Big Data solution providers make big promises. Just plug your data into our solution, they say. We’ll deliver a stream of insights that enable dramatic improvements in marketing productivity, customer experience quality and service operations efficiency. It’ll be a snap for you and your team; our technology and your data scientists will do all of the heavy lifting.
Feel like you’ve seen this movie before? If you were caught up in the initial euphoria of the customer relationship management (CRM) revolution, then you did. Starting in the early 1990s, many companies bought into the hype and the technology, only to wind up with unusable databases, rebellious sales teams and depleted capital budgets.
The CRM industry has since matured, and there is no doubt that CRM solutions can now deliver real value to many organizations. As evidence, CRM was the sixth most popular business tool in FORFIRM's 2015 Management Tools & Trends survey. And global CRM spending totaled $20.4 billion in 2014, up from $18 billion the previous year, according to Gartner research.
Yet CRM failure rates remain high. A 2014 report from C5 Insight found that more than 30% of all CRM implementations fail and second and third CRM implementations at the same companies had only slightly lower failure rates. And this is 20 years into the “revolution”!
We see Big Data going down a similar path, making big promises about customer impact and value creation predicated on large investments in technology and expertise. In a recent report, Gartner predicted that “through 2017, 60% of Big Data projects will fail to go beyond piloting and experimentation and will be abandoned.” Why is history repeating itself? It’s not for lack of interest, effort or investment. Instead, it 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.
Companies are under increasing pressure to harness Big Data and advanced analytics. Customers demand more from the organizations with which they do business. Competition is intensifying, especially in mature industries such as financial services, retail, telecoms and media. Data-driven businesses continue to disrupt the status quo. Disruptors old and new including Progressive, Capital One, Amazon, Google, Uber and Zappos, to name a few have created data-driven business models that apply deep insights to deliver tailored products and services that win in the marketplace.
Leading users of Big Data set a high bar for success. They have assembled deep benches of analytical talent and created processes that allow their organizations to glean useful insights from advanced analytics.
US auto insurer Progressive, for example, uses plug-in devices to track driver behavior. Progressive mines the data to micro-target its customer base and determine premium pricing. Capital One, an American financial services company, relies heavily on advanced data analytics to shape its customer-risk scoring and loyalty programs. To this end, Capital One exploits multiple types of customer data, including advanced text and voice analytics. Meanwhile, US retail giant Amazon mines customer data intensively to create personalized online shopping experiences. Amazon uses purchase histories and click streams to create a sophisticated recommendation engine that it presents on customized Web pages. On the logistics front, Amazon has also been a leader in applying data analytics to optimize inventory distribution and reduce shipment times.
Leading users of Big Data set a high bar for success. They have assembled deep benches of analytical talent and created processes that allow their organizations to glean useful insights from advanced analytics. They have built technology platforms that deliver timely data and insights when and where they are needed in the organization. Many have also created cultures of continuous innovation based on rigorous “test and learn” methodologies.
Three promises and perils of Big Data
So how can your company profi t from Big Data? The first step is learning how to distinguish the actual potential from the extravagant claims. Much of the ongoing hype rests on three flawed promises: The first is that Big Data technology will identify business opportunities all by itself. The second is that harvesting more data will automatically generate more value. The third is that expert data scientists can help any company profit from Big Data, no matter how that company happens to be organized.
Below we identify perils associated with each of these three promises, and present examples of companies that have overcome each on the way to creating real value from advanced customer analytics.
Promise: The technology will identify business opportunities all by itself.
Peril: Limited return on investment despite large expenditures of money and time.
Failed technology deployments often start with the assumption that the shiny new tool will generate value all by itself. Companies that successfully harness the power of Big Data solutions tend to start by applying advanced analytics to solve a small number of high-value business problems with in-house data before investing in technology. In the process they learn how to implement solutions organizationally. They also gain insight into operational challenges and come to understand the limitations of their data and technology. They can then defi ne the requirements for their Big Data technology solution based on an understanding of their actual needs.
For example, one large insurance company recently focused its data analytics program on fraud. The company was seeing a spike in fraudulent claims, and was incurring significant costs to investigate these claims. The program aimed to reduce fraudulent behavior at lower cost. To this end, the company built a text-mining algorithm that generated fraud propensity scores. This algorithm helped the company achieve a 20% increase in the number of fraudulent scores that it detected. The upshot was fewer cases under investigation and about $30 million in savings. Having proven the value of advanced analytics, the company is now increasing its technology and capability investments.