Big Data News Magazine http://bigdatanewsmagazine.com All about Big Data & Predictive Analytics Fri, 23 Jun 2017 17:30:40 +0000 en-US hourly 1 https://wordpress.org/?v=4.4.10 Predictive analytics: Driving the rise of the machines in federal IT – FedScoop http://bigdatanewsmagazine.com/2017/06/23/predictive-analytics-driving-the-rise-of-the-machines-in-federal-it-fedscoop/ http://bigdatanewsmagazine.com/2017/06/23/predictive-analytics-driving-the-rise-of-the-machines-in-federal-it-fedscoop/#respond Fri, 23 Jun 2017 17:30:40 +0000 http://bigdatanewsmagazine.com/?p=118879 Machine learning and artificial intelligence are no longer just the subjects of Hollywood summer blockbusters; they’re becoming key weapons in the Defense Department’s cybersecurity strategy. As Deputy Defense Secretary Robert Work stated, when there is imminent risk, “a learning machine helps you solve that problem right away.” Of course, machines …

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Machine learning and artificial intelligence are no longer just the subjects of Hollywood summer blockbusters; they’re becoming key weapons in the Defense Department’s cybersecurity strategy. As Deputy Defense Secretary Robert Work stated, when there is imminent risk, “a learning machine helps you solve that problem right away.”

Of course, machines themselves are just tools—dumb by their nature. The ability for these machines to collect raw data, process it, and use it to make informed decisions that can help prevent attacks and system failures is what makes them smart.

But preventing an attack as it’s happening is one thing; true intelligence lies in being able to predict things before they happen, like a potential threat or system failure. That’s where predictive analytics comes into play—collecting historical and recent data and applying that information to situations that may signal impending problems.

Learning from the past to protect the future

In effect, predictive analytics allows machines to become smart by “learning” from past incidents and behaviors. They can use the knowledge attained from past outcomes and make human-like decisions to automatically address issues before they become serious threats.

Predictive analytics involves collecting information from different data sets and comparing them side by side to gain a better perspective on where an anomaly may have taken place. These data sets can be collated from various points, including applications, virtual machines, storage appliances and more.

Like a science fiction film where characters jump between time periods, the data can be overlaid on timelines to trace the event in question from inception to eventual outcome. This helps establish the series of steps that took place to get from the initial start of the event to its eventual outcome. It lays the groundwork for the machine to be able to “see” and “understand” the process that took place so that it can react appropriately in case it detects a similar pattern in the future.

For instance, let’s say that a machine begins to detect some form of network anomaly. That anomaly appears similar to one that resulted from a different situation that occurred a few months ago. The machine “remembers” the previous situation and the events that led up to the ultimate outcome. It can then apply that knowledge to the present issue at hand and use that information to create a set of predictive rules or policies to ensure that the issue is not repeated. These rules could range from automatically patching all machines on the network to up-to-date software, to blocking IPs, disabling users, or other actions that might be appropriate depending on the situation.

With predictive analytics, machines become increasingly intelligent over time. They continue to build upon the knowledge that has been collected as a result of different incidents. As they become more intelligent, they are able to proactively address a number of different issues, from security threats to more mundane problems, such as network slowdowns. As a result, the network becomes much stronger.

Helping—not hurting—humanity

It’s important to note that this can all be accomplished without the need for significant human intervention, which is undoubtedly good news for time pressed federal IT professionals. One of the things we learned in a recent IT Trends Report is that IT administrators are trying to adjust to the challenges of increasingly hybrid IT environments. The more time it takes to learn new skills, the less opportunity there is to proactively manage and respond to threats and network issues. By providing machines with a platform upon which to process and learn from collected data—and make that data actionable—predictive analytics can take at least some of the network security management onus off of these individuals.

Although all of this may sound like a sci-fi movie where AI becomes self-aware and starts to master humanity, the reality is that federal IT professionals should be very optimistic about the potential for intelligent machines. When paired with actionable data derived from predictive analytics, these tools have the ability to make administrators’ lives much easier, while automating and enhancing network security and availability.

Joe Kim is senior vice president and global CTO of SolarWinds.

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Chico’s Taps First Insight for Predictive Analytics Tools – WWD http://bigdatanewsmagazine.com/2017/06/22/chicos-taps-first-insight-for-predictive-analytics-tools-wwd/ http://bigdatanewsmagazine.com/2017/06/22/chicos-taps-first-insight-for-predictive-analytics-tools-wwd/#respond Thu, 22 Jun 2017 13:10:10 +0000 http://bigdatanewsmagazine.com/?p=118877 Today Chico’s Inc. announced its partnership with First Insight Inc., the product pricing and predictive analytics platform provider, for the specialty retailer’s White House Black Market, Soma and Chico’s brands. The fashion company will tap First Insight’s consumer-driven predictive analysis to inform future design, buying and pricing decisions on categories ranging …

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Today Chico’s Inc. announced its partnership with First Insight Inc., the product pricing and predictive analytics platform provider, for the specialty retailer’s White House Black Market, Soma and Chico’s brands. The fashion company will tap First Insight’s consumer-driven predictive analysis to inform future design, buying and pricing decisions on categories ranging from apparel, footwear, accessories and jewelry for physical locations and e-commerce.

In addition to refreshed internal operations, Chico’s will upgrade its social engagement with consumers in order to optimize shopper feedback regarding the popularity of new products. What’s more, First Insight will then crunch the data in its predictive tools to furnish an improved awareness of its shopper and calibrate inventory buys, maximize allocation strategies and strategically recommend products.

“One of our key strategic imperatives is to leverage actionable retail science to improve our operational performance,” said Shelley Broader, chief executive officer and president of Chico’s FAS Inc. “Our rich customer data combined with First Insight’s online customer engagement and analytics should help us to identify top-performing product as much as 12 months preseason. We expect this partnership will enhance our ability to consistently deliver the beautiful merchandise our customers want, resulting in stronger sales for our company.”

With supply chains coming under the strain of accelerated consumer demands, retailers are turning to service-providers such as First Insight. The platforms aim to resolve pain points and facilitate accurate production of collections to deliver the highest ROI – and maintain consumers’ attention.

“By introducing enhanced analytics around the voice of the customer into its product development process, we will be able to help Chico’s FAS increase sell-through and reduce markdowns while continuing to deliver trend-right products,” said Greg Petro, ceo and founder of First Insight.

More from WWD:

Slide Over, Siri, Samsung Debuts Voice-Control Updates

The Draw of Brick-and-Mortar in a Digitized Retail Landscape

The Science Behind Influencer Marketing

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Hurricane season: how predictive analytics can reduce risk for businesses – Information Age http://bigdatanewsmagazine.com/2017/06/22/hurricane-season-how-predictive-analytics-can-reduce-risk-for-businesses-information-age/ http://bigdatanewsmagazine.com/2017/06/22/hurricane-season-how-predictive-analytics-can-reduce-risk-for-businesses-information-age/#respond Thu, 22 Jun 2017 08:55:25 +0000 http://bigdatanewsmagazine.com/?p=118875 One key challenge is helping customers to ensure existing information can be accessed and integrated into the ProcessMAP platform. ProcessMAP also urges customers to ‘think out of the box’ to allow the platform to truly leverage the data and insight it provides What are the risks posed by natural disasters …

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Predictive Analytics Risk

One key challenge is helping customers to ensure existing information can be accessed and integrated into the ProcessMAP platform. ProcessMAP also urges customers to ‘think out of the box’ to allow the platform to truly leverage the data and insight it provides

What are the risks posed by natural disasters to a business?

Business interruption and business continuity are key risks posed by natural disasters. When a natural or man-made disaster occurs, having a solution, which incorporates the intelligence to know which employees and locations are impacted, is crucial.

With a robust solution, businesses can contact employees via email or text to ensure business continuity protocols are in order to mitigate specific additional risks, protect employees and assets, and resume regular business operations as soon as possible.

How can businesses protect themselves from weather-related disasters using technology from ProcessMAP?

With a cloud-based EHS platform like ProcessMAP, businesses can proactively avoid a variety of worker or customer incidents and injuries as a result of weather-related disasters.

>See also: IT disaster recovery: flooding lessons learned

With ProcessMAP, businesses benefit from:

• A list of every employee, job function, location, training and tenure on the job.
• An understanding of where each business location is on the map, including distribution centres, retail stores, manufacturing centres, fleet of vehicles, etc.
• Integration with the most up-to date weather forecast.
• A history of incidents as well as the cost of those incidents.

Here is how that process would unfold in a real-life scenario. Consider a cable communications company with teams in the field that are at times working many stories above ground on telephone poles.

Using ProcessMAP, the company would be able to know which employee is in the field at all-times courtesy of geo-coordinates. In the event that the ProcessMAP system receives a weather notification of high winds or lighting, the system would notify the field team, supervisors and safety team to stand down and seek shelter until the bad weather passes.

Another example could be any retail/distribution centre with hundreds or thousands of locations across the US. In the event of a freezing rain scenario, the platform would be able to report if there was precipitation within the last 12 hours within a given zip code by analysing if temperatures are expected to be freezing within the next six hours. If so, the system would then alert the shift manager or store manager to initiate de-icing protocols both via email as well as text and send the protocols to them. The system would also ask the manager or shift supervisor to confirm whether he/she has taken action.

>See also: How organisations can take a holistic approach to disaster recovery

These are just a few ways that businesses can use technology to proactively prevent damage/injuries as a result of weather-related incidents.

How does ProcessMAP’s solution differ from other competitors?

ProcessMAP differentiates from the competition in several key areas. First, ProcessMAP focuses on proactive health and safety initiatives to eliminate or mitigate incidents from occurring.

As part of this focus ProcessMAP has incorporated the industry’s most robust and advanced data intelligence and analytics solution, and develops and deploys an automated intelligence capability to address human constraints. The company, in essence, serves as a virtual assistant to process the massive of amount of growing information in a business and offers insight and alerts to take action based on that information.

ProcessMAP is also unique in terms of its expertise with integrating unlimited internal as well external data systems to make the overall platform more valuable. The company also embraces the Industrial Internet of things (IIoT) to further increase the timeliness and value of the data that ProcessMAP’s solutions provides.

Can you discuss the business impact of employing a predictive analytics solution for environmental health and safety? How much can companies save?

In the US alone, worker compensation drives both direct and indirect expenses total up to $250 billion annually. There are huge dollar opportunities to create significant return on investment for corporations as well as to dramatically improve the safety culture by employing a predictive analytics solution for environmental health and safety.

>See also: Disaster recovery — a best practice approach

What are some of the challenges in creating a predictive analytics solution for EHS?

ProcessMAP looks at both predictive solutions as well as having the system take actions based on certain sets of conditions to serve as smart agents and virtually extend the health and safety team.

One key challenge is helping customers to ensure existing information can be accessed and integrated into the ProcessMAP platform. ProcessMAP also urges customers to ‘think out of the box’ to allow the platform to truly leverage the data and insight it provides.

The UK’s largest conference for techleadership, TechLeaders Summit, returns on 14 September with 40+ top execs signed up to speak about the challenges and opportunities surrounding the most disruptive innovations facing the enterprise today. Secure your place at this prestigious summit by registering here

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Global Predictive Analytics Market to register $7.8 Bn By 2020 … – People Today 24 http://bigdatanewsmagazine.com/2017/06/21/global-predictive-analytics-market-to-register-7-8-bn-by-2020-people-today-24/ http://bigdatanewsmagazine.com/2017/06/21/global-predictive-analytics-market-to-register-7-8-bn-by-2020-people-today-24/#respond Wed, 21 Jun 2017 19:30:55 +0000 http://bigdatanewsmagazine.com/?p=118873 Post by relatedRelated post Zion Market Research has published a new report titled “Predictive Analytics Market by Software Solutions (Data Mining & Management, Decision Support Systems, Fraud & Security Intelligence, Financial Intelligence, Customer Intelligence and Others) for Customer & Channel, Sales and Marketing, Finance & Risk and Other Applications : …

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Zion Market Research has published a new report titled “Predictive Analytics Market by Software Solutions (Data Mining & Management, Decision Support Systems, Fraud & Security Intelligence, Financial Intelligence, Customer Intelligence and Others) for Customer & Channel, Sales and Marketing, Finance & Risk and Other Applications : Global Industry Perspective, Comprehensive Analysis And Forecast, 2014 – 2020” According to the report, the global predictive analytics market was valued at approximately USD 2.5 billion in 2014 and is expected to reach approximately USD 7.8 billion by 2020, growing at a CAGR of around 17.0% between 2015 and 2020.

Predictive analytics explore large quantities of data and past events to identify patterns and build forecasts about future events.  It is set of different techniques and technologies that include machine learning capabilities and improved big data aggregation technologies. Predictive analytics helps to solve real world’s problems in economics, business, government, and others. It is widely used in different sectors like financial, communication, retail and marketing organizations with a strong consumer focus.

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The predictive analytics market is segmented on the basis delivery mode including cloud-based and predictive on-premises installation. The cloud-based segment accounted for largest share of predictive analytics market in 2014. This segment is expected to witness significant growth in the years to come owing to increasing IT priorities among different enterprises over the past couples of years. This delivery mode has a much faster time to value which is a critical advantage for the approach.

Based on software solutions, predictive analytics market has been classified into customer intelligence, decision support systems, data mining and management, fraud and security intelligence, financial intelligence and others (Including performance management, campaign management etc.). Fraud and security intelligence and financial intelligence are key segments contributing to driving demand for predictive analytics.

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The predictive analytics market is segmented on the basis of end users including banking, financial services & insurance (BFSI), government, public administration & utilities, telecom and IT, healthcare, retail, manufacturing, and others (including energy, media, and entertainment, etc). Among all segment, banking, financial services & insurance accounted for major share and predicted to continue this trend over the forecast period. This growth can be mainly attributed to rising product quality in financial service sectors to deliver measurable results. Manufacturing and retail are other key end-use industries anticipated to grow at fastest CAGR over the forecast period.

Key application areas for predictive analytics includes customer & channel, sales and marketing, finance & risk and others. Finance & Risk was largest application segment with over 42.0% market share of total revenue generated in 2014. The predictive analytics helps to analyze fraud vulnerabilities and security breach. Marketing segment is anticipated to emerge as the biggest user of predictive analytics with cross-selling, campaign management, budgeting and forecasting models in the coming years.

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North America predictive analytics market led the global industry with share more than 55% in 2014. This region expected to continue its dominance in predictive analytics market over the forecast period. Europe was another leading regional market for predictive analytics. Deployment of IoT coupled with shifting trend towards smart cities is the key factor to propel the demand in this region. Asia Pacific is also expected to witness the fastest growth in the years to come as the region is anticipated to adopt Hadoop on a large scale with a rise in awareness.

Some of the key participants in predictive analytics market include Microstrategy Incorporation, SAP AG, Information Builders, Fair Isaac  Corporation, Tableau Software Inc., IBM Corporation, Microsoft Corporation, SAS Institute Inc, TIBCO Software Inc, Teradata Corporation and Oracle Corporation.

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The science vs. the art of predictive analytics techniques – TechTarget http://bigdatanewsmagazine.com/2017/06/21/the-science-vs-the-art-of-predictive-analytics-techniques-techtarget-3/ http://bigdatanewsmagazine.com/2017/06/21/the-science-vs-the-art-of-predictive-analytics-techniques-techtarget-3/#respond Wed, 21 Jun 2017 17:40:39 +0000 http://bigdatanewsmagazine.com/?p=118871 Predictive analysis utilizes many different statistical techniques, examining historical data in an effort to project future behavior. While past behavior may not be 100% indicative of future behavior — think of financial market analysis and how hard it is to pick the next great stock performer — it can help …

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Predictive analysis utilizes many different statistical techniques, examining historical data in an effort to project future behavior. While past behavior may not be 100% indicative of future behavior — think of financial market analysis and how hard it is to pick the next great stock performer — it can help contact centers work more smoothly and efficiently.

In this case, predictive analytics techniques can be wonderfully useful. However, it’s essential to know there’s a balance between “science” and “art” that each organization must discover to make its predictive analytics deployments successful.

Historically, contact centers have been using a form of predictive analytics in the area of workforce management for many years. Workforce management analyzes relationships between key pieces of historic data to project future staffing requirements.

With the increase in computing power, the retention of more data and enhanced statistical techniques, predictive analysis can now be used during specific “moments of truth” in the contact center.

Workforce management: The old standby

Predictive analysis may identify situations where customers who have used a specific product later return to purchase a complimentary product.

Predictive analysis is the fundamental concept behind workforce management systems that project future demand of resources by analyzing historical data. If a contact center does not have the staff in place to handle customer inquiries in a timely manner, overall customer satisfaction decreases — and that, in turn, drives down loyalty and customer retention.

The science. Workforce management looks at historical data regarding volume of calls, average handle time and other factors to project future call volume and gauge staffing requirements.

The art. Historical data may not be representative of the future. For example, in a certain year, the Fourth of July may fall on a Monday, and the following year, it may fall on a Tuesday. When looking at each year, either additional assumptions must be added to the model prior to engaging predictive analytics techniques, or the business must utilize judgment to make the appropriate adjustments to the outputs of the models once the forecast is developed.

Upselling: Getting more from each engagement

Customers often lack knowledge of additional relevant products or services that may be available from a company. Analytics can put the right suggestions in front of employees at the right time.

The science. By analyzing historical information, predictive analysis may identify situations in which customers who have used a specific product return to purchase a complimentary product. If the current caller has a pattern of behavior that is similar to previous customers, there’s a greater likelihood that caller may be interested in purchasing the same complimentary product. The current interaction provides an opportunity to proactively educate the caller of a potential need.

The art.Upselling is tricky in the contact center. Prior to embarking on an upsell opportunity, the contact center agent must resolve the customer issue to the caller’s satisfaction. Once resolution of the issue is complete, only then should the agent pursue an upsell opportunity if it makes sense. It takes training and practice to use the cues that analytics can provide.

Customer retention: Building loyalty

On many occasions, a change in behavior — such as buying less often — or a significant event — such as approaching the end of a contract — can indicate that a customer may discontinue doing business with an organization. Retention efforts often occur too late in a relationship with a customer. An interaction with the contact center may provide an opportunity to engage the customer before it’s too late — and help build upon an ongoing relationship.

The science. By analyzing historical information, predictive analytics techniques may identify patterns in which customers with similar behaviors have left an organization or a significant event is approaching. If a customer is approaching the end of a contract, a contact center agent can broach the subject with the customer and attempt to extend the contract.

The art.For effective customer retention, judgment must be used to ensure that the relationship between the customer and organization is still valued by both entities. If a customer has reduced or stopped using a specific service as a result of moving out of the service area, a company must allow that customer to leave without hassle.

Fraud intervention: Stopping loss sooner

Fraud can cost organizations a tremendous amount of money — and once it occurs, it can be very challenging to discover the criminal and recover losses. Predictive analytics techniques can help uncover potentially fraudulent activity by keeping an eye on trends that go against the norm.

The science. By analyzing historical information, predictive analysis may be able to identify spending patterns that are inconsistent with a customer’s previous activity. For example, if a customer normally makes one or two purchases in a month for $500 each and all of a sudden that customer makes 10 purchases in a two-day time period, red flags should be raised and a special group of contact center agents should reach out to the customer to confirm the purchases were indeed made by that person.

The art.Don’t assume the customer is guilty. The outbound contact should be treated as a courtesy call and the contact should instill confidence in customers that the organization is looking out for them.

Making it work

The idea of predictive analytics use in contact centers has been around for a long time, starting with workforce management. But as the technology evolves and becomes more accurate, predictive analysis is becoming more and more useful for other tasks. As predictive analytics techniques continue to develop, there are many new opportunities to increase their use in the contact center for both inbound and outbound calls.

It’s critical to always add the human factor of customization and common sense to implementations of these increasingly powerful systems. It’s not the tool that guarantees success. Always keep in mind the necessary balance between the science of predictive analytics and the art of how and when to utilize that tool’s capabilities.

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With predictive analytics, clinician buy-in is more important than the algorithm – FierceHealthcare http://bigdatanewsmagazine.com/2017/06/21/with-predictive-analytics-clinician-buy-in-is-more-important-than-the-algorithm-fiercehealthcare/ http://bigdatanewsmagazine.com/2017/06/21/with-predictive-analytics-clinician-buy-in-is-more-important-than-the-algorithm-fiercehealthcare/#respond Wed, 21 Jun 2017 14:12:15 +0000 http://bigdatanewsmagazine.com/?p=118869 Predictive analytics can help solve some of healthcare’s most vexing problems, but only if clinicians are willing to use it. Demonstrating the value of predictive modeling for front-line clinicians and providing C-suite executives with measurable benefits are key to integrating analytics into a healthcare system, three researchers wrote in Harvard Business …

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Predictive analytics can help solve some of healthcare’s most vexing problems, but only if clinicians are willing to use it.

Demonstrating the value of predictive modeling for front-line clinicians and providing C-suite executives with measurable benefits are key to integrating analytics into a healthcare system, three researchers wrote in Harvard Business Review.

RELATED: Oscar launches machine learning tool to put relevant clinical insights in front of physicians

Based on interviews with 34 health system leaders, policy experts and vendors, analytics experts from Brigham and Women’s Hospital and Partners HealthCare highlighted the importance of implementation for both in-house and off-the-shelf solutions. Usually, it helps to have a clinical champion that can reach out to colleagues to demonstrate the value of the tool and promote valuable health IT solutions.

“A common reason these tools are underutilized is that frontline employees don’t fully understand their value,” the authors wrote. “Thus, successful programs start with a problem where predictive analytics can make a clear difference.”

Their assessment aligned with several healthcare organizations that have found success using predictive analytics by ensuring new solutions are meaningful for physicians. But there is a clear disconnect among many healthcare organizations. A recent survey showed just over 30% of hospitals are using predictive analytics currently, but 80% of executives believe it can improve patient care.

RELATED: Academic medical centers team up with Google to bolster machine learning and predictive analytics

A report released this week by the Stanford School of Medicine advocated for better data literacy among physicians since analytics is likely to become a core job function in the future. Stanford is one of several academic medical centers teaming up with Google to enhance healthcare analytics and predict hospitalizations or cardiac arrest.

Presenting hospital executives with quantifiable measures—in the form of quality improvement, lower costs or patient satisfaction—will help secure the funding necessary to maintain or enhance predictive analytics systems, according to the Boston researchers.

Hospitals have faced similar implementation struggles with artificial intelligence, which offers tremendous promise for the healthcare industry even as clinicians and executives are still wrapping their arms around the technology.

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The science vs. the art of predictive analytics techniques – TechTarget http://bigdatanewsmagazine.com/2017/06/21/the-science-vs-the-art-of-predictive-analytics-techniques-techtarget-2/ http://bigdatanewsmagazine.com/2017/06/21/the-science-vs-the-art-of-predictive-analytics-techniques-techtarget-2/#respond Wed, 21 Jun 2017 11:22:45 +0000 http://bigdatanewsmagazine.com/?p=118867 Predictive analysis utilizes many different statistical techniques, examining historical data in an effort to project future behavior. While past behavior may not be 100% indicative of future behavior — think of financial market analysis and how hard it is to pick the next great stock performer — it can help …

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Predictive analysis utilizes many different statistical techniques, examining historical data in an effort to project future behavior. While past behavior may not be 100% indicative of future behavior — think of financial market analysis and how hard it is to pick the next great stock performer — it can help contact centers work more smoothly and efficiently.

In this case, predictive analytics techniques can be wonderfully useful. However, it’s essential to know there’s a balance between “science” and “art” that each organization must discover to make its predictive analytics deployments successful.

Historically, contact centers have been using a form of predictive analytics in the area of workforce management for many years. Workforce management analyzes relationships between key pieces of historic data to project future staffing requirements.

With the increase in computing power, the retention of more data and enhanced statistical techniques, predictive analysis can now be used during specific “moments of truth” in the contact center.

Workforce management: The old standby

Predictive analysis may identify situations where customers who have used a specific product later return to purchase a complimentary product.

Predictive analysis is the fundamental concept behind workforce management systems that project future demand of resources by analyzing historical data. If a contact center does not have the staff in place to handle customer inquiries in a timely manner, overall customer satisfaction decreases — and that, in turn, drives down loyalty and customer retention.

The science. Workforce management looks at historical data regarding volume of calls, average handle time and other factors to project future call volume and gauge staffing requirements.

The art. Historical data may not be representative of the future. For example, in a certain year, the Fourth of July may fall on a Monday, and the following year, it may fall on a Tuesday. When looking at each year, either additional assumptions must be added to the model prior to engaging predictive analytics techniques, or the business must utilize judgment to make the appropriate adjustments to the outputs of the models once the forecast is developed.

Upselling: Getting more from each engagement

Customers often lack knowledge of additional relevant products or services that may be available from a company. Analytics can put the right suggestions in front of employees at the right time.

The science. By analyzing historical information, predictive analysis may identify situations in which customers who have used a specific product return to purchase a complimentary product. If the current caller has a pattern of behavior that is similar to previous customers, there’s a greater likelihood that caller may be interested in purchasing the same complimentary product. The current interaction provides an opportunity to proactively educate the caller of a potential need.

The art.Upselling is tricky in the contact center. Prior to embarking on an upsell opportunity, the contact center agent must resolve the customer issue to the caller’s satisfaction. Once resolution of the issue is complete, only then should the agent pursue an upsell opportunity if it makes sense. It takes training and practice to use the cues that analytics can provide.

Customer retention: Building loyalty

On many occasions, a change in behavior — such as buying less often — or a significant event — such as approaching the end of a contract — can indicate that a customer may discontinue doing business with an organization. Retention efforts often occur too late in a relationship with a customer. An interaction with the contact center may provide an opportunity to engage the customer before it’s too late — and help build upon an ongoing relationship.

The science. By analyzing historical information, predictive analytics techniques may identify patterns in which customers with similar behaviors have left an organization or a significant event is approaching. If a customer is approaching the end of a contract, a contact center agent can broach the subject with the customer and attempt to extend the contract.

The art.For effective customer retention, judgment must be used to ensure that the relationship between the customer and organization is still valued by both entities. If a customer has reduced or stopped using a specific service as a result of moving out of the service area, a company must allow that customer to leave without hassle.

Fraud intervention: Stopping loss sooner

Fraud can cost organizations a tremendous amount of money — and once it occurs, it can be very challenging to discover the criminal and recover losses. Predictive analytics techniques can help uncover potentially fraudulent activity by keeping an eye on trends that go against the norm.

The science. By analyzing historical information, predictive analysis may be able to identify spending patterns that are inconsistent with a customer’s previous activity. For example, if a customer normally makes one or two purchases in a month for $500 each and all of a sudden that customer makes 10 purchases in a two-day time period, red flags should be raised and a special group of contact center agents should reach out to the customer to confirm the purchases were indeed made by that person.

The art.Don’t assume the customer is guilty. The outbound contact should be treated as a courtesy call and the contact should instill confidence in customers that the organization is looking out for them.

Making it work

The idea of predictive analytics use in contact centers has been around for a long time, starting with workforce management. But as the technology evolves and becomes more accurate, predictive analysis is becoming more and more useful for other tasks. As predictive analytics techniques continue to develop, there are many new opportunities to increase their use in the contact center for both inbound and outbound calls.

It’s critical to always add the human factor of customization and common sense to implementations of these increasingly powerful systems. It’s not the tool that guarantees success. Always keep in mind the necessary balance between the science of predictive analytics and the art of how and when to utilize that tool’s capabilities.

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Cathay Chooses Honeywell Predictive Analytics for APUs – Aviation Today http://bigdatanewsmagazine.com/2017/06/21/cathay-chooses-honeywell-predictive-analytics-for-apus-aviation-today/ http://bigdatanewsmagazine.com/2017/06/21/cathay-chooses-honeywell-predictive-analytics-for-apus-aviation-today/#respond Wed, 21 Jun 2017 03:47:26 +0000 http://bigdatanewsmagazine.com/?p=118865 Photo: Honeywell Cathay Pacific is set to use Honeywell’s GoDirect Connected Maintenance program on its fleet of Airbus A330s, Honeywell said at the Paris Air Show. The auxiliary power unit solution is to be put on more than 60 of Cathay Pacific and Cathay Dragon’s A330s. During a trial with …

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Honeywell GoDirect

Photo: Honeywell

Cathay Pacific is set to use Honeywell’s GoDirect Connected Maintenance program on its fleet of Airbus A330s, Honeywell said at the Paris Air Show. The auxiliary power unit solution is to be put on more than 60 of Cathay Pacific and Cathay Dragon’s A330s.

During a trial with Cathay Pacific, the product demonstrated that it can reduce inoperative systems by up to 35%, Honeywell said. This directly reduces maintenance and aircraft downtime.

“Cathay Pacific is a leader in this shift to predictive flying. The aviation industry is going through a digital transformation sparked by the use of big data analytics and the Internet of Things. Honeywell is combining its mechanical heritage with access to better connectivity to help airlines achieve more predictive maintenance operations,” said Brian Davis, VP of airlines of Asia Pacific for Honeywell Aerospace. “During the trial, Honeywell’s GoDirect Connected Maintenance saved Cathay Pacific several-hundred-thousand dollars in operational and reactive maintenance costs per aircraft and reduced APU-related delay minutes by 51%.”

Honeywell said that Cathay Pacific is considering extending the program to its fleet of Boeing B777s.

The GoDirect Connected Maintenance program provides predictive fault analyses that have a false positive rate of less than 1%, according to Honeywell. The company said it looks to expand the program beyond APUs, to include wheels, brakes and environmental control systems.

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Eye on the exchanges: What predictive analytics tells us about drivers of health plan profitability – FierceHealthcare http://bigdatanewsmagazine.com/2017/06/20/eye-on-the-exchanges-what-predictive-analytics-tells-us-about-drivers-of-health-plan-profitability-fiercehealthcare/ http://bigdatanewsmagazine.com/2017/06/20/eye-on-the-exchanges-what-predictive-analytics-tells-us-about-drivers-of-health-plan-profitability-fiercehealthcare/#respond Tue, 20 Jun 2017 21:12:41 +0000 http://bigdatanewsmagazine.com/?p=118863 As insurers submit initial filings about how they plan to price their Affordable Care Act exchange policies next year and where they’ll offer them, both current political uncertainty and past profitability will factor into in their decisions.  But politics aside, what exactly are the factors that led some insurers to thrive …

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As insurers submit initial filings about how they plan to price their Affordable Care Act exchange policies next year and where they’ll offer them, both current political uncertainty and past profitability will factor into in their decisions. 

But politics aside, what exactly are the factors that led some insurers to thrive in the individual marketplaces while others lost millions of dollars? That’s precisely what Syed Mehmud, an associate of the Society of Actuaries and senior consulting actuary at Wakely, set out to uncover.

For two years in a row, Mehmud has conducted a study to determine the factors that drive financial success—and struggles—on the exchanges. The most recent results are based on 2015 EDGE server data covering about 5 million lives, which insurers supplied in exchange for insights about how their exchange business is faring relative to their peers.

In order to glean insights from so much information, Mehmud and his colleagues wrote their own algorithm geared toward parsing large-scale medical data and created an adaptation of a popular predictive modeling technique called decision premodeling.

“What we wanted to do was to analyze the information and see if we can find any patterns, some common threads,” Mehmud told FierceHealthcare.

The patterns they did find may be surprising to some. Here’s a look:

  • Sicker patients aren’t necessarily driving higher costs. Thank the federal risk adjustment program for that, according to Mehmud. What makes the program so powerful is that it’s a concurrent model rather than a prospective model, he said, meaning it moves more money around among issuers. The greater the number of patients with costly conditions—meaning higher risk scores—a health plan has, the more money it gets from the risk adjustment program, and vice versa. Thus, “sometimes it is actually the healthier patients and the younger patients that are more unprofitable, because they have fewer medical conditions,” Mehmud said.
  • Not all metal levels are created equal. Plans with richer benefits, like gold and platinum, struggled in the individual marketplaces in 2015, Mehmud noted, but oftentimes thrived in the small-group market. That goes to show “that the dynamics of the individual and small-group markets are very different,” he noted.
  • Contracting plays a key role in profitability. In Mehmud’s project, he repriced claims to a percentage of the local Medicare rates in order to compare across the board what individual market insurers were paying for services. Not surprisingly, he found a considerable spectrum of contracting rates—and more profitable health plans tended to have negotiated better rates. While provider contracting doesn’t get as much “air play” as other factors, the study showed that it’s an “important ingredient in the success of an ACA individual market participant, and carriers should pay attention to that,” Mehmud said.
  • Command of data can make or break a health plan. Unlike in Medicare, where plans had some time to get used to risk adjustment as the program ramped up, in the commercial market, “a switch turned on,” Mehmud noted. So the carriers that weren’t paying good attention to their data did not fare well in 2014, and that pattern continued in 2015, with “tens of millions of dollars left at the table” among carriers that failed to adequately code for risk. Health plans, Mehmud concluded, “doesn’t necessarily win by making sure that their data is correct, but definitely lose if they don’t.”

One caveat to the patterns that Mehmud observed, however, is that they don’t apply to all insurers. Therefore, he recommended that insurers study their own data to determine strategy shifts tailored to their individual organizations.

“Every carrier has a different story to tell,” he said.

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Opportunities for Predictive Analytics in LatAm Land Development – Nearshore Americas http://bigdatanewsmagazine.com/2017/06/20/opportunities-for-predictive-analytics-in-latam-land-development-nearshore-americas/ http://bigdatanewsmagazine.com/2017/06/20/opportunities-for-predictive-analytics-in-latam-land-development-nearshore-americas/#respond Tue, 20 Jun 2017 19:12:19 +0000 http://bigdatanewsmagazine.com/?p=118861 Over the past few decades, I’ve developed an increasingly keen interest in studying the role that certain policies and analytics technologies have played in influencing the economic health and well-being of Latin America’s many different economies. Of course, the region has experienced quite a bit of upheaval during that time …

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Over the past few decades, I’ve developed an increasingly keen interest in studying the role that certain policies and analytics technologies have played in influencing the economic health and well-being of Latin America’s many different economies.

Of course, the region has experienced quite a bit of upheaval during that time — not to mention the widespread disruption that has taken place over the past century – and, in many cases, any ongoing upheaval can be either directly or indirectly attributed to specific economic policy measures.

Due to its history of widely varied and relatively unique economic practices and policy measures, Latin America is a particularly fascinating case study when it comes to land development. This includes the potential role that predictive analytics can have on this niche.

Predictive analytics tools have achieved an impressive level of sophistication, enabling dynamic and detailed analyses based on the full range of information available, making them an ideal fit for land development companies.

An Untapped Opportunity

Given the wide variances in policy measures that have extended back several decades and influenced local and national economies throughout Latin America, it appears quite likely that the use of predictive analytics offers a wealth of opportunities to help ensure positive economic outcomes benefiting people throughout the region.

During my time in the industry, I’ve always emphasized the value of examining the full range of the potential impacts generated by a particular land development project, including analyses on the short- and long-term economic impact on the region. In order to accomplish this, I’ve often turned to data analytics tools that incorporate any relevant data to predict a comprehensive range of outcomes associated with the project.

It is my belief that a similar approach can be readily applied for the purpose of predictive analyses within the more underdeveloped areas of Latin America – an opportunity that data analytics vendors, and even software development vendors, should be preparing to seize.

Data Analytics is Essential

With such widespread and increasing outside interest in land development opportunities throughout the whole of Latin America, it will be interesting to determine how certain market-oriented policy measures and reforms — including land banks, for example, or even individual land titling — have influenced local and national economies in the past, and also how those policies might influence the outcomes generated by any future land development efforts. The same is true for other policy measures, including, for example, re-distributive land reforms or sustainable land use and development.

Anyone who has conducted even a cursory examination of the rural regions of Latin America recognizes the potential associated with the land development opportunities as they currently exist, but a more in-depth analysis is still necessary to ensure any future land development projects are executed in a way that yields economic benefits for anyone in the region who might be affected in one way or another.

It is for this reason that it remains necessary to utilize thoughtful, detail-oriented assessments that rely on research, consider past policy outcomes, and incorporate predictive analyses capable of considering the total impact of a land development project undertaken within the region.

It is all too easy to be blinded by excitement over the mere existence of such a unique and potentially valuable opportunity, and surely there are many land developers outside of Latin America ready to leap at the chance to undertake a land development project in the region as soon as possible.

If State-side companies begin to recognize this opportunity in Latin America, you can be sure they’ll be looking for Nearshore vendors to develop the tools to achieve it.

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