Big Data, Big Trend

"Data is ubiquitous. Drawing important business conclusions is a game changer."
Dhiraj Rajaram, CEO of Mu Sigma

As a concept, "Big Data" isn't novel. Companies have been struggling with volumes of data for decades. What is new is that predictive analytics, or taking large quantities of data and making important business decisions, is becoming a key competitive advantage but only to those who have the expertise to make sense of the data. GA's recent investment in Mu Sigma, a leading global provider of analytics and decision support services, underscores our belief in the strategic importance of this large growth market. From Target's ability to identify and market to first time mothers based on their shopping behavior to Netflix's million dollar recommendation engine contest, the media has been filled with examples of sophisticated analytic techniques applied to the growing flood of digital information. As companies across all industries seek to navigate the evolving digital ecosystem, they will need to watch this trend closely in order to remain competitive.

There are now a countless number of devices including laptops, mobile phones, digital cameras, credit card readers and GPS devices (to name just a few) capturing all of our emails, text messages, Facebook posts, purchasing behavior, geo-location data, and other forms of human activity. There are also billions of less obvious digital sensors including RFID tags, weather sensors, X-ray machines, network routers, and CCTV systems, generating equally large amounts of data. Consequently, McKinsey estimates that the total amount of digital data created annually has grown more than +10x since 2005 at an accelerating pace to 1.8 zettabytes currently (10^12 gigabytes). By 2020, this amount will increase another 20x to 35 zettabytes, more than doubling the total amount of digital information every two years.

In a recent McKinsey Quarterly report, the author noted, "Over time we believe big data may well become a new type of corporate asset that will cut across business units and function as much as a powerful brand does, representing a key basis for competition[...]Success will depend not only on new skills but also on new perspectives on how the era of big data could evolve - the widening circle of management practices it may affect and the foundation it represents for new, potentially disruptive business models." The long-term implications of this phenomenon are still being determined, but the near-term consequences are clear. Companies that have already invested in the tools and management systems to make sense of these data sets have seen tangible value in areas of marketing, product pricing, product cross-selling, customer targeting and sales force effectiveness.

Utilizing data analytics can produce numerous positive trends for companies, including:

Increased ROI via More Effective Marketing
Companies can maximize marketing effectiveness and improve ROI by building models to pinpoint how different marketing spend drives revenue and using complex algorithms to determine how to optimize revenue, profits or ROI for a given campaign. Additionally, models are used to smooth data on sales and remove the impact of seasonal / price changes and to look for statistical linkages between the major types of marketing activities and variations in revenue and profitability.

Optimizing Product Pricing Strategies
Companies now often use data & analytics to create optimal pricing strategies for new and existing products by using advanced mathematics on historical pricing data and running pricing scenarios to maximize revenue under different pricing constraints.

Improving Cross-Sell of Product Sets
Improved product cross-sell to existing customers can be achieved by benchmarking penetration rates across product lines to identify gaps and opportunities, analyzing key behavioral / demographic indicators and developing portfolio segments with distinct purchase patterns using cluster analysis and launching pilot projects to test effectiveness and generate key learnings.

Accurately Segmenting Customers
Companies have improved customer segmenting by building look-a-like models to predict the behavior of existing customers in a new population pool, to increase loyalty among customers and to more effectively implement differential treatment among customers.

Building Visualization Tools for Sales Force Optimization
Companies have enhanced sales force productivity and effectiveness by using models to run various sales force scenarios that maximize revenue under constraints. Model output allows managers to optimally reallocate the sales force in an easy-to-use visual manner and to compare the results of different planning scenarios through visualizations.

For companies looking to capitalize on these large data sets, they need to take the first step of investing in the infrastructure to store and organize the unprecedented amount of data. Many of the early believers in the power of Big Data view the investment in infrastructure and services as necessary to remain competitive. For example, many large retailers need to analyze real time sales, pricing, economic, and weather data to custom tailor their merchandising selections and marketing campaigns. Shipping companies like FedEx, UPS, and DHL, for example, need to analyze traffic patterns to fine-tune their routing schedules. And online dating services will need to improve the algorithms they use for matching subscribers on dates. Longer term, however, the enormity of the problem and the opportunity it presents for creative thinkers has never been greater.

For more information on data analytics or an introduction to Mu Sigma, please contact your GA team.