Data management is having a dramatic change on marketing. Companies that rely on promotions, merchandizing improvements and other traditional marketing tools are losing market share across a range of profitable segments to companies that invest in their ability to collect, integrate, and analyze data from each distribution unit and use this data to run real-world experiments.

By constantly testing, bundling, synthesizing, and making information instantly available across the organization—from the branch or store to the CFO’s office, a company can become far nimbler.

Data Explosion

Data has been particularly effective in managing employees;  tracking purchases and sales, and offering clues about how customers will behave. But over the last few years, the volume of data has exploded.

In 15 of the US economy’s 17 sectors, companies with more than 1,000 employees store, on average, over 235 terabytes of data—more data than is contained in the US Library of Congress. Reams of data still flow from financial transactions and customer interactions but also cascade in at unparalleled rates from new devices and multiple points along the value chain. Just think about what could be happening at your own company right now: sensors embedded in process machinery may be collecting operations data, while marketers scan social media or use location data from smartphones to understand teens’ buying quirks. Data exchanges may be networking your supply chain partners, and employees could be swapping best practices on corporate wikis. – McKinsey Quarterly, October 2011 (Are you ready for the era of ‘big data’?)

New academic research suggests that companies that use data and business analytics to guide decision making are more productive and experience higher returns on equity than competitors that don’t. McKinsey research shows that “networked organizations” can gain an edge by opening information conduits internally and engaging customers and suppliers strategically through Web-based exchanges of information.

“Big Data” in a Nutshell

The key elements of big data are:

  1. Companies can  collect data across business units and even from partners and customers.
  2. A flexible infrastructure can integrate information and scale up effectively to meet the surge.
  3. Experiments, algorithms, and analytics can make sense of all this information.

Implications

The Loss of Proprietary Data: As information becomes more accessible across sectors, it can threaten companies that have relied on proprietary data as a competitive asset. Take the real-estate or travel industries, for example, which enjoy access to transaction data and data on the bid and ask behavior of buyers, which require significant expense and effort to acquire. In recent years, online specialists data and analytics have started to bypass agents, permitting buyers and sellers to exchange perspectives on the value of properties and creating parallel sources for real-estate data. Cost and pricing data are also becoming more accessible across a spectrum of industries.

Better Integration:  Your company’s data is often housed in various departmental silos that impedes timely use. McKinsey notes that financial institutions in particular suffer from failure to share data among diverse lines of business, such as financial markets, money management, and lending, which precludes a coherent view of individual customers or understanding of the links among financial markets.

Integrating data from multiple systems and inviting collaboration among  functional units or coordination with external suppliers and customers allows partners to collaborate during a project’s design phase, which is a crucial determinant of final costs.

Smarter Decisions: Big data introduces the possibility for a different type of decision making. Using controlled experiments, companies can test hypotheses and analyze results to guide investment decisions and operational changes. This can help you distinguish causation from mere correlation, and reduce variability of outcomes and improve financial performance.  Researchers can model the impact of variations in design and delivery of products and services, training and other processes that impact productivity and sales.

Leading online companies are continuous testers. They may allocate a  portion of their Web page views to conduct experiments that reveal what factors drive higher user engagement or promote sales. McDonald’s has equipped some stores with devices that gather operational data as they track customer interactions, traffic in stores, and ordering patterns.

“Natural” experiments that identify the sources of variability in performance can improve productivity. One organization that collected data on multiple groups of employees doing similar work at different sites found that simply making the data available spurred lagging workers to improve their performance.

Real-Time Targeting: Companies have long used data to segment and target customers, but big data makes real-time personalization possible.

Tracking the behavior of individual customers from Internet click streams, allows you to update their preferences, and model their likely behavior in real time. You could then recognize when customers are nearing a purchase decision and nudge the transaction to completion by bundling preferred products, offered with reward program savings. Such real-time targeting,  leveraging data from a rewards program, cam increase purchases of higher-margin products by its most valuable customers.

One personal-line insurer, tailors insurance policies for each customer, using constantly updated profiles of customer risk, changes in wealth, home asset value, and other data inputs.

Quicker Metrics:  Products can now generate data streams that track their usage. Some retailers use “sentiment analysis” techniques to mine the huge streams of data generated by consumers using various types of social media to gauge responses to new marketing campaigns in real time, and adjust strategies accordingly. This can cut weeks from the normal feedback and modification cycle.

The bottom line can be improved performance, better risk management, and consumer insights that would otherwise remain hidden.

Complications

The greater access to personal information that big data often demands will place a spotlight on the tension between privacy and convenience.

Certainly, consumers should be made aware that they benefit from the “economic surplus” that big data generates in the form of lower prices, better alignment of products with consumer needs, and lifestyle improvements from better health to more fluid social interactions.

However, privacy and data security concerns will grow.  With more open access to information, new devices for gathering it, and cloud computing means that that IT architectures will become more integrated and outward facing and thereby pose greater risks to data security and intellectual property. A proactive response to this is vital.

Conclusion

Done right, however, big data could improve productivity and produce hundreds of billions of dollars in new value if you can more proactively capitalize on it, you will not be blindsided by it. And, according to McKinsey Global Institute’s analysis the Finance and Insurance sector is one that could benefit most from this in terms of ease of capture and value potential.

Snap Principle of Big Data:

Capitalize on it now or be blindsided.