By 2025, the world will have 175 zettabytes
(175 trillion gigabytes) of data. That’s an unfathomable amount of information, so why does it feel like your company is amassing most of it? Not only are you drowning in data, but you’re spending millions of dollars on analytics tools just to make sense of it all. Yet for all the money you’ve spent on different platforms and tools, hoping that just one more will solve the growing complexity problem, a majority of the data remains underutilized, unintegrated and inaccessible at large by those who need it most.
The priority shouldn’t be buying analytics tools—integrated analytics is worth nothing without integrated data access, and vice versa. For companies wishing to distinguish themselves from their competitors, the ability to centralize access to their data, wherever it might be stored, and minimize the costs and time associated with data movement and query processing is the first critical leap. Taking advantage of both integrated data and analytics has a proven track record of helping organize operations, enhance customer experience and improve revenue and market growth. In fact, the enterprises who utilize data-driven decision support systems tend to emerge as leaders among their competitors.
Without integrated data, some of the analytics processes can be incredibly costly. The necessary data is often scattered over multiple data silos and environments. It’s collected by sales, marketing, customer service, research and development or even external sources outside of the enterprise’s domain. Integrating the essential information in a timely manner is critical for companies who desire optimized business solutions through better-informed decisions. By employing a simplified and centralized data strategy, more time can be spent on operationalizing insights.
A cross-functional analysis of all available data exemplifies how a well-thought-out data strategy can be impactful. Consider a scenario where a medium-sized U.S. health insurance provider has been losing year-over-year revenue consistently for the past three years. Without data-driven solutions, the CFO is at a loss to explain the decrease beyond a decline in customers. Without understanding the root cause, how is the company to execute an effective turnaround plan? Should the focus be on retaining the most profitable customers or recruiting new ones? Or perhaps the problem resides in product pricing or utilization costs? While the company continues to maintain separate data silos for customers, operations and other reporting systems, it may be impossible to answer the necessary questions to get to the root of the problem.
For this company, integrating data through a central access point was a critical first step. From there, several insights were procured from a more holistic view of their declining revenue problem:
Revenue Decline: Is the company losing customers?
With steady increases in premium pricing over the past three years, customer churn has increased as well as the number of customer complaints involving switching insurance due to high pricing.
Decision—Reduce premiums for policies with the lowest marginal cost.
Revenue Decline: Is the growth rate of new policy sales declining?
After also noticing a significant decrease in new customers year-over-year, the leadership team investigates further with the sales team. They discover that as premium pricing has increased, potential customers weren’t even considering this company for health insurance. To remedy this, salespeople had to make local decisions to offer reduced costs to attract new customers.
Decision—Reduce premiums for policies with the lowest marginal cost.
Claim Payouts and Fraud
Increased Expenses: Are claim payouts higher than usual? Is fraud a part of this problem?
The average payout for one of their health insurance claims has nearly doubled in three years. After reviewing their fraud detection methods, management concludes that fraud is not the primary reason for the higher payout costs.
Decision—Reduce premiums for policies with the lowest marginal cost and raise deductibles to offset increasing payouts.
Increased Expenses: Why are claim payouts higher than expected?
The CFO questions their methods for calculating underlying risk and learns that their methods for segmenting customers into risk levels, which greatly affects both the premium and deductible pricing, haven’t kept up with the competition who use advanced machine learning and graph analytics to micro-segment and personalize risk calculations. Potential customers receive overpriced quotes for new policies (deterring new business), while existing customers aren’t paying enough to compensate for the increasing payouts.
Decision—Overhaul the risk management strategy with personalized, disease-specific risk scoring algorithms. Doing so helps reduce non-emergent ER use for specific cases while also improving treatment of chronic disease patients who are admitted to a hospital more than once. Most importantly, it helps the company determine appropriate premium and deductible pricing for each available policy.
Grow The Business: How can the slowdown be reversed? What can be done to better attract new customers?
By calculating risk with more accurate and personalized algorithms, the company can lower pricing to attract new customers and grow revenues, while minimizing claim payouts to reduce expenses, and improve patient health in the process.
Decision—Employ Teradata Vantage to:
Analyze integrated data for a complete view of the subscriber
Employ machine learning and graph analytics for more accurate disease-specific risk scoring algorithms
Use 4D Analytics to uncover behavior patterns and identify potentially highly profitable customers clustered in communities that the company is already established in. The new risk scoring algorithms are used alongside behavioral analytics to segment which of these customers would have the most potential profit. A new marketing campaign is launched to target this potential new business.
With a more personalized approach to risk management and attracting new business, retention and satisfaction among customers increased once pricing plans were restructured. If the data was poorly integrated, the number of questions to ask would be less and the company would struggle to see the complete picture and make fully informed decisions. Only by looking at cross-functional data in tandem was the company able to view the entirety of the problem and act accordingly.
In the case of this company and countless other organizations investing in solutions-driven analytics, Teradata provides a clear path to rise above the complexity of data analytics into intelligence. Imagine trying to make an important business decision without understanding how it impacts your customers, operations or bottom line. Integrated data used alongside powerful analytics tools empowers companies to better make differentiated decisions by leveraging all the available information at their disposal. Consequently, these companies arise as leaders among their competitors.
Ethan Smith is a product marketing intern at Teradata who promotes the new analytics platform, Vantage. He facilitates the creation of customer-facing assets, such as business use case demonstrations for custom analytics functions, and is aiding the overhaul of a competitive intelligence sales tool used to visualize differentiators between competitors’ platforms and Teradata. Ethan is an undergraduate senior at University of Southern California majoring in Computer Science and Business Administration. His work experience through his multiple internships has helped him build a foundation of understanding in data warehousing, big data, and product marketing, although he’s eager to get involved in product management where technology can be focused toward a greater societal impact. Ethan’s passions include Scouting (where he is an Eagle Scout and still involved in youth leadership seminars), hiking and backpacking, and investing and financial technology.
View all posts by Ethan Smith