management was an assumed, and sometimes greatly protected, responsibility of this group of users.

So, what’s going on?

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Defining Your Organization’s Competitive Differentiation

When I saw the results, I was reminded of a sales strategy workshop I took a couple of years ago with the sales and presentation strategy consulting firm, Corporate Visions. The firm helps companies define their “value wedge”, or competitive differentiation, in efforts to allow organizations to compete more successfully, based on the “IS,” “DOES” and “MEANS” of your product.

value proposition The Value Wedge: What Data IS, DOES, and MEANS in Data Governance and Data Quality Initiatives

An example of this strategy can be easily explored and outlined by looking at your very own smart phone device. If I asked you what isthe smart phone made of, you might respond, plastic, circuit boards, a camera, speaker, screen, and buttons. If I asked you what the phone does, you might say that it stores the information of your friends and family, it makes phone calls, takes pictures, lets you surf the internet, play games and helps you with math from time to time. But what does the smart device mean to you?

It means that you can video chat with your kids when you are halfway around the world. It means that you can run your small business on a beach vacation, or connect with old high school buddies on social media. It means you can capture memories that will stay with you forever. This is where understanding what the product means helps sell the product.

I can’t help but feel that the value wedge strategy also holds true when we look at how organizations manage and interact with their own organizational data. I believe we are seeing the clear differentiation of two core groups within every organization that are equally important in a successful approach to any data critical initiative.

  • Technical users excel in understanding what data IS, the coding and integration, the 0s and 1s, managing and provision of infrastructure, as well as how specific data supports mission critical organizational applications. (Amongst countless other strategic tasks, often thankless).
  • Business users understand what that specific data DOES, and excel in using the data made available to them within applications. Not only at an organization macro level, but how it directly impacts their critic departmental processes and performance.

What Your Data MEANS: Uncovering the Value Wedge

A 1994 episode of Seinfeld, “The Gymnast,” features a Magic Eye stereogram, much like the image below. Elaine’s boss finds himself staring at the image for hours each day trying to make out a hidden spaceship, seeing beyond the color and patterns, while Kramer easily makes out the real image in vivid detail.

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I can’t help but think of how powerful of an analogy that can be in many circumstances when a person who excels in understanding what data IS, stands near a person who excels in the DOES. This IS person clearly sees the many colors, shapes, patterns, and borders of the picture, while the DOES person can make out a vivid real image (I’m an IS person, so I couldn’t tell you whether the picture above is a spaceship or dinosaur).

I firmly believe that there is, and will always, be a striking difference in how data is viewed and understood, and that is what makes both perspectives so valuable for organizations. You need both perspectives to be successful. However, it isn’t until those two perspectives combine that an organization truly understands what the data MEANS

blog data value wedge The Value Wedge: What Data IS, DOES, and MEANS in Data Governance and Data Quality Initiatives

So, what does your data MEAN to your organization? For compliance and regulation reporting? What does it mean for your bottom line? For efficiency and productivity? What would having confidence in your enterprise data today mean to you?…

On May 31st, ASG and Trillium Software hosted an educational webcast where we explored the importance – and challenge – of determining what data MEANS to your organization, as well as solutions to empower both your technical (IS) and business users (DOES) to collaborate in an efficient, zero-gap-lineage user interface. If you missed the live event, I invite you to watch the replay here.

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Plotting a general ZigZag curve with possible threshold value

I want to use zigzag curve to describe the trend of simple data. here is a list as


and I give new definition of FindPeaks and the related.

JFindPeaks[list_?ListQ] := MapAt[Round, FindPeaks[list] // N, {All, 1}]
JFindValleys[list_?ListQ] := Module[{x, y}, Map[({x, y} = #; {x, -y}) &, JFindPeaks[-list]]]
JFindExtremes[list_?ListQ] := Sort[JFindPeaks[list]~Join~JFindValleys[list]]

then some lists are computed as

peaks = JFindPeaks[lstPrices];
valls = JFindValleys[lstPrices];
extrs = JFindExtremes[lstPrices];

and two plots too,

p1 = ListLinePlot[lstPrices,
   Epilog -> {
     {Red, PointSize[0.015], Point[peaks]},
     {Blue, PointSize[0.015], Point[valls]}},
   PlotStyle -> Directive[Black, Dotted]
p2 = Graphics@Line@extrs;

finnally, the target plot comes out.

Show[p1, p2,
 AspectRatio -> 1/GoldenRatio,
 Frame -> True,
 GridLines -> Automatic,
 GridLinesStyle -> Directive[Gray, Dotted],
 ImageSize -> Large

It’s like this,

dNBIQ Plotting a general ZigZag curve with possible threshold value

but the most I want to get could be like the following one or the other similarly, or these sub-peaks-valleys should be ellminated on the plot.

JVeZN Plotting a general ZigZag curve with possible threshold value

so how to realize it? Maybe a threshold value is necessary. Thanks!

1 Answer

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Recent Questions – Mathematica Stack Exchange