Archive for 2013

 

Quality Customer Service Requires Predictive, Real-Time Insight

Friday, December 13th, 2013 by

In today’s increasingly crowded and fast-paced business world, organizations are continually pressured to meet the rapidly evolving needs of prospective and existing customers. In many cases, embracing Big Data strategies will enable firms to collect large amounts of information, analyze those assets, and turn them into meaningful insights that will help build better relationships with customers, ensure retention, and enhance loyalty.

Quality customer service requires predictive, real-time insight

Quality customer service requires predictive, real-time insight

Businesses that don’t provide customers with a unique, holistic experience will feel the repercussions of not doing so faster than ever. The proliferation of social media and other highly collaborative web services gives consumers a booming voice over the Internet. This phenomenon is encouraging organizations to adopt more innovative customer service programs that allow decision-makers to understand what customers want before those demands are requested. The only way to achieve these capabilities is through the use of Big Data analytics.

The real-time necessity
Executives, Support staff, and other customer service representatives need the ability to identify poor experiences as quickly as possible because waiting too long and allowing customers to leave an interaction on a bad note can results in major long-term consequences. Although monitoring conversations between corporate employees and consumers can help decision-makers gain more insight into how their agents are handling queries, this practice doesn’t necessarily provide the time needed to make adjustments before it’s too late.

Rather than taking this traditional reactive approach, companies should become proactive. Launching predictive analytic initiatives to collect large volumes of data on prospective and existing customers can help identify current and future trends. This information gives decision-makers a unique perspective on what customers actually want, making it easier to meet (and hopefully exceed) expectations while developing a reputation for responsiveness, innovation, and ease of use.

Overcoming unforeseen hurdles
Big Data technologies hold the promise of gaining a deeper understanding of customer behavior. However, decision-makers must guard against being too ambitious by collecting every scrap of information available in the hope that a certain piece will provide insight into a certain process. Embracing Big Data in this way can potentially lead to performance and efficiency problems, even if a company uses cloud computing technologies to match the scalable needs of increasingly large and complex information sets.

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Using Big Data to Improve Customer Metrics

Thursday, December 5th, 2013 by

Although Big Data projects can be helpful to many organizations, launching these initiatives using the wrong metrics can lead to poor practices. And even though most experts agree these endeavors aren’t a panacea, business decision-makers continue to hastily adopt strategies that increase challenges.

Using big data to improve customer metrics

Using Big Data to improve customer metrics

An Enterprise Apps Today report, which highlighted the mindset of customer analytics expert Larry Freed, noted that the gathered data by itself won’t necessarily provide firms with opportunities. Businesses must also embrace the right tools to take advantage of those digital resources.

Freed believes that the variety of information being collected will give organizations the biggest challenge when adopting Big Data endeavors because some of the information being gathered isn’t always accurate.

“If I am an anonymous visitor to a website on my tablet, my home PC, and my work PC, companies are not going to have the ability to recognize that I am the same person,” Freed said, according to Enterprise Apps Today. “Companies are gathering enormous amounts of digital data, but it can accelerate some bad practices.”

In other words, context is everything. Companies need to proactively establish parameters to ensure they understand prospective and existing customer behavior and have the tools needed to turn activity into useful information. Doing so, however, may require the development of new metrics.

More detail is needed
In the past, businesses would often use generic metrics that would put customers in one of three categories based on their response to whether or not they would recommend a company to other people, Freed asserted. The classifications — promoter, passive, or detractor — would then tell decision-makers how “loyal” clients are. During the past several years, however, executives have learned that these categories aren’t entirely accurate, which also means that the strategies built around this segregated system weren’t always effective.

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Big Data’s Theory of Relativity

Monday, December 2nd, 2013 by

During the past several years, Big Data has made headlines, entered boardrooms, and been prioritized by companies of all sizes in the hopes that their processes will present new opportunities for long-term success. In many cases, the programs will enable organizations to experience financial, operational, and managerial benefits much as squeezing juice from a handful of data-filled oranges will provide the much-needed drink for any thirsty business.

Big data's theory of relativity

Big Data’s theory of relativity

Still, executives can’t approach Big Data with the mindset that virtually any project will suffice. In fact, if firms take this route, they’ll often find themselves overburdened with complex information that doesn’t necessarily yield any advantages. IT and business decision-makers need to establish Big Data initiatives with their companies in mind because implementing a generic house-blend of products and strategies won’t respond to the unique challenges and demands faced by most organizations.

Although there are many important factors to prioritize when building a Big Data program, two in particular should be emphasized: Understanding the business and prioritizing quality over quantity.

Understand the business
If Big Data executives in large enterprises or small organizations don’t fully understand the needs of their specific companies, they’ll find it increasingly difficult to implement the tools, technologies, and processes necessary for employees to complete operations in a timely and efficient manner. Breaking down Big Data projects into smaller segments parsed out to multiple departments can help firms embrace more relevant information that augments key procedures.

Marketing, sales, and other decision-makers who intend to embrace Big Data must understand their core principles and needs if they want to ensure their endeavors are as effective as possible. If executives recognize the direction they want to travel, building an analytics program that caters to those specifications will be much easier, regardless of the volume of information those departments are responsible for managing.

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Implementing Big Data in the Cloud: 3 Pitfalls that Could Cost You Your Job

Monday, November 25th, 2013 by

In IT departments around the globe, CTOs, CIOs, and CEOs are asking the same question: “How can we use Big Data technologies to improve our platform operations?” Your particular role could be responsible for solving for a wide variety of use cases ranging from real-time monitoring and alerting to platform operations analysis or behavioral targeting and marketing operations. The solutions for each of these use cases vary widely as well. But no matter which Big Data solution you choose, make sure you avoid the following 3 pitfalls.

Pitfall #1: Assuming a single solution fits all use cases

In a recent post, Liam Eagle of 451 Research looked at GoGrid’s Big Data product set, which is purpose-built for handling different types of workloads. He noted that variety is the key here. There isn’t a single one-size-fits-all solution for all your use cases. At GoGrid, for example, many of our Big Data customers are using 3 to 5 solutions, depending on their use case, and their platform infrastructure typically spans a mix of cloud and dedicated servers running on a single VLAN. So when you’re evaluating solutions, it makes sense to try out a few, run some tests, and ensure you have the right solution for your particular workload. It’s easy for an executive to tell you, “I want to use Hadoop,” but it’s your job that’s on the line if Hadoop doesn’t meet your specific needs.

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As I’m sure you already know, Big Data isn’t just about Hadoop. For starters, let’s talk about NoSQL solutions. The following table lays out a few options and their associated use cases to help illustrate the point.

Solution Common Use Cases Pros and Cons
Cassandra (more…) «Implementing Big Data in the Cloud: 3 Pitfalls that Could Cost You Your Job»

How To Deploy an SSD Cloud Server in 5 Minutes on GoGrid

Wednesday, November 20th, 2013 by

GoGrid’s solid state disk (SSD) Cloud Servers are the next evolution in cloud servers. With 10 Gbps public and private network connectivity, RAM allocations of up to 64 GB, 40 cores, up to 2,199 GB of persistent storage, plus the ability to provision up to 12 TB block volumes, these SSDs are designed to solve for most high I/O applications. You can find out more about our SSD Cloud Servers in a video we posted on YouTube.

In this post I’ll walk you through the basics of deploying an SSD Cloud Server on GoGrid. Like everything we deliver as a service, deploying SSD Cloud Servers through the GoGrid management console is simple and easy. Let’s get started.

I begin by logging into my.gogrid.com with my user name and password. If you don’t have one, just sign up for a GoGrid account by clicking here.

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Once in the management console I’ll select the +add button to begin setting up my server deployment. By selecting +add , I can deploy a variety of compute, network, and storage options.

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