To Benefit from Big Data, Resist the Three False Promises

To Benefit from Big Data, Resist the Three False Promises

Big Data solution providers make big promises, but technology alone cannot make marketing more effective. Here's how to be smart about your new tools.

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To Benefit from Big Data, Resist the Three False Promises

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Big Data solution providers make big promises. Just plug your data into our solution, they say, and we’ll deliver a stream of insights that allow you to improve marketing productivity, customer experience quality and service operations efficiency. It’s like the initial euphoria of the CRM revolution, which often led to unusable databases, rebellious sales teams and depleted capital budgets.

Gartner recently predicted that “through 2017, 60% of Big Data projects will fail to go beyond piloting and experimentation and will be abandoned.” This reflects the difficulty of generating value from existing customer, operational and service data, let alone the reams of unstructured internal and external data generated from social media, mobile devices and online activity.

Yet some leading users of Big Data have managed to create data-driven business models that win in the marketplace. Auto insurer, for instance, uses plug-in devices to track driver behavior. Progressive mines the data to micro-target its customer base and determine pricing in real time. Capital One, the financial services company, relies heavily on advanced analytics to shape its customer risk scoring and loyalty and offer optimization initiatives. It exploits multiple types of customer data, including advanced text and voice analytics.

How can your company profit from Big Data? You’ll have to break through the hype built on three flawed promises.

Promise 1: The technology will identify business opportunities all by itself.

Before investing in new technology, Big Data leaders tend to start by applying advanced analytics to solve a small number of high-value business problems with their in-house data. That allows them to learn about the operational challenges and limitations of the data, which in turn helps them define the requirements for a technology solution.

A large insurance company recently focused its data analytics program on fraud, after seeing a spike in fraudulent claims, which were costly to investigate. The insurer built a text-mining algorithm that generated fraud propensity scores from claim applications, historical behavior and suspicious social network relationships. This algorithm helped raise by 20% the number of fraudulent scores the company identified. The upshot was fewer cases under investigation and about $30 million in savings. Based on its experience in fraud prevention, the company is now investing in technology for other areas.

Promise 2: Harvesting more data will automatically generate more value.

The temptation to acquire and mine new data sets has intensified, yet many large organizations are already drowning in data, much of it held in silos where it cannot easily be accessed, organized, linked or interrogated.

From an analytic perspective, it’s generally easier to work with data that has some history rather than building new data sets. One large U.S. telecom company took just this approach. The company faced increasing competition and wanted to create a program to systematically increase the value of its existing customer base. To achieve this goal, the company combined more than 200 data elements from 15 marketing, service and operations databases to create “high definition” portraits of all its customers. The company used these customer portraits to develop targeted onboarding, cross-selling and customer engagement programs.

For example, a new onboarding program focused on customers who showed signs of low engagement with the company’s products. Instead of sending sales-focused marketing messages to these customers, the company began sending them product awareness messages. That increased product usage, which in turn led to a drop in customer churn and more upgraded services. In parallel, the company did more cross-selling to the highly engaged customers, because the data showed that these customers were more likely to upgrade. Cross sales rose by 2.5 times, with a far higher return on marketing investment.

Promise 3: Good data scientists will find value for you.

To profit consistently from Big Data, you need an operating model that deploys advanced analytics in a repeatable manner. And that involves many more people than data scientists.

One telecom service provider created a partnership model between the business units, support functions, such as IT, and the business intelligence scientists. The business units inject market experience and knowledge into the insights from the data scientists, raising the odds that their solutions will be pragmatic and can be implemented at scale. The IT division, which owns the data architecture, figures out how to incorporate new technology such as data lakes, manages the ever-growing data sets, and defines the policies and rules that govern them.

The company focused early on improving the economics of value-destroying customers. Sales and marketing staff defined the specific issue for the business intelligence team, which collaborated with IT to consolidate two years of customer data from various marketing and operational databases to identify the root causes of the value-destroying behaviors. Together, the three teams defined a strategy that could be implemented to turn value-destroying customers into profitable customers, and that has generated millions in incremental revenue.

Big Data has disrupted many industries, but technology alone cannot make marketing more effective. By applying advanced analytics to a few high-value problems before investing in technology, using in-house data and ensuring that frontline teams collaborate closely with the data scientists, you’ll be better positioned for success.

Written by Eric Almquist, John Senior and Tom Springer, partners at Bain & Company.


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