How Executives Can Grow Revenue with Big Data

Last Updated January 16, 2020

Two words from the business lexicon may help jumpstart a broader understanding of Big Data and the notion of data-driven growth: Incremental revenue.

Uncovering sources of incremental revenue is important to the success of a business. Being able to find and leverage these sources is a sign of an efficient and well-oiled enterprise. One way to find these revenue opportunities is through analysis of the increasingly rich and voluminous amount of data being created in interactions with customers and other stakeholders. The more business professionals understand how to use this data to find insights, the smarter their decisions can be.

READ MORE: Rise of the Data Scientist

The ability to harness Big Data, however, remains a challenge. According to The CMO Survey, companies with sales revenues of $10 billion or more spend nearly 14% of their marketing budgets on marketing analytics. Yet there’s only a 30% usage rate of the analytics that have been made available.

Christine Moorman, director of The CMO Survey and a professor at the Fuqua School of Business, Duke University, puts this utilization gap down to any number of causes:

• Users need insights, not just data – a downfall of marketing analytics is data, not true insight.
• The planning or marketing decision-making process has not evolved to include a phase where available analytics would be employed.
• There is not a strong relationship between producers and users of marketing analytics. Analysts may not understand or anticipate users’ needs and most users aren’t trained to understand data analytics.
• Companies need to learn how to use marketing analytics to enable them to enter new markets or compete differently, not just to expand their reach in markets they’re already in.
• Managers model the use of marketing analytics by asking for data and data insights. They push for details and move in response to them. That’s how a wider understanding of marketing analytics’ role in decision-making is grown.

What does it take to close the gap and drive an impact from Big Data?

It starts with the evaluation of three key areas to identify the desires of improved analytics and the insights they provide. The first key area is how the business or department is performing according to key performance indicators, like revenue or margin growth, that have been agreed upon company-wide. Once those have been identified, it’s important to delve into the drivers of each performance indicator. This requires utilizing portfolio analysis to understand the business’ dynamics. Finally, an understanding of customers and their needs should be used to steer the indicators in the right direction. For the greatest potential revenue growth, the direction should be aligned to the customers’ needs and have an accompanying message that resonates, which can then be deployed into the channels that are most relevant.

The second key area is to invest in analytic skills development. A study by Accenture, a management consulting firm, found the biggest in-demand skill gaps for U.S. workers tend to be in the areas of problem solving, analytical and managerial skills. Once the business understands the implications of the customer data on hand, better decisions can be made on campaign approaches, budgets and customer targets. Data-to-decision skills are key on both the data and business side of the organization.

As individuals’ skills are being brought up to snuff, so should the data infrastructure – the approach to business intelligence that is critical to guiding the business and creating a powerful competitive edge. The most difficult aspect of putting this into place is less about your hardware or technology choices and more about how your information system is designed and architected.  Whether it’s big data or small data, the architecture should be designed around the information flow, against which an internal team can execute.  Evaluating analytics and insights needs can help uncover the gaps in business understanding and help shed light on what kind of data can populate the infrastructure.

The last key area is a formal decision-making process, understood across the organization. Mapping this out and executing it can be beneficial as this helps establish guidelines on issues such as what kinds of projects may be funded and the criteria for funding decisions. It can also help people understand the rationale for the work they’re doing – project objectives and their relationship to overall business priorities.

Big data or small, the business community needs to lay the groundwork to use it more effectively in order to succeed and grow in an increasingly competitive environment.