As long ago as 2008, the Royal Shakespeare Company in the U.K. saw a 50 percent increase in ticket sales at one of its theaters following an investment in an analytics program that helped it identify specific audience segments for targeted marketing campaigns.
Since then, businesses have been continuously adopting data-driven decision-making, applying analytics tools for forward-looking insights. A recent Accenture study, for example, found 33 percent of companies now use analytics for predictive purposes, up from 12 percent in 2009.
Turning Data into Money
Though we are nowhere near exhausting the opportunities to capitalize on data, many organizations have made significant returns on their analytics investments. Examples of what I call the “analytics ROI” are now being achieved in many industries.
U.K. retail giant Tesco used data from its customer loyalty card scheme to generate customer purchasing insights that enabled it to dramatically improve its supply chain management. Its 50-strong analytics division uses the data to make accurate forecasts about what customers will buy and when, and is therefore able to stock its stores precisely. The program has generated more than £100 million (approximately $162 million, U.S.) in operational cost savings for the retailer, while increasing revenues.
A U.S. services company invested $5 million in sales analytics last year with the aim of boosting revenues by almost $90 million. Revenues increased by $100 million, a more than 50 percent increase on the previous year. The program’s analytics models rate all sales leads generated by the company’s sales representatives in several areas across the country. Each representative then receives a list of their leads ranked on the likelihood of the customer making a purchase, enabling them to focus on the leads with the greatest potential.
Analytics is enabling insurance companies to cut fraud, an area identified by the industry as a major priority for action. Some 71 percent of insurers have seen fraudulent claims increase over the past three years according to Accenture research. For a typical motor insurer paying out 500 million (approximately $684.9 million, U.S.) a year in claims, analytics-based fraud management can produce savings of 10 million to 15 million (approximately $13.7 million to $20.5 million, U.S.).
From Improving the Business to Generating New Business
Data isn’t just helping organizations to improve their existing businesses, it is also an important source of new ideas. At least one in four companies is now using data as a source of inspiration for new business opportunities “to a great extent.”
Consider Verizon as an example. The telecommunications company has begun offering clients such as marketers, media companies and event venues much more detailed and meaningful data on their potential audiences. It is able to use its data to provide clients with analysis of the demographic, geographic and psychographic makeup of the audience for a specific advertising billboard, enabling advertisers to more effectively target audience segments.
One industry where analytics has been making a significant impact for many years now is health care.
For instance, U.S. health care providers are vulnerable to fraudulent claims, but one leading provider has reduced its medical costs by 30-40 percent per year using analytics during its claims processing. The analytics package scrutinizes the provider’s data in real time, identifying potentially fraudulent claims for investigation before they are paid. In addition to the headline savings, the solution has improved the hit rate of the provider’s fraud audits from 6 percent to 38 percent, reduced administration costs and improved customer satisfaction.
Analytics ROI is not only limited to companies and businesses — it applies to the public sector too.
The State of Maryland, for example, collected more than $175 million in additional revenues since implementing a data warehouse and business intelligence project in 2009. The solution enabled the State Comptroller to identify areas where it was failing to maximize revenues, leading to the creation of 13 revenue-generating units.
The Irish Office of the Revenue Commissioners used analytics models to scrutinize claims for tax refunds and credits. The Revenue Commissioners was able to substantially reduce the number of payouts made in undeserved or fraudulent cases; the savings during just the first few months of the project, which saw 1.2 million transactions assessed, exceeded 2.5 million (approximately $3.4 million, U.S.), more than covering the cost of the project.
From Insights to Outcomes
Much of what it takes to successfully apply analytics can be learned from examples like these. One lesson is that analytics only makes sense in a particular industry and functional context. Sophisticated algorithms and statistical models aren’t ends in themselves. To turn them into veritable solutions, you need to understand the specific logic of the business and processes the analytics can apply to.
Another lesson is that a key factor in realizing strong returns lies in industrializing your data processes: identifying the issue to be solved, analyzing the right data, generating the insights, taking action accordingly and then measuring the outcomes. By embedding this sequence into every part of the business and its processes, you create a virtuous circle in time, everything it does will be data-driven.
Many organizations are beginning to recognize the benefits their business could receive by applying data analytics. High-performing businesses now put data and analytics at the heart of everything they do and develop repeatable decision-making processes to maximize the potential of their data. They also continually measure the ROI achieved and where those returns disappoint, they work out why and start over.
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