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How to Recruit Data Scientists

Posted by Tim Bridges on Tue, Feb 07, 2017

A guide from the head of HR at a leading analytics company

Data scientists are in higher demand than ever before. According to the latest CrowdFlower survey, 79% of respondents reported a data scientist shortage in 2015. In 2016, that number grew to 83%. The race is on to find skilled people who can organize, structure, and make business sense out of Big Data sets. People with heavy STEM, analytics, and conceptual skills, and the attendant work-friendly personality traits (insatiable curiosity, ability to prioritize, and a healthy dose of skepticism, to name a few), can virtually write their own tickets.

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Topics: Big Data, Data Science, Analytics

How to Transform Marketing with the Membership Economy and Advanced Analytics

Posted by John Mack and Sarah Anderson on Wed, Nov 30, 2016

Everyone wants to belong. But how can that basic human need coexist with the commercial needs of a business so that both the customer and the business find the relationship beneficial? Big Data analytics makes it possible while also opening new possibilities.

History is replete with examples of human beings finding ways to connect with one another. We form tribes, congregations, clubs, and entire societies. We develop communications channels and pass specialized content through those channels. Even efforts to divide these groups and disrupt these channels simply engender new ones and actually help strengthen our identity as individuals. This pattern has continued to evolve with the advent of the digital age and extends to peoples’ relationships with products and services. Are you loyal to Mac or PC? Do you use Facebook or Instagram? Are you enrolled in Amazon Prime? Are you a Netflix subscriber? This need to identify one’s self with a larger group is a primal human instinct no matter how contemporary the group.

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Topics: Big Data, Analytics, Marketing

CLV and Clumpy Customers: Why Clumpy Behavior Is Changing the Customer Lifetime Value Equation

Posted by Michael Turon on Fri, Sep 23, 2016

Calculating customer lifetime value has been a critical part of the marketing process for years, but new technologies are changing consumer behavior, and marketing professionals need to catch up.

Ever have a weekend where you just can’t stop spending money? Maybe you’re using some free time to stock up, or you’re decorating a room or planning a party. Or maybe you’re not spending more but just consuming more — a good television or movie series or, for example, plowing through a good book on your Kindle. This kind of behavior is called “clumpiness.” And while it’s not necessarily new, new technologies — particularly in the world of digital content — have shed light on this type of behavior for all industries. The problem is that it hasn’t been included in the formula marketers have been using for decades.

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Topics: Signal Hub Technologies, Analytics, Marketing

Data Science for Business Users — An Overview of Popular Techniques

Posted by Anatoli Olkhovets on Thu, Aug 04, 2016

Everybody seems to be talking about machine learning these days, and a quick check of Google returns 21.2 million search results for the term. Clearly, this is a popular topic. Yet the term “machine learning” can have many different meanings, depending on the context in which it is being discussed. It is also associated with an equally lengthy list of data science techniques and technologies. Business leaders often feel overwhelmed by this rather bewildering array of terms, analytical approaches, and technology solutions. There are hundreds of algorithms, with new variants seemingly appearing every day. Researching them online does not seem to clarify the choices or point to an obviously superior decision, as most articles target deep experts and revolve around nuances of a particular model or open source package. As a result, business leaders can be reluctant to adopt something they don’t fully understand, resulting in missed opportunities.

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Topics: Data Science, Machine Learning, Analytics

Big Data Analytics: Necessary but Not Sufficient

Posted by John Mack on Thu, Jun 30, 2016

Part 2 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the second in a four-part series. The first installment, “Go Beyond the Symptoms: How to Overcome Revenue Growth Challenges,” discussed the key signs of slowing growth and the first steps organizations can take to turn it around. Here, we’ll discuss three new ways in which companies can use Big Data analytics to improve a business’ ability to consistently improve revenue growth from their existing customers.

Big Data analytics in 2016 occupies roughly the same spot in the corporate consciousness as did the concept of cloud computing in 2008. By now, every world-class company that generates vast quantities of data has recognized that this data has exceptionally high value as an asset. These companies have made technology investments accordingly, procuring software solutions to organize, analyze, and manage the data, storage solutions (cloud or on-premise) to facilitate access to and distribution of the data, and often also professional services to enable and operate this infrastructure.

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Topics: Big Data, Signal Hub Technologies, Analytics

5 Obstacles to Achieving Scalable Data Science, and How to Overcome Them

Posted by Anatoli Olkhovets on Wed, Jun 22, 2016

The struggle is real — and it’s becoming increasingly apparent to companies that have dipped their toes into popular data science tools. As enterprises test the limits of their new tools, old technology, and data scientists’ time, their infrastructure is starting to show its cracks. Read on to see how these issues are revealing themselves — and more importantly — gather some ideas on what to do about it.

Over the past year, I have been averaging 2–3 customer meetings per week, resulting in over 100 customer and partner conversations around Big Data, analytics, and data science for the enterprise. From these conversations, I have found one key recurring theme: scale. Large enterprises no longer want to build one model quickly or implement just one use case in production. They all struggle with a large backlog of ideas. They need a way to rapidly turn these many ideas into real use cases that deliver tangible business value.

However, many companies simply can’t find a pathway to make this happen. Across my numerous conversations, I noticed very similar patterns and identified 5 common obstacles that can prevent companies from achieving scale for data science.

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Topics: Big Data, Data Science, Signal Hub Technologies, Machine Learning, Hadoop, Analytics, Spark

Analytics: How to Predict Future Behavior

Posted by Alex Guazzelli on Thu, Jun 25, 2015

Signals empower us to predict the future by learning from the past. 

We all learn from the past. So if you failed a mathematics exam in school, you learned you had to study harder not to fail the next one. Events that happened in the past can be measured in many different ways. Measuring a past event puts it into focus. In data mining, this process translates into creating a descriptive feature to describe or tell a story about the past in some way, shape, or form.

We call these descriptive features Signals. Signals are the indicators extracted from raw data that have proven to be valuable for solving a particular problem. Signals can also be created from other Signals by transforming one piece of information into something more meaningful or interesting. For example, using the initial Signal extracted from raw data — in this case, a test score — we can create several Signals that capture past school performance in mathematics and science for a certain student.

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Topics: Signal Hub Technologies, Analytics, Signals

How an Integrated Data Analytics Platform Can Help Tear Down Silos and Spark Collaboration

Posted by Jon Lexa on Thu, Jun 04, 2015

Are your employees communicating effectively? Find out how one company got everyone speaking the same language.

The larger a company gets, the more siloed it can become, and relying on happy hours, retreats, or top-down mandates are not enough to break these silos down. No matter how hard leadership tries, people have a difficult time getting on the same page when they all have different business objectives. And no company is immune. Even Apple has struggled with silos. Steve Jobs, in an effort to deconstruct silos, set up Apple's headquarters to encourage "collisions," or interactions between people who may not have necessarily interacted. 

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Topics: Signal Hub Technologies, Analytics

The Realization of Personalized Marketing

Posted by Sarah Anderson on Thu, May 07, 2015

2015 could be the year that true personalization finally takes hold, but to achieve it, you’ll need to change the way you think about marketing.

Customers expect to be treated as individuals. They require relevancy and prefer having a relationship with a company that keeps track of their preferences, purchases, and correspondence. Customers are ready for personalized marketing, and now, finally, more and more companies are, too.

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Topics: Analytics, Marketing

How Telcos Are Using Signals to Drive Savings and Revenues

Posted by Ankur Desai on Thu, Sep 25, 2014

When to nudge a delinquent customer for a payment — that is the question. Or rather, that is one of many — many — questions that Signals are helping telecom companies (telcos) answer. Because when to call and ask for payment can actually help determine whether to call at all, and eliminating calls can lead to significant savings. So how do Signals do this? And more important, how can telcos, specifically, take advantage of these Signals to improve customer experience and maximize bottom-line impact?

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Topics: Analytics, Signals, Marketing, Supply Chain & Operations