2014-22-October-Rainy-Day-Race.jpgToday’s data-driven companies have a competitive edge over their peers. How? They are generating breakthrough insights by bringing together all of their structured and unstructured data and analyzing it together.

When you can analyze a large amount of data as a single set -- rather than separate silos -- you can uncover significant insights that would be impossible to get from traditional business intelligence. And it’s these breakthrough insights that are giving companies a serious competitive advantage.

Yet while most organizations understand the value of analyzing big data to stay ahead of the curve, they often struggle to apply these data insights to business situations that matter most. So what are the most lucrative and effective use cases for big data analytics?

When applied correctly, everyone from chief marketing officers to product executives can make smarter decisions and realize benefits such as significantly greater operational efficiencies, new revenue streams and more innovative products. See how these top four big data use cases can drive the greatest business value for your organization.

Customer Analytics

Consumers interact with companies through multiple channels -- mobile, social media, physical stores, e-commerce sites and more. Deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalizing campaigns to increase revenue, avoiding customer churn and lowering acquisition costs. With insights about the customer acquisition journey, marketing departments can design campaigns that improve conversion rates and can predict and proactively intervene with customers that are at risk of churn.

One of the most effective ways to put customer data to work is to identify and understand high-value customer behavior beyond simple segmentation. Now, businesses can understand where and how their most valuable customers spend their money, and use that information to deliver more targeted and effective advertisements and offers.

A financial services company performed a customer segmentation that combined social media data and transaction data. They found that high-value customers frequently watched the Food Channel and shopped at Whole Foods Market. Armed with those insights, they were able to strategically design advertising campaigns that targeted high-value customers with health food promotions, reducing customer acquisition costs by 30 percent.

Operational Analytics

Manufacturing, operations, service and product executives know all too well the intense pressure to optimize asset utilization, budgets, performance and service quality. It’s essential to gaining a competitive edge and driving better business performance. By quickly delivering high-impact data projects that help them achieve their goals, they can analyze manufacturing production, predict product failures before they occur, optimize existing infrastructure to increase uptime and reduce operational and capital expenditures.

Collecting, preparing and analyzing data from everything including servers, plant machinery, customer-owned appliances, cell towers, energy grid infrastructure and even product logs can drive better operations and potentially save millions of dollars. A leading telecommunications service provider used data analytics to optimize network capacity and save more than $100 million. By incorporating all data sources such as customer segments, mobile broadband usage, customer plans and demographics, they were able to find the balancing point to determine what cell towers to upgrade to 4G and what cell towers did not require upgrading.

Fraud and Compliance

Data can identify and address security issues before they become problems. Security landscapes and compliance requirements are constantly evolving, and data can help uncover suspicious activity and mitigate risks in a way that wasn’t previously possible.

Analyzing data can reduce the operational costs of fraud investigation, help anticipate and prevent fraud, streamline regulatory reporting and compliance and, ultimately, protect your brand. Doing so effectively requires aggregating and analyzing data from multiple sources and types and analyzing it all at once -- think financial transaction data, geo-location data from mobile devices, merchant data, and authorization and submission data. This melting pot of data can yield insights and answers you never had before.

With the ability to analyze all data including point of sale, geo-location and transaction data, a credit card issuer was able to spot potential fraud more accurately than ever before. By identifying patterns in historical data and quickly spotting outlying data, this credit card company identified and prevented over $2 billion in potential fraud.

Data-Driven Products and Services

Innovating new products and services is the lifeblood of any business. Unless businesses can develop offerings that closely align with customer needs and desires, how else can they create new revenue streams, gain a competitive advantage and boost customer loyalty?

Savvy companies are leveraging big data to create new, data-driven product and service offerings. Think about how you can harness your customer data, social media insights, transaction data, geo-location data and device data. All this data can be combined with third-party data to offer new services. For example, a media company could provide brands and advertisers with analytic reports about how customers behave using mobile apps, allowing them to optimize ads and boost responses.

With data analytics, the possibilities are endless. Apply smart, data-driven decision making to these mission critical business processes, and you will reap the rewards of high impact data analytics.

Title image by Sam Javanrouh (Flickr) via a CC BY-NC 2.0 license