Tag Archives: Intelligence

Power BI expands access to intelligence for external guest users

Power BI was first introduced with a simple commitment: Empower people and organizations with access to critical intelligence. The recent general availability of Power BI Premium in June broadened the service’s reach, allowing organizations to distribute BI content broadly without requiring recipients to be licensed individually.

Today I am happy to announce that Power BI users can seamlessly distribute Power BI apps and dashboards to guest users outside of their organization – recipients are able to securely sign into the service using their own organization’s security credentials or personal email address, while the content owner is able to maintain control over the internal data.

This new feature is the result of Power BI integration with Azure Active Directory (AD) business-to-business (B2B) collaboration.

For example, consider a scenario where an automotive manufacturer working with many diverse suppliers wants to streamline its supply chain logistics – all the components, materials and services necessary to run its manufacturing operations. The organization plans to use Power BI to monitor key supply chain performance metrics by building a BI portal its employees and partners can access.

Previously the automaker would have needed to create duplicate identities for users belonging to partner organizations, requiring those users to remember multiple sets of credentials, and creating challenges for governance enforcement and identity management. Alternatively, the automaker could have invested the time and cost of building an app with Power BI Embedded that employs custom authentication.

In this instance Power BI’s integration with Azure AD B2B enables seamless, secure access for guest users from partner organizations – the automaker can create a Power BI app in the service, invite guest users, and distribute the BI content to them to access by authenticating via their organization’s Azure AD credentials.

External users can be licensed to receive BI content in two ways – either the content is allocated to Power BI Premium capacity, or the external user is assigned a Power BI Pro license. And in the instance of the external user being assigned a Power BI Pro license, this can be done by either the external user’s administrator or, as a new capability, by the sharing organization’s administrator.

Power BI integration with Azure AD B2B provides the peace of mind organizations can employ trusted Azure AD authorization policies to protect their data, including conditional access policies and risk-based authentication. Admins are also able to set policies for external B2B invites, such as the ability to turn off or restrict the ability for users to send invitations.

Next steps:

  • AAD B2B with Power BI is available starting today.  Read the documentation and try inviting an external user now!
  • For more information and a step-by-step guide to distributing BI content with Power BI and Azure AD B2B read this whitepaper. Also visit Azure documentation for more information on Azure AD B2B.

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Business Intelligence Emboldens Digital Transformation

In May 2017, a computational social scientist from The Psychometrics Centre at the University of Cambridge stood before an audience at the Linux Foundation’s Apache Big Data conference and revealed how close we’ve come to the ultimate goal of marketing: an easily scalable, highly accurate way to predict customer preferences using minimal data.

When she was still a PhD candidate, Sandra Matz created a Facebook ad campaign targeting people based on nothing more than how extroverted their Facebook Likes indicated they were. People with Likes associated with extroverts saw ads for a party game played in a group. People with more introverted Likes saw ads for a quiet game meant to be played solo.

The campaign required only simple algorithms and no advanced analytics. Yet over seven days of testing, the targeted ads generated up to 15 times higher click-through and conversion rates—and significantly more purchases and revenue for the game company.

SAP Q317 DigitalDoubles Feature3 Image2 Business Intelligence Emboldens Digital Transformation“We developed this approach to show that you can achieve highly accurate behavioral and psychological targeting with a minimal amount of data and relatively simple machine learning tools,” says Matz, who is now an assistant professor of management at Columbia University’s business school.

As effective as this experiment was, Matz suggests that it’s still rudimentary compared to what could be done with more and richer data from more sources. And it’s downright primitive given the possibilities of applying more sophisticated Big Data analytics.

These possibilities have created a watershed moment for marketing and its role in the business.

Spiraling Down the Marketing Funnel

Tension has always simmered over marketing’s contribution to business success. The business knows it can’t sell products or services if it doesn’t make customers aware of them, but the impact of marketing strategy on sales and revenue is hard to quantify and reliably replicate—which, in the age of the data-driven enterprise, often leaves some business leaders not just undervaluing marketing but actively mistrusting it. No wonder human resources consultancy Russell Reynolds reports that the 2016 turnover rate among CMOs was the highest it has seen since it began tracking the statistic in 2012.

Most companies still determine customers’ readiness to buy by using a primitive model known as the marketing funnel, which sorts customers into increasingly smaller groups as they progress from first becoming aware of a company to buying, using, and finally advocating for the company’s products. Different versions have different definitions and numbers of stages, and some approaches see the model as a circle, but they all have one thing in common: their ability to sort customers into various stages is limited by the amount of knowledge the company has about each customer.

As a result, the marketing funnel ends up leaking. Some customers back away because they feel harassed by campaigns that don’t apply to their needs, while some of those who are interested fall through the cracks from a lack of attention. Many data-hungry business leaders think of the marketing funnel as no more than a variation of “throw something against the wall and see if it sticks,” and with the proliferation of digital channels and diffusion of customer attention, they have less patience than ever with that approach.

The silver lining is that a more precise, quantifiable way to build customer relationships is emerging. Done properly, it promises to defuse the tension between marketing and the rest of the business, too.

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The Defining Moment

The Cambridge University experiment is one more step toward the long-held marketing dream of the “segment of one.” This concept of marketing messages that are highly granular, even individually tailored, has been around since the late 1980s. Over the last 15 to 20 years, as customer behavior has become digitalized as never before, marketers have been optimistic that they could capture this data and use it to tailor their messaging with laser-like precision.

Yet what’s achievable in theory has been impossible in practice. We’re still struggling to find the right tools to move beyond the basics of demographic targeting. The rise of the internet, smartphones, and social media has generated more types of information about customer behavior in larger amounts than ever before. But using digitally expressed sentiment about everything from toys to turbines as the basis for accurately disseminating highly individualized marketing messages is still time consuming and cost prohibitive.

However, experiments like Matz’s are bringing us closer to creating highly personalized customer experiences—perhaps not always at the individual level but certainly at a level of granularity that will let us unequivocally determine how to best target and measure marketing programs.

Liking Lady Gaga

Between 2007 and 2012, Psychometrics Centre researchers gathered seven million responses to a simple questionnaire for Facebook users. The carefully designed questions measured respondents’ levels of extroversion, agreeableness, openness, conscientiousness, and neuroticism, a constellation of basic personality traits known as the Big Five.

With the respondents’ permission, the researchers used simple machine learning tools to correlate each person’s responses with the official Facebook Pages that the person had liked, such as Pages for books, movies, bands, hobbies, organizations, and foods. They soon saw that certain personality traits and certain Likes went hand in hand.

For example, most people who liked Lady Gaga’s Page tested as extroverts, which made liking the Lady Gaga Page a relevant data point indicating that someone was probably an extrovert. By 2016, Matz was able to create a lively Facebook ad to be shown only to people who had liked a significant number of official Pages that seemed to be linked to extroversion. A more serene ad was shown only to those whose Likes suggested that they were introverts.

SAP Q317 DigitalDoubles Feature3 Image4 Business Intelligence Emboldens Digital TransformationDespite the large size of the Psychometric Centre’s data set, what’s most remarkable about its work is how few data points within that data set were necessary to build a reliable profile that could model useful predictions. Matz told EnterpriseTech that the algorithm the Centre developed needs, on average, just 65 liked Pages to understand someone’s Big Five personality traits better than their friends do, 120 to understand them better than their family members, and 250 to understand them better than a partner or spouse. This may be the first sign that the era of true behavioral marketing is upon us.

Of course, most marketers want to know far more about customers than how outgoing or reserved they are. Scraping Facebook Likes isn’t enough to give them the holistic customer understanding they crave—not when they have an entire universe of other data to consider. The race is on to identify from the vast spectrum of available customer data not only which specific online behaviors have a predictive element such as extroversion or introversion but also which ones will drive the most potent response to specific product or service messaging.

Complicated? Yes—but we are within reach of the algorithms we need to connect the dots for greater customer insight. By reaching out over new channels with more accurate behavior-based messaging, companies could transform the entire customer journey.

A Customized Journey for Each Customer

Attribution, the ability to know the source of a sales lead, is key to behavioral targeting. The more details a business knows about what its customers have already done, the more accurately it can predict what they will do next.

In the past, developing a customer profile relied on last-touch attribution analysis, that is, evaluating the impact of the last interaction a prospective customer had with a brand before becoming a lead. The problem was that companies could rarely be certain what that last touch was, given how much activity still takes place offline and isn’t captured or quantified.

Companies also couldn’t be certain how, or even if, a last touch—be it downloading a white paper, visiting a store, or getting a word-of-mouth recommendation—accelerated the customer through the marketing funnel. They could only predict revenue by looking at how many people were deemed to be at a specific stage and extrapolating from past data what percentage of them were likely to move ahead.

SAP Q317 DigitalDoubles Feature3 Image5 Business Intelligence Emboldens Digital TransformationToday, we’re capturing so much more information about people’s activities that we have a far more accurate idea of both what the last touch was and how influential it was. Behavioral targeting makes any content a customer interacts with valuable in analyzing the customer’s journey. A company can use hard data about those interactions to see where each individual prospect is in the customer journey and predict how likely each one is to continue moving forward.

The company can then generate a tailored offer or other event to nudge individuals along based on what has been successful with other customers who buy the same things and behave in the same ways. For example, a large grocer may send out two million individualized offers each week based on loyalty card activity. This may not strictly create a segment of one, but it creates many small segments of customers with similar behaviors based on what the grocer knows to be effective.

As Cambridge University’s experiment in creating an algorithm to identify and target introverts and extroverts proves, more precise messaging is more effective. By using more complex machine learning algorithms to further filter and refine successful messages to target smaller groups, companies could boost their conversion rates to as high as 50%—an exponential increase beyond today’s average rates.

By using machine learning to speed up the testing of different campaigns and to continuously compare results, companies could rapidly create a dataset about every potential customer’s responses and then benchmark it against others’ responses. This would let them determine individual prospects’ likely responses based on concrete actions rather than assumptions.

For super-luxury brands with a limited number of customers and the ability to capture a vast amount of information about each one, this could lead to true segment-of-one marketing. For other brands, the challenge is not just to figure out who the customer is and what messages to send but also how to scale that personalization to segments of tens of thousands (or hundreds of thousands) of customers at a time. To do that both effectively and quickly, companies will need to leverage machine learning, the Internet of Things, and other advanced technologies that enable accurate predictive models. Companies can then benchmark their projected hit rates against their actual results and refine their algorithms for even greater agility and responsiveness.

The Next Steps of Predictive Marketing

Effective behavioral targeting requires companies to identify all the relevant data points, including external data points that indicate which information is valuable. This calls for data scientists who can spot and remove the irrelevant data points that are at the far ends of the curve and distill what remains into meaningful algorithms. It also requires machine learning tools capable of high-volume, high-speed listening, assessing, learning, and making recommendations to improve the algorithm over time.

Once you’ve created a baseline of primary criteria, you can determine the important criteria by which to segment your customer base. To use an oversimplified example, imagine that you own a coffee shop and you want to increase sales of high-margin bakery items. You need to look not at the customers who always get a muffin with their coffee or at those who never do but at those who buy a muffin sometimes, so that you can start to identify the triggers that make them choose to indulge.

To scale this process, look at both user-based and item-based affinities. User-based affinities link customers who have similar interests and shopping patterns. Item-based affinities link customers based on what they buy, individually or in groups of items. Using machine learning to pair and cross-reference these two factors will enable you to create messages that are personalized enough to seem individualized, even though they’re actually targeting small, multi-person segments.

SAP Q317 DigitalDoubles Feature3 Image6 Business Intelligence Emboldens Digital TransformationRetailers of all types collect data about individuals, down to location, date, time, and SKU of the sale. They may experiment with behavioral targeting by making in-the-moment offers based on what they already know about their customers. For example, they may use a mobile app with geofencing to be alerted when a customer using the app is in the store. The alert triggers back-end systems to look up the customer’s purchase history, generate a relevant offer, and deliver that offer to the customer’s smartphone while the customer is still in the store.

The Line Between Marketing and Manipulation

Just the idea of receiving marketing messages influenced by their behavior will disturb some customers. When marketing is designed, as behavioral targeting is, to maximize engagement, the value of the content depends less on whether it’s useful to the audience or even true and more on whether it gets the target audience to engage and reveal another piece of the behavioral puzzle. As a result, companies considering behavioral marketing must consider a question as old as marketing itself: where is the line between advertising and propaganda?

Creating personal profiles of customers based on their actions and personalities will become inexpensive and easy, for better or worse. Better will lead to more relevant and compelling offers based on predictive models of what customers would like to buy next. Worse will create (or at least look like) scalable, granular manipulation.

If companies hope to apply this level of targeted marketing without coming across as intrusive or invasive, they will need to be completely transparent about what they’re doing and how—and with whom they’re sharing the information. Most shoppers say they’re willing to give up data about themselves if it leads to a better shopping experience and more relevant recommendations.

Numerous studies show that customers are comfortable sharing their buying patterns and preferences as long as it doesn’t compromise their personally identifiable information. Nonetheless, they may decide otherwise if they believe that by welcoming you into their lives, they’re throwing open the doors to strangers as well.

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As data mining for behavioral targeting becomes more common, companies will have to offer customers the opportunity to opt in and out at varying levels of detail. They will also need to identify and flag the significant minority of customers who prefer not to be profiled in such depth (or at all). Machine learning will be invaluable in responding to complaints on social media, tracking the relevant details of offers that were ignored or got negative reactions, and otherwise ensuring that companies don’t misuse customer data or misunderstand consumer wants and needs.

“The entire paradigm of targeting and campaign implies a vendor doing something to customers,” says Mark Bonchek, founder and “chief epiphany officer” at Shift Thinking, a Boston-based consulting firm that helps companies pursue digital transformation. “It implies getting people to do what you want them to do rather than helping them do what they want to do,” he says. “Be clear on the mental model behind your behavioral targeting. Is it more like a friend figuring out the right gift for a friend or a salesperson trying to close a deal with a prospect? People don’t want to be targets.”

Instead, Bonchek suggests, think of behavioral targeting as a way to build a reciprocal relationship that lets you enhance the customer experience at multiple touch points, not all of them actual transactions. Utility companies send customers information about their own and their neighbors’ energy use so they can benchmark themselves. The utilities often follow up with suggestions about how to save both power and money. Meanwhile, a credit card issuer could help customers understand their purchasing patterns and discover new stores or service providers.

“Loyalty is an emotion first and behavior second,” Bonchek says. “It’s the difference between pushing customers through a funnel and helping them achieve a shared purpose.”

The Art of Scientific Marketing

In mid-20th century New York City, a small local chain of markets developed a national reputation for customer service. It let favored customers call in orders and pay for them at pickup. Managers kept lists—handwritten lists, no less—of their best customers’ preferred products and called those customers with special offers. People were happy to pay slightly higher prices overall in exchange for exclusive bargains and highly customized service.

Although it leverages new technologies like machine learning and Big Data, behavioral targeting will in many ways bring us full circle to that hands-on era in which companies created relevant offers that made customers feel valued and understood. Matz believes it would be a competitive advantage for companies to let customers interact with their profiles and even correct them to ensure that they only receive offers that meet their needs and preferences.

As more situational data pours in from smartphones and wearables to be analyzed by AI, she adds, behavioral targeting could become something more immersive than mere marketing. “If you know from that data that someone is not just an extrovert with specific preferences but that they’re currently in a good mood, you can start fine-tuning messages for that particular point in time,” she says. “We’ll move beyond static profiles to interactions based on characteristics that fluctuate.”

With enough data to work with, she suggests, behavioral targeting could become less about making offers and more about informing customers about their options at any given moment, in real time. D!


About the Authors

Denise Champion is Vice President of Strategy, Research, and Insights for Global Marketing at SAP.

Jeff Harvey is Global COO, SAP Analytics & Insight at SAP.

Lori Mitchell-Keller is Global General Manager, Consumer Industries at SAP.

Jeff Woods is Global COO, SAP Leonardo | Data and Analytics.

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


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

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Connected Intelligence at Work in the O.R. and the Airport

blog 1 Connected Intelligence at Work in the O.R. and the Airport

University of Iowa Hospitals and Clinics Transforms the O.R.

In the United States, roughly one in 20 patients admitted to a hospital develops an infection. According to the US Centers for Disease Control and Prevention, surgical site infections are the most common, accounting for more than 30 percent of occurrences, and putting patients at risk for illness, prolonged hospitalization, and death. In addition, the cost of hospital-acquired infections is estimated at $ 10 billion per year.

At the University of Iowa Hospitals and Clinics, Dr. John Cromwell, director of gastrointestinal, minimally invasive, and bariatric surgery, believed that predictive analytics could prevent a high percentage of surgical site infections and decrease healthcare costs. He and his team needed a flexible, enterprise-grade, advanced analytics platform that encompassed the entire analytics lifecycle—from data aggregation and preparation, to model development, deployment, and monitoring, and selected TIBCO Statistica™.

Using Statistica, results included a 58 percent reduction in surgical site infections, and, says Cromwell, “Big data and predictive analytics are transforming outcomes at virtually every point in patient care.”

Read more about the challenges faced and overcome and how the group achieves its results.

Aeroporti di Roma Lands Digital Transformation Right on Time

Aeroporti di Roma, which operates both Leonardo da Vinci Fiumicino Airport and Rome Ciampino Airport, needed to deliver innovative, efficient, and high-quality services to passengers, retailers, and the entire airport ecosystem.

“TIBCO offered a new approach, a platform to support vertical implementations and drive the future of our digital transformation,” says Head of Demand Management Floriana Chiarello. “We are using TIBCO ActiveMatrix BusinessWorks, Enterprise Message Service, StreamBase, Live Datamart, and TIBCO API Exchange to integrate and correlate all information to understand customer behaviors, support managing operational situations, and prevent critical events. TIBCO technology is managing all airport information and its correlation to enable decision-making.”

Benefits include operational cost reduction, improved customer experience, and increased retail revenue―and very a smart innovation in how customer flow data is captured, processed and understood. Read more.

Join the TIBCO customer reference program to have your business transformation story shared globally with the technology industry, and trade and business press. Your story in print, web, and video format can boost your status as a thought leader and increase awareness with technology leaders, helping you raise your company visibility and attract and retain top talent. Email customermarketing@tibco.com today!

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New eBook: Mainframe & Machine Learning for IT Service Intelligence (ITSI)

Our modern computing environments rely on many hardware components and several software layers to work together in unison. The failure of one element in this complex system could impact hundreds, thousands, or even millions of users.

Syncsort’s latesteBook reviews how an ITSI solution with machine learning capabilities can provide a comprehensive view of your organization’s service delivery, allowing you to effectively set SLAs, identify potential problems, and plan for changes in the IT environment.

blog banner eBook MF Machine Learning ITSI New eBook: Mainframe & Machine Learning for IT Service Intelligence (ITSI)

Download the eBook now: Mainframe and Machine Learning for IT Service Intelligence

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Expert Interview (Part 1): Splunk’s Andi Mann on IT Service Intelligence, ITOA and AIOps

For over 30 years across five continents, Andi Mann (@AndiMann) has built success with Fortune 500 corporations, vendors, governments, and as a leading research analyst and consultant. He currently serves as Splunk’s Chief Technology Advocate. He is an accomplished digital business executive with extensive global expertise as a strategist, technologist, innovator, and communicator. In the first of this two-part interview, he shares his thoughts on IT Service Intelligence (ITSI) and its role in IT Operational Analytics (ITOA) and Artificial Intelligence Operations (AIOps).

What is ITSI and how does it fit with ITOA and/or AIOps?

According to Gartner, IT Operational Analytics (ITOA) is a market for solutions that bring advanced analytical techniques to IT operations management use cases and data. ITOA solutions collect, store, analyze, and visualize IT operations data from other applications and IT operations management (ITOM) tools, enabling IT Ops teams to perform faster root cause analysis, triage, and problem resolution.

As it has become more sophisticated, Gartner has redefined ITOA as “AIOps,” initially calling it Algorithmic IT Ops, now morphing into “Artificial Intelligence Ops,” reflecting the increasing use of machine learning, predictive analytics, and artificial intelligence in these solutions.

Splunk IT Service Intelligence (ITSI) is a next-generation monitoring and analytics solution in the ITOA/AIOps space, built on top of Splunk Enterprise or Splunk Cloud. ITSI uses machine learning and event analytics to simplify operations, prioritize problem resolution, and align IT with the business.

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Using metrics and performance indicators that are aligned with strategic goals and objectives, ITSI goes beyond reactive and ad hoc troubleshooting to proactively organize and correlate relevant metrics and events according to the business service they support. With ITSI, IT Ops can better understand and even predict KPI trends, to identify and triage systemic issues, and to speed up investigations and diagnosis.

This allows maturing IT organizations to quickly yet deeply understand the impact that service degradation has not only on the components in their service stack, but also on service levels and business capabilities – think more “web store” than “web server.”

We are seeing a lot of investment by organizations in leveraging the value of Big Data, what do you see as the major drivers for this?

I see three main drivers for this new focus on Big Data.

Firstly, the increasing volume of data is creating a maintenance nightmare, but also an analytics dream. This new data – from online applications, mobile devices, cloud systems, social services, partner integrations, connected devices, and more – is full of insights, but cannot be managed with traditional tools. Big Data is often the only way to understand a modern business service at scale.

Secondly, speed and agility are emerging as market differentiators. Slow, old-school techniques like data warehousing, Extract-Transfer-Load (ETL) operations, batch data processing, and scheduled reporting are not fast enough. New-style Big Data tools, by contrast, ingest data in real time, use machine learning and predictive analytics to generate meaning, instantly display sophisticated and customizable visualizations, and produce actionable insights from Big Data as it is produced.

Thirdly, there is an increasing focus on data-driven decisions to drive innovation. From junior IT admins to senior business execs, innovation requires all stakeholders make accurate decisions in real time. Big Data allows everyone to try new ideas, determine what works and what doesn’t, and then iterate quickly to course-correct from failures or double-down on successes, quickly adjusting to new information and meeting the changing demands of the market.

In Part 2, Andi Mann discusses the reasons mainframe and distributed IT are sharing data, and the use cases where organizations are building more effective digital capabilities with mainframe back ends.

Download Syncsort’s latest eBook, Ironstream in the Real-World for ITOA, ITSI, SIEM, to explore real world use cases where new technologies can provide answers to the questions challenging organizations.

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How to Become an Intelligence Influencer

rsz blog intelligence influencer How to Become an Intelligence Influencer

Throughout our three-part series, we’ve covered how data analytics is an ally to your marketing cause and how demystifying customer insights begin with a humanized approach. In a nutshell, organizations struggling with new technology isn’t a new problem, and not all hope is lost. There are quick ways to get your foot through the door using data intelligence and influencing process-driven innovation.

Context, context, context

When was the last time you’ve seen a design failure when taken out of context? (See here and here for examples). It’s easy to laugh it off when the contextual error is purely aesthetics, but if a misinterpretation of data results in your sales teams engaging prospects with an ineffective sales pitch and the wrong product, coupled with an irrelevant marketing program, the impact can be far worse.

Forrester listed three prerequisites that drive predictive marketing success in a 2016 white paper. The first is to refresh capabilities to boost insights-driven engagement, and second is to ensure sales and marketing alignment through lead processes. Last is to gather useful data to inform predictive algorithms.

“…apply the same lenses as you would an external customer to really understand how data will be accessed by them and more importantly, in what context.”

What these points summarize is the state of needing different pieces in the bigger picture to get the customer view right. With shiny new toys like data automation and predictive analytics in place, we need first to ensure that the people using these data know what to do with it, how it is used—in short, to improve on business intelligence, starting from within the organization. It is also akin to applying an internal marketing funnel to bring perspective to a process.

There are many ways for businesses to improve their intelligence, and they probably have some areas of the organization that has developed such skills and processes. The challenge is to combine a very broad view of context within a specific time frame—let’s say a market segment and the products and campaigns targeted toward it―with a multitude of sharply focused views of context, often in real time (such as the interactions and experience provided to a customer).

How can your business reconcile those broad and focused views to define the best decisions and actions? Your stakeholders, be it sales, marketing, or customer experience, needs to apply the same lenses as you would an external customer, to comprehend how data is accessed and more importantly, in what context. Then you need to combine all of their intelligence and related capabilities so you can connect their intelligence.

People first, always

We have already identified sales and marketing as the most active users of data within an organization, but this is based on the functional needs of their roles. Similar to how marketers use personas to help us find our best customers look-alikes, a lens can be applied to help us understand what a salesperson or a marketer needs then apply that to a defined lead generation workflow and map that to success indicators to identify what works and what doesn’t.

As Aberdeen Group pointed out, “strongly aligned marketing and sales teams are 53% more likely to ensure relevant value propositions aligned to buyers’ business challenges”. The idea of a strongly aligned team refers to having a set of shared objectives, priorities, strategic and tactical operational processes, goals, and resources.

Understanding what a salesperson versus a marketer needs, then putting them together into a shared program not only accelerates the alignment process between these two functions but also helps the analytics team build dashboards efficiently. We can also apply a particular set of goals tied to these different roles, and further customized the data output according to the actual user needs at any given time.

For example, TIBCO Spotfire provides interactive visualizations derived from previously disparate data for marketers to review the effectiveness of digital engagement pathways via web content consumption trends, while sales may use another visualization with the same data sources to plot predictive trends of peaks and troughs of customer interests during a given period. Our customer is never single-dimensional, so our data outputs shouldn’t be either.

Linear or cyclical?

Once you have initiated an alignment between internal stakeholders and begin to design shared objectives within functions and cross-functionally, a process of continuous realignment and refinement needs to be set in place. How do you decide then if the process should be linear or cyclical?

One needs to only look at the shared priority of your stakeholders—the customer. While not all organizations have successfully applied measurements of Customer Lifetime Value (CLV), it is essential to a business focusing on becoming customer-centric. Why is CLV important? It tells us many things that can help maximize sales and marketing efforts, such as:

  • The potential worth or contribution your customer can bring
  • How to move your most desired (i.e. more valuable) customers through each stage of the buyer’s journey
  • Which prospects to focus on with your limited resources

This is where your data-driven intelligence comes into play. Using dashboards set up with combined data sources, mapped to your operational perspectives acquired with your “People First” approach, you are now ready to layer this information against a broader framework of calculating your customer lifetime value.

The business can then learn to identify threats or opportunities throughout a customer’s mapped lifetime while refining its predictions of outcomes of the context, and finally, make recommendations on the best action for the business to take. It is also important to note that when you have billions of people, systems, and devices interacting simultaneously, data is arriving in real time from hundreds and thousands of resources—it has a shelf life; its value diminishing over time, so you need to be able to decide and act instantly too.

TIBCO understands this and provides event-driven solutions that help businesses augment traditional business intelligence processes to capture, aggregate, and analyze data at-rest and in-motion, and help businesses spark insight for contextual awareness that leads to preemptive, actionable insights. This real-time intelligence becomes part of your competitive strategy, providing a continuous feedback loop that continually enriches your knowledge, capabilities, and responses.

And because a customer’s behavior isn’t always linear, your business intelligence shouldn’t be either.

This article is the final piece of a three-part series to look at visual, predictive, and streaming analytics technologies can apply to modern marketing.

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How to Calculate Total Cost of Ownership for Business Intelligence

Imagine you’re comparing gym memberships to figure out which one offers the best value. Sure, you could simply look at the monthly fee and go for the cheapest, but that wouldn’t tell you everything you need to know about the total cost of ownership.

For starters, you’d want to know what the cost includes. Does it offer all the machines and classes? Do you have to rent/buy extra equipment? Then there are the less obvious considerations. Do you need to pay a trainer to get true value? What’s the price of travel? Is there enough capacity to cope with the crowds, even during peak hours?

Loosely speaking, the approach to buying a new gym membership should be, for the majority of savvy businesses, the same approach they use for price comparisons when weighing up different tech solutions for their business – especially with a solution as powerful and intricate as Business Intelligence.

Business Intelligence Pricing – There’s a Catch

There are many things to consider when pricing out the total cost of ownership of BI. To really get a feel for the cost of implementing a BI solution, start by making sure that the platform in question does everything you need and has enough capacity for all of your data – or if not, how much you’ll need to spend on additional technical infrastructure, tools, or the necessary consulting / IT expertise manpower to tailor a solution version that does work for you.

Try to estimate how much you’ll need to commit in terms of internal budget and resources, whether you’ll need to pay to take on new staff, and the opportunity costs of taking existing personnel off revenue-generating projects to ensure smooth deployment and daily use.

Then, once you’ve tallied up all the hidden costs of rolling out and operating a workable solution, choose the option that offers the best value for the price tag.

Sounds sensible, right? Well, yes – in 99% of cases, this formula works just fine.

But BI is different. To work out the real cost of using your BI platform, you have to take a final, vital step: calculate the value that a BI solution gives you – it’s cost of new analytics.

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Considering the Cost of New Analytics

Let’s look at the gym membership example again. Imagine that you spot in the small print that one of the gyms is only open on weekends, whereas the other one is open every day.

Until this point, you’d thought Gym A offered the better deal. You’d calculated the total cost of ownership at $ 820 per year, while Gym B worked out at $ 1200 per year.

But if you can only visit Gym A a maximum of twice a week, even if you take every available opportunity to go, you’re still paying a significant amount of money per session. The gym is only open 104 days of the year, so the absolute minimum you pay per workout will be:

$ 820 / 104 = $ 7.8

Gym B, on the other hand, might be more expensive, but it’s open seven days a week. In fact, it’s only closed on two days out of the whole year. If you took advantage of this and went there on every possible day, the minimum you’d pay per workout would be:

$ 1200 / 363 = $ 3.3

Suddenly, Gym B looks like a much better option, right?

This is precisely how you need to approach your value assessment of a BI platform, too.

That’s because BI platforms vary wildly in the time it takes you to submit a new data query, generate results and present them in a format that makes sense – for example, an easy-to-process dashboard showing progress on your KPIs.

On first look, it might seem that the annual total cost of ownership of one product is much higher than another. Once you factor in the turnaround time for a data analysis project, though, and divide your number by the maximum amount of data projects you can process in a year, this could quickly start to look very different indeed.

That’s because BI tools aren’t best measured by total cost of ownership per annum, but by the cost of running each individual analysis.

How to Calculate the Cost of New Analytics

In short, it’s putting a concrete number on the actual value you and your team are going to be getting from a BI solution.

Since we have already established that upfront costs is just one aspect of a bigger equation, businesses are now using a newer, more clever and accurate way of measuring the total cost of ownership of a BI solution by incorporating the full value potential of BI – how much will you and your team benefit from BI – that’s by calculating the cost of new analytics.

Ask yourself: What is the cost of a new analytics report for my team? This is precisely how you need to approach your value assessment of a BI platform because the cost of new analytics essentially calculates how quickly your team can churn out (and benefit from) new analytics and reports, which actually measures how much value for how much investment you are getting from your BI tool.

A Formula for Calculating BI’s Total Cost of Ownership

By incorporating the notion of speed, you will quantify how agile a BI tool is, which depends on quickness on operations.

Get our guide on calculating the total cost of ownership of a BI tool to see an exact formula on how you can quantify the cost of new analytics and take all costs – from technical infrastructure to manpower- into account before you buy a business intelligence solution.

770x250 TOC 2 770x250 How to Calculate Total Cost of Ownership for Business Intelligence

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Expert Interview (Part 2) with Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality

At this year’s Strata + Hadoop World, Syncsort’s Paige Roberts caught up with Jules Damji (@2twitme), the Spark Community Evangelist for Databricks and had a long conversation. In this second post of our four-part interview, they discuss the trend of the Spark and Hadoop technologies and communities merging over time, and how that’s creating a science fiction novel kind of world, where artificial intelligence is becoming commonplace.

blog damji quote today theres not Expert Interview (Part 2) with Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality

Paige Roberts: One thing I’ve noticed over the last few years is that to a certain extent; the Spark and Hadoop communities seem to be merging. We just had a Hadoop focused conference, and yet half the sessions were about Spark. Why do you think that is?

Jules Damji: Apache Spark is such an integral part of Big Data because it allows people to deal with and process large scale data in a very quick manner. It allows people to run different workloads on a single, unified engine. That’s one of the main attractions.

If you look at the history of Big Data, you had all these different systems and you had to stitch them together to do your end-to-end job pipeline. It was difficult. You had to learn five different systems.

Another reason people are rallying around Apache Spark is that it works very well with the Hadoop ecosystem. You can store your data in HDFS or S3 or whatever. The API works well with the storage level. It works well with the applications. Apache Spark talks to BI tools, to Sqoop, to all these third-party data ingestion tools. And it can be deployed in different environments as well. You can have it running on YARN, on its own cluster, or on Mesos.

These dimensions of Apache Spark’s flexibility make it an integral part of Hadoop or Big Data in general. Today there’s not a single conversation that’s happening in the world where Big Data and Apache Spark are not mentioned in the same sentence.

blog banner BBDtL ExpertsSay Expert Interview (Part 2) with Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality

Roberts: Right. I see that, too.

Damji: We are in the Big Data era. We have seen data coming in fast and we need this real-time end-to-end solution. If I get data, I should be able to make a decision fast. And I should be able to consult either my machine learning model in split second time or I should be able to interact with my stored data. One of the things that Apache Spark provides through Structured Streaming is the ability to write a continuous application.

Today, you heard Reynold Xin speak about the ability to write fault-tolerant applications that give you the ability to interact with streaming data and query it as if you were querying your old, stationary data. It gives you the ability to do ad hoc analysis on the fly. Before, it took you a long time to do this after you finished getting the data. Now, you can do it instantly. That’s one thing.

The other thing I see is that Artificial Intelligence (AI) has come to its fore, and Spark is going to play a big role in the democratizing aspects of Big Data and AI.

blog damji quote AI has come Expert Interview (Part 2) with Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality

Yeah, you’re seeing artificial intelligence now. On things like self-driving cars and such.

Yes, exactly, self-driving cars, image and voice recognition, recommendation engines, and so much more. At the center of that is the ability to do advanced analytics quickly. The ability to employ popular framework like TensorFlow with Apache Spark, to be able to do machine learning using Apache Spark’s library at scale, to be able build deep neural networks, and do computational analysis quickly. That enters us into this new era of Artificial Intelligence. We now have some of these AI systems, which used to be science fiction. Now, they are taking realistic form.

blog damji quote AI systems Expert Interview (Part 2) with Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality

The science fiction novels that I read as a kid are now old hat. Yeah, we did that last year.

You will see more and more Apache Spark playing an integral role in this Big Data and Artificial Intelligence era, what I call the Zeitgeist of Big Data. At the core is the ability to process a lot of data fast, ability to manage large clusters seamlessly, ability to transform data at immense speeds, ability to process myriad kinds of data, such as text, video, unstructured, and structured data. It can all be done through the same processing engine such as Spark.

Streaming, batch, …

We’re streaming, we’re doing batch. Before, all these different systems had different formats of data, and different engines.

Yeah, different engines, and different APIs…

Right. But now you have a unified API. You have workloads that run on the same engines so that makes things a little easier. It’s the stepping stone to this powerful digital revolution. No previous industrial industrial revolutions had so many fast technology trends and innovations than this digital revolution. Just in less than few years, I mean, look at what we are going through with Apache Spark.

blog Spark 2.0 Expert Interview (Part 2) with Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality

Yeah, it’s amazing!

And 10 years from now you might have something else which might be different from Spark, but the next five, we will see Apache Spark growing. We’ll see more and more intelligent application built on top of machine learning techniques that Apache Spark facilitates and catalyzes. And we’ll see huge performance improvements.

Like Project Tungsten?

Tungsten is the second generation of how you can have 10X to 40X the performance. The need is there. The need is not new. That you have data coming in at enormous velocity is new. So, you need capacity to process it instantly. In order to do that, you need very performant distributed systems. And I think you and I are both living in the heart of this data Zeitgeist.

This revolution has been a lot like being in the center of a tornado. Everything around you changes so quickly. So, how do you like the Strata conference?

Oh, this has been wonderful. Like you said, this is a Big Data Hadoop conference and to see how many Apache Spark talks were there was amazing. A testament that Spark is an integral part of Big Data.

It’s certainly is, yes.

It is. Spark Summit is growing fast, too. Big Data and Apache Spark, that’s become a very symbiotic relationship. It’s very complimentary. You can’t really talk about Big Data and not talk about Apache Spark.

Be sure to read the first part one of this conversation, on the importance of the Apache Spark community, and don’t miss the next part of the conversation! We’ll talk about Security, and the big move of Big Data processing to the Cloud.

For more talk about the future of Big Data, including more on Spark and Hadoop, read our eBook, Bringing Big Data to Life: What the Experts Say.

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Excellent Customer Service Requires Emotional Intelligence

Staff engagement is a key component in a telco’s ability to positively impact customers, based on new data from
InMoment.

customer service Excellent Customer Service Requires Emotional Intelligence

The company recently conducted a survey of 11,000 North American customers of Internet, mobile and TV services and found that telecommunications companies’ customers just plain hate them.

Telcos are turning to self-service, automation and artificial intelligence applications like chatbots, but these tools are appropriate only in some scenarios, InMoment suggested.

In many parts of the telco customer journey, positive human interactions are essential and contribute to a good user experience, the company maintained.

Telcos should emphasize emotional IQ, or EQ, in hiring and training personnel with high emotional intelligence for all client-facing roles, the study recommends.

This holds true especially for physical locations where elevated support issues likely will be handled.

Ideal Support Staff Traits

“A huge indicator of whether support staff have what it takes for successful customer interactions is whether they really listen to and understand the issue,” said Andrew Park, director of CX strategy at InMoment.

Support staff “must be creative problem solvers, looking at each opportunity as an interesting puzzle to solve rather than a problem to get through,” he told CRM Buyer. They “should also be comfortable making smart, timely, yet empathetic decisions.”

Telcos should pay greater attention to emotional trends and related areas of weakness, the study suggests.

“We recommend [telcos] hire for connection, train for skills,” Park said. “It’s much easier to train personnel on facts than [instill] emotional and personality traits that may not come naturally to everyone.”

Telco Customers’ Disgust

Customer satisfaction fell at the one-year mark, no matter which service was used, as did the likelihood of a customer recommending the service provider to others, according to the survey.

Satisfaction over support staff knowledge, ability, efficiency, friendliness and helpfulness fell significantly at the one-year anniversary mark, and fluctuated throughout the term of a customer’s contract with the provider.

For many lines of service, the metrics showed no recovery of satisfaction.

Bill payment frustrations also peaked at the one-year anniversary mark. Both the ease of understanding a bill and the ease of paying it posed problems.

Customers who had switched providers in the previous year rated key areas of the user experience lower than those who had not switched, but they still were more likely to recommend their second provider.

Most major telcos have proactive outreach programs in the first year of their relationship with customers, but the outreach diminishes over time, the study found.

Many promotional offers expire at the one-year mark and a customer’s bill after that may differ from expectations. Customers were less willing to tolerate frustrations after the promotional period.

How Telcos Can Resolve the Problem

Telcos can invest in new technologies, such as remote diagnostic tools with video capabilities, the study suggests. That said, customer resolution strategies should include the option of human intervention with automated self-service solutions.

“Automation works well for simple and known issues,” InMoment’s Park pointed out. “Humans always perform better in more complex and emotionally charged situations.”

Sophisticated automation technology, such as AI chatbots, can understand what customers are saying in real time and trigger relevant follow-up questions to better understand what the next best action might be, Park observed.

“For instance, if an AI chatbot detects a customer’s experiencing escalating frustration based on their word choice and sentiment, it can ask whether the customer would like to be immediately routed to a human for more personalized care,” Park said.

Service reps who work well with people “are hard to find and command a higher wage than traditional service reps, who are often hired because they’re cheap,” observed Michael Jude, a research manager at Stratecast/Frost & Sullivan.

“Rather than treating customer service as an unavoidable overhead, operators should view it as the point of it all,” he told CRM Buyer. “A good customer service rep can upsell and reduce churn simply by being facilitative.”
end enn Excellent Customer Service Requires Emotional Intelligence


Richard%20Adhikari Excellent Customer Service Requires Emotional IntelligenceRichard Adhikari has been an ECT News Network reporter since 2008. His areas of focus include cybersecurity, mobile technologies, CRM, databases, software development, mainframe and mid-range computing, and application development. He has written and edited for numerous publications, including Information Week and Computerworld. He is the author of two books on client/server technology.
Email Richard.

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How Collective Intelligence Can Empower Workers And Develop Leaders

277357 l srgb s gl 300x200 How Collective Intelligence Can Empower Workers And Develop Leaders“Innovation distinguishes between a leader and a follower.” – Steve Jobs

As a part of the last wave of Millennials joining the workforce, I have been inspired by Jobs’ definition of innovation. For years, Millennials like me have been told that we need to be faster, better, and smarter than our peers. With this thought in mind and the endless possibilities of the Internet, it’s easy to see that the digital economy is here, and it is defining my generation.

Lately we’ve all read articles proclaiming that “the digital economy and the economy are becoming one in the same. The lines are being blurred.” While this may be true, Millennials do not see this distinction. To us, it’s just the economy. Everything we do happens in the abstract digital economy – we shop digitally, get our news digitally, communicate digitally, and we take pictures digitally. In fact, the things that we don’t do digitally are few and far between.

Millennial disruption: How to get our attention in the digital economy

In this fast-moving, highly technical era, innovation and technology are ubiquitous, forcing companies to deliver immediate value to consumers. This principle is ingrained in us – it’s stark reality. One day, a brand is a world leader, promising incredible change. Then just a few weeks later, it disappears. Millennials view leaders of the emerging (digital) economy as scrappy, agile, and comfortable making decisions that disrupt the norm, and that may or may not pan out.

What does it take to earn the attention of Millennials? Here are three things you should consider:

1. Millennials appreciate innovations that reinvent product delivery and service to make life better and simpler.

Uber, Vimeo, ASOS, and Apple are some of the most successful disruptors in the current digital economy. Why? They took an already mature market and used technology to make valuable connections with their Millennial customers. These companies did not invent a new product – they reinvented the way business is done within the economy. They knew what their consumers wanted before they realized it.

Millennials thrive on these companies. In fact, we seek them out and expect them to create rapid, digital changes to our daily lives. We want to use the products they developed. We adapt quickly to the changes powered by their new ideas or technologies. With that being said, it’s not astonishing that Millennials feel the need to connect regularly and digitally.

2. It’s not technology that captures us – it’s the simplicity that technology enables.

Recently, McKinsey & Company revealed that “CEOs expect 15%–50% of their companies’ future earnings to come from disruptive technology.” Considering this statistic, it may come as a surprise to these executives that buzzwords – including cloud, diversity, innovation, the Internet of Things, and future of work – does not resonate with us. Sure, we were raised on these terms, but it’s such a part of our culture that we do not think about it. We expect companies to deeply embed this technology now.

What we really crave is technology-enabled simplicity in every aspect of our lives. If something is too complicated to navigate, most of us stop using the product. And why not? It does not add value if we cannot use it immediately.

Many experts claim that this is unique to Millennials, but it truly isn’t. It might just be more obvious and prevalent with us. Some might translate our never-ending desire for simplicity into laziness. Yet striving to make daily activities simpler with the use of technology has been seen throughout history. Millennials just happen to be the first generation to be completely reliant on technology, simplicity, and digitally powered “personal” connections.

3. Millennials keep an eye on where and how the next technology revolution will begin.

Within the next few years Millennials will be the largest generation in the workforce. As a result, the onslaught of coverage on the evolution of technology will most likely be phased out. While the history of technology is significant for our predecessors, this not an overly important story for Millennials because we have not seen the technology evolution ourselves. For us, the digital revolution is a fact of life.

Companies like SAP, Amazon, and Apple did not invent the wheel. Rather, they were able to create a new digital future. For a company to be successful, senior leaders must demonstrate a talent for R&D genius as well as fortune-telling. They need to develop easy-to-use, brilliantly designed products, market them effectively to the masses, and maintain their product elite. It’s not easy, but the companies that upend an entire industry are successfully balancing these tasks.

Disruption can happen anywhere and at any time. Get ready!

Across every industry, big players are threatened — not only by well-known competitors, but by small teams sitting in a garage drafting new ideas that could turn the market upside down. In reality, anyone, anywhere, at any time can cause disruption and bring an idea to life.

Take my employer SAP, for example. With the creation of SAP S/4HANA, we are disrupting the tech market as we help our customers engage in digital transformation. By removing data warehousing and enabling real-time operations, companies are reimagining their future. Organizations such as La Trobe University, the NFL, and Adidas have made it easy to understand and conceptualize the effects using data in real time. But only time will tell whether Millennials will ever realize how much disruption was needed to get where we are today.

Find out how SAP Services & Support you can minimize the impact of disruption and maximize the success of your business. Read SAP S/4HANA customer success stories, visit the SAP Services HUB, or visit the customer testimonial page on SAP.com.

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