The concept of Big Data" has sales and marketing executives abuzz over its potential, but the approach to date has been inadequate. Earlier Big Data strategies and tools have focused on collecting as much data as possible, and shaking it all up in hopes that something useful would fall out. In that sense, the term "Big Data" is perhaps less useful than would be the term "right data."
Modern analytics must go beyond quantity of data and the resulting "needle in a haystack" approach to deriving value; rather, a focus on accuracy hones in directly on the data that is most important to the company's strategic goals and missions. A purpose-driven methodology behind Big Data and analytics can transform a Big Data environment into one that delivers defined results and increased value. This starts with using Big Data to gain a deeper understanding of the essence of the enterprise, its goals and how to achieve specific outcomes.
In a sales and marketing environment, defined goals for a Big Data project includes acquisition, retention, cross-sell, up-sell, profitability & LTV. However, these outcomes must not be seen in isolation, and each must be seen in a broader context. Each outcome contributes to a greater whole that contributes to the overall marketing and sales objective of achieving customer equity.
Is There Such a Thing as Too Much Data?
Over the last decade, changes in technology have begun to generate unbelievable amounts of data. According to IBM, 90 percent of the world's data has been created in the last two years. In manufacturing, for example, sensors collect data minute-by-minute. Marketing organizations gather excruciatingly detailed information about customers, and how, what and why they buy. But the ability to gather massive quantities of data has outpaced the ability to do something useful with all that data.
An outcomes-driven approach makes sense of the data overload. The correct approach begins with changing the focus of data gathering. Large amounts of data are essential, but it's even more essential to make sure the right type of data is being analyzed and applied. The process of data analysis begins by focusing on strategic objectives, and before anything else, answering the question: What data is meaningful and actionable in order to achieve our strategic goals and answer specific questions?
Seven Steps to Success
The purpose of Big Data is to gain a deeper understanding of your business. Maintaining a steady level of sales, modest growth or even not having any losses will often lead to stagnation and a resistance to "dive deeper" into really understanding the essence of the enterprise and what makes it grow and prosper. Beginning with a tight focus on a specific objective, the seven steps are:
Focus on an objective. Before implementing a Big Data strategy, understand what you hope to achieve with that data.
Think process and optimize value drivers.What specifically drives value for your customers and what are the building blocks of that value?
The "right" data trumps "big" data.Understanding the value drivers informs the type of data that must be analyzed.
Tactics to monetize your predictions.Move those analytics-driven predictions out of the analyst's office and into operations. Make them happen.
Operationalize your predictive and prescriptive insights in a timely manner.Time is not on your side. Once you have gained insights into your customer base and what makes them buy, don't hesitate to take action. External factors change quickly.
Your business changes daily, so does your data.Big Data isn’t just historical. Constantly changing input means you need a real-time view, and the ability to switch tactics in response to what the analytics are telling you.
Extend your analytics to achieve customer equity.Finally, the end goal of analytics is achieving customer equity (acquisition, retention, cross-sell, up-sell, profitability and LTV).
Deriving the greatest and most focused value from Big Data also comes from the realization that business is never a static proposition. Input changes daily, with factors as specific as product availability and as broad as macroeconomic trends all coming into play. As such, a pure historic snapshot is inadequate – analytics needs to provide a live, real-time view of the future based on constantly changing input.
A Customer-Centric Approach
Many companies traditionally rely on a product-centric approach, but building a better mousetrap offers success only until the competition builds an even better one for a nickel cheaper. While companies enjoy more access to information and data, so too do consumers. Success in this hyper-competitive environment requires a shift away from that product-centric model, towards one that focuses from the very beginning on achieving customer equity and using Big Data to find the most important value drivers at any given time. Rather than organizing a company by product deliverables, organizing a company by unique customer lifetime value delivers a competitive advantage.
Using analytics to achieve that heightened level of customer equity gets at the heart of the customer experience – gaining a deeper understanding of it, and organizing the company around that experience rather than around the products themselves.
Right Data, not Big Data
Understanding the customer, achieving customer equity and driving value all depend on a foundation built on extending analytics to deliver up-to-the-minute data and real-time views, as opposed to simple historical information. The right data, gathered from the beginning with specific objectives in mind, trumps Big Data for its own sake every time – and will yield better and more profitable results.
Phani Nagarjuna is founder and CEO of Nuevora, a Big Data analytics and apps firm. His global leadership experience of more than 16 years includes C-level positions across product management, sales and marketing, and corporate strategy and turnaround. Nuevora was recently ranked by CIO.com as one of the top 10 Big Data firms to watch out for.