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.

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.

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.

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.

Most companies realize they are sitting on a treasure trove of customer data that has the potential to deliver tremendous business benefits; however, most also have no idea how to realize those benefits. How can companies use their data to bring in more customers, increase the amount they spend, and make them more loyal? How can companies use data to turn unhappy customers into loyal champions of the brand? And perhaps most important, how can companies use that data to drive a significant increase in revenue?  

Have you ever wondered how Google or Hotmail finds and blocks a single spam email out of billions of emails? Or how companies analyze tweets for customer sentiment? Or how questionable content is identified on a Website? Natural language processing (NLP) does this in real time — and it can be used for a lot more than spam filtering.

When it comes to analyzing a company's spend, the old adage "what gets measured gets managed" is definitely true. However, measuring an enterprise's spend when you have free-text fields, or fields where employees can type in any response they want, can be an insurmountable task. Rarely do two people use the same words and phrases to describe the same thing. Even spelling varies. These variations make measuring how much is being spent on a given category or with a given vendor a very common challenge.