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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

Data Science: Recommender Engines with Collaborative Filtering

Posted by Daniel D. Gutierrez on Mon, Jun 02, 2014

Ever wonder how services like Netflix or Pandora choose media to suggest to you? If you’ve been reading this blog for a while, you’re familiar — at least a little bit — with recommender engines. In our post “How Machine Learning Will Affect Your Next Vacation,” we talked about the impact machine-learning recommender engines have on regular consumers. But here, we want to dive deeper and talk about the math and science behind recommender engines.

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Topics: Data Science, Analytics, Marketing

Data vs. Gut Instinct: How Will You Drive Your Business?

Posted by Daniel D. Gutierrez on Tue, May 20, 2014

Corporate executives make dozens of business decisions every day — most of which are invisible to the general population. But one business decision of late stands out as a stark exception: CNN’s decision to focus on missing Malaysian flight 370 (MH370) long after other news sources moved on. Some CNN watchers grew tired of the endless coverage, especially as other big stories fought for attention elsewhere. Yet CNN seemed to be stubbornly obsessed with the missing flight. For the first time in a long time — possibly ever — people were questioning why an entire network was ignoring major human interest stories — including a sunken ferry with nearly 300 teenage casualties in South Korea and 200 kidnapped schoolgirls in Nigeria — in favor of one human interest story that was no longer news. CNN even became the butt of jokes for its coverage.

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Topics: Big Data, Analytics

What Is Data Science?

Posted by Daniel D. Gutierrez on Tue, May 06, 2014

As a relatively new term, “data science” can mean different things to different people due in part to all the hype surrounding the field. Often used in the same breath, we also hear a lot about “big data” and how it is changing the way that companies interact with their customers. This begs the question — how are these two technologies related? Unfortunately, the hype often masks reality and worsens the Signal-to-noise ratio when it comes to our increasingly data-driven society. Rest assured, there truly is something deep and profound representing a paradigm shift in our society surrounding data, but the hype isn’t helping to clarify data science’s exact role in Big Data. In this article, we strive to put to rest many of the misunderstandings surrounding data science.

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Topics: Data Science, Machine Learning, Analytics

How Data Science Impacts Intelligent Retargeting

Posted by Daniel D. Gutierrez on Thu, Apr 24, 2014

Conversion rates for online retailers are generally considered pretty dismal by most common measures. Imagine if only 1–3 percent of shoppers entering your store ended up making a purchase. Maybe you’d think of trying a new strategy. The new strategy employed by many online retailers is called retargeting — the use of search and display campaigns to target the 97 percent of visitors who came to your e-commerce site but didn’t convert, meaning they did not make a purchase, fill out a form, or request a demo or call. Retargeting works by keeping track of people who visit your site and displaying your retargeting ads to them as they visit other sites online.

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Topics: Big Data, Data Science, Analytics

Is Big Data the Problem or the Solution?

Posted by Todd Higginson on Fri, Apr 18, 2014

Be careful what you wish for. Most of us probably heard that phrase at some point during our childhood, or perhaps even more recently. The point is valid. When wishing for something we tend to focus on the positives while ignoring the potential negatives. After all, who would wish for something that had a downside?

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Topics: Big Data, Data Science, Analytics

Increase Revenue and Improve Business Strategy with Retail Analytics

Posted by Daniel D. Gutierrez on Mon, Apr 07, 2014

This beginner’s guide to retail analytics will help you get the most out of the data you’re probably already collecting.

When people shop online, they know they’re sharing personal data to be collected and used for marketing purposes. And yet we’re surprised by how many companies fail to do even the bare minimum of analysis with the data they’ve painstakingly gathered. By implementing just a few basic analytics techniques into your marketing practices, you can quickly gain insight into your site’s performance, your customers’ satisfaction, and the effectiveness of your marketing campaigns. And if you take your analytics a few steps further, you can actually predict what your customers want and gain specific recommendations about how best to market to them — whether you have hundreds of customers or millions of them.

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Topics: Big Data, Data Science, Analytics, Signals, Marketing