From a customer’s perspective, it’s a simple question to answer: “Don’t you remember me?” For any enterprise with hundreds to millions of customers, that recognition doesn’t always happen. That’s changing. As our spotlight on Avis Budget notes, the company uses big data to identify its most valuable customers today and tomorrow. And it’s doing so without penalizing customers who aren’t frequent renters.
1.) Big Data Expands Customer Intelligence:
How? Big data differs from narrow applications that look at just one source of data, yielding small answers. Big data examines a broad range of sources that include structured information such as purchase histories, customer relationship management (CRM) data and intelligence from industry partners, as well as unstructured information such as social media. In the case of the airline, those partners could include credit card companies, hotels and other travel industry sources.
Big data analytics also brings unstructured data into the fold, information gleaned from social media feeds, blogs, videos and other sources. Sorting through this information would have helped the airline answer a big-picture question that companies have struggled for decades to answer: How do we treat all of our customers like rock stars?
Expanding customer intelligence is just one trend. As the technology evolves, using big data will accelerate three other trends over the coming year.
2.) Big Data Improves Operational Efficiencies:
Big data will finally forge the last links of the value chain that will help companies drive more operational efficiencies from existing investments.
That feedback loop is created by data generated in the field, and it’s growing at a pace that’s hard to comprehend. Sensors on a single commercial aircraft generate 20 terabytes of data an hour. Automobiles are reporting back data collected from onboard sensors and dealer service systems. And let’s not forget the growing tide of RFIDequipped vehicles, crates and packages.
These incredible repositories of data, combined with machine-to-machine interaction, are fueling a new wave of predictive analytics, services that enable equipment such as airplanes to determine their own maintenance schedule, alerting the supply chain to ensure that the needed parts arrive at the right place at the right time.
Big data is moving from the realm of data scientists into everyday business transactions and encounters. In call centers, analytics-infused CRM systems can review multiple data sources in real time to suggest offers that a representative can present to a customer. At the doctor’s office, analytics integrated into a health maintenance app may improve outcomes by presenting the physician with informed suggestions and next steps to consider in treating a patient.
Insurance companies, which have long been data driven, will benefit significantly from the introduction of big data. Industry-specific analytics will help them speed claims processing while reducing costs and spotting potential fraud by use of analytics-backed solutions that can determine whether a claim can be processed automatically or should be flagged for review by a specialist.
3.) Big Data + Mobile Means New Business Processes:
As companies become more data-driven, it’s only natural that those insights find their way into the hands of people who can put them into action. Mobility will accentuate the impact of big data on both customer intelligence and operational efficiency by making everything immediately actionable. Armed with immediate decision-making capability and intelligence on your mobile phone, you will be able to implement new business processes that will change how business is done.
Adding mobility to big data means enabling frontline employees with real-time insights, when and where they need them. Those insights will come from blending data in motion — information that’s changing on the fly — with data at rest. Mobility also enables real-time data collection from the field, adding to the pool of knowledge that will drive insights in another part of the system.
For example, a delivery company with trucks in the field can improve on first-generation, efficient-routing tools by using smarter tools that can anticipate traffic conditions along certain routes at a specific time of day or create a new route in response to information about an accident that just occurred or information that’s input by the driver.
4.) No Time To Lose — Big Data And Analytics Go “As A Service”:
Building an internal big data department stacked with petabytes of storage, rows of blade servers and a team of data scientists isn’t within the reach or a desired core competency of every company. In an earlier time, spreadsheets were the de facto tool and best friend of marketing managers, collecting data from campaigns and digesting it into rough but valuable insights. Ask them how that’s working now.
The avalanche of inputs from social media and other unstructured information that is characteristic of big data doesn’t fit the spreadsheet model any longer. The volume, variety and velocity of data have made it too complex to analyze using old-school tools, and not everyone wants to become a data scientist.
That’s where data and analytics offered as a service will help. Companies of any size can employ big data by sharing a pool of scientists and resources, getting the expertise they need without venturing beyond their core competencies or taking on a big fixed expense.
Speaking of expense, that brings me back to my colleague and the ticket agent. How did she fare? “Well, I paid the fee because I really had no choice at that point,” she said. “But I do avoid booking with that airline when I can.” That’s an important and expensive lesson. Maybe the issue isn’t whether we can afford to implement big data, but rather, whether we can afford not to.