“Didn’t you just go to a similar Big Data conference recently?” my wife asked me. “How much could have changed in a few months?” I was hesitating about attending another conference in a short time span. My wife is right about most things, but in this case, I am glad I didn’t listen and went anyway. I learned about many new advances in both commercial and open source tools and across the whole technology stack: new hardware, in-memory databases, and new-and-improved tools.
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.
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.
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.
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.
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.
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.
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.