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How to Nurture Existing Customer Relationships to Drive Revenue Growth

Posted by John Mack on Tue, Jul 26, 2016

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

This post is the fourth in a four-part series. The third installment, “Existing Customers vs. New Customers — Exploring the Road Less Traveled,” discussed aspirational value and Big Data analytics’ role in attaining it. Here, we’ll discuss how all the elements described in this series fit together to drive revenue growth.

Businesses that overemphasize or exclusively focus on new business development to drive revenue growth are missing a substantial opportunity: their existing customers. Information about a business’ existing customers already resides in multiple areas of the overall corporate database, not just on a business development list. These customers’ profile information, consisting of demographic and psychographic details, preferences, and behaviors is there, offering a data picture that is far richer than the picture associated with prospective customers.

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Topics: Big Data, Signal Hub Technologies, Marketing

Flipping the Model: Successful Retailers Tailor Campaigns to Each Customer's Needs

Posted by John Mack on Tue, Jul 19, 2016

With 2016 already halfway over, retail marketers are deep into their 2016 campaigns, which are intended to acquire and retain customers, drive sales, and improve overall customer loyalty. But what are marketers doing differently to make 2016 better than 2015? Last year, the Commerce Department recorded only a 2.1% increase in retail sales (excluding automotive) over 2014 — marking the worst such performance since 20091 and a far cry from the 4.1% increase that the NRF projected2. If retailers haven’t changed the way they approach marketing, are we in for more of the same?

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Topics: Big Data, Marketing

Existing Customers vs. New Customers — Exploring the Road Less Traveled

Posted by John Mack on Tue, Jul 12, 2016

Part 3 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the third in a four-part series. The second installment, “Big Data Analytics: Necessary but Not Sufficient,” discussed three new ways companies can use Big Data analytics to improve a business’ ability to consistently improve revenue growth from existing customers. Here, we’ll discuss aspirational value and Big Data analytics’ role in attaining it.

People sometimes fail to notice opportunities that later seem obvious. Even when we do identify such opportunities, we may neglect to pursue them, or we may take an ineffective approach to pursuing them. One example of this phenomenon is how businesses think about driving revenue growth.

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Topics: Big Data, Signal Hub Technologies, Marketing

Making Big Data Smarter: How to Move from Raw Data to Real Value at Scale

Posted by Anatoli Olkhovets on Wed, Jul 06, 2016

In a previous post, “5 Obstacles to Achieving Scalable Data Science, and How to Overcome Them,” we talked about perspectives distilled from hundreds of conversations with our customers and partners and the challenges they face in trying to achieve a scalable data science capability. All of these customers have an extensive backlog of ideas, but they struggle to convert these ideas into actual use cases, or mini-applications, that can run in a production environment and generate real business value. These businesses universally encounter the following key obstacles:

(1) They have too many tools and technologies to manage effectively.

(2) Data is everywhere, but deriving value from it is extremely difficult.

(3) The traditional “artisan” approach to use cases severely limits the number of business problems they can solve.

(4) Operationalizing data science, with hundreds of models in production, is extremely difficult.

(5) Companies are willing to experiment but are afraid to make the long-term commitment necessary to foster widespread adoption.

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Topics: Big Data, Signal Hub Technologies

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

Grow Revenue by Using Big Data More Intelligently

Posted by John Mack on Fri, Jun 17, 2016

Go Beyond the Symptoms: How to Overcome Revenue Growth Challenges

Part 1 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the first in a four-part series. Here, we discuss the key signs of slowing revenue growth and the first steps organizations can take to turn it around.

It isn't hard to Identify the symptoms of declining revenue growth. Often, more than one of the classic signs will be evident at any given time. A declining growth rate or a shortfall in revenue vs. the target level are perhaps the most obvious indicators. Operating expenses growing faster than revenue can be yet another strong harbinger of revenue growth challenges, as well as a potential indicator of a cost structure that is no longer aligned with the business’ sales capabilities. While they require more research and calculation to derive, declining market share or shrinking revenue growth rates vs. competitors are other telltale signs.

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Topics: Big Data, Signal Hub Technologies, Marketing

Customer Aspirational Value – A First Principle for Business

Posted by Georges Smine on Fri, Jun 10, 2016

Conventional wisdom leads you to think that when a company knows its customers, it ends up providing better service, increasing loyalty, and generating more sales. Right?

Yes, but the truth lies between what is desired and what is achieved.

Opera Solutions has been helping Fortune 500 companies apply Big Data analytics to address challenges in operations, sales, and marketing, among other business functions. In today’s business climate, one of the most relevant challenges is how businesses can most effectively grow revenues from their existing customers. We have chosen this to be the topic of our next webinar.

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Topics: Signal Hub Technologies, Marketing

5 Key Big Data Trends to Watch

Posted by John Mack on Tue, Feb 23, 2016

As a company that works intensively with Fortune 500 companies, our finger is firmly on the pulse of the latest needs, wants, and aspirations of the world’s biggest Big Data drivers. Here are five key trends we’re seeing for 2016 and beyond.

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Topics: Big Data, Machine Learning

5 Things to Consider When Buying a Big Data Solution

Posted by Abhishek Rathi on Thu, Dec 17, 2015

Any time you sign onto a long-term agreement for your company, you're making a major commitment. These five critical considerations should make the decision-making process a little easier.

Buying Big Data software and services is a Big Deal — regardless of the size of your organization. The category is ambiguous: There are too many providers offering too many flavors of Big Data software and services, and implementing the wrong or inadequate tools and technologies can derail your efforts. In addition, technology and business needs are evolving quickly, so companies need a solution that is agile and doesn’t become obsolete in five years. In the same vein, once a certain path is chosen and a significant investment is made, companies may be stuck with that decision for years to come. And finally, while your organization likely knows it needs a Big Data solution, those who make the purchasing decisions may not have a full understanding of all the disciplines involved in deploying one to fully exploit its benefit and transform your business.

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