As technology grows in capability and breadth, information output is consequently greater, and in today’s era of the Internet of Things, where uncountable devices and systems across the globe are interconnected, this means one thing: an enormous amount of Big Data. In fact, recent research by the IDC predicts that by 2020, there will be nearly as many digital bits as there are stars in the universe, and data created and copied will reach 44 zettabytes (44 trillion gigabytes).
While this statistic is staggering, it also presents a valuable opportunity for businesses around the world to capture and capitalize on the insights and observations granted by the devices we use and depend on every day. For companies deploying CRM solutions, the potential is especially great—smarter devices and more strategic analyses mean customer relationships are strengthened, marketing efforts are more direct, and service levels are unparalleled.
Yet, weeding through the astounding amounts of data generated across every interaction to locate the elements that add specific business value is no easy feat—requiring a new generation of tools that can sort and organize the knowledge into a relevant and applicable vision.
This practice, known in the industry as “data science”, relies on interwork between embedded systems and personnel to capture and review critical data, so executives can make more informed decisions and accurate forecasts while team members are provided with just the information they need to drive more engaging interactions across every step of the sales cycle. As a result of this functionality, the IDC predicts that by 2020, 37 percent of tagged and analyzed data will be useful, compared to only 22 percent in 2013.
The key to successfully solving the data science equation is seamlessly integrating such intelligence into existing systems, an approach seen in Salesforce’s recent addition of data science capabilities to its Service Cloud and Marketing Cloud. The former seeks to automate select customer service tasks by assigning requests to agents based on skill set, case history, and other factors, while the latter is designed to drive more direct predictive intelligence and marketing outreach efforts.
Though the tools at our disposal are becoming smarter and sleeker, their overall objective remains the same—to create and sustain a customer-centric business model—and new and emerging data science technologies are no exception. By leveraging customer data across the enterprise, these tools can help turn the wild frontier of Big Data into a goldmine of opportunity for engagement, one zettabyte at a time.
To learn more about how partnering with our seasoned CRM consultants can deliver this functionality to your business, please contact any member of our consulting team at firstname.lastname@example.org. We also encourage you to contact Tokara’s VP of Business Development, Mark Fillingim, directly at +1 972-719-0213.
Berridge, Eric, “Data Science Leads The Customer Engagement Revolution,” Bluewolf Blog, April 30, 2015, http://www.bluewolf.com/blog/data-science-leads-customer-engagement-revolution.
IDC Whitepaper,” The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things,” April 2014, http://idcdocserv.com/1678.
Lashinsky, Adam, “Salesforce CEO Marc Benioff on Where Big Tech is Headed,” January 22, 2015, http://fortune.com/2015/01/22/salesforce-ceo-marc-benioff-on-where-big-tech-is-headed/.
Trefis Team, “Salesforce Adds The Power of Data Science to Its Service and Marketing Clouds,” Forbes, March 10, 2015, http://www.forbes.com/sites/greatspeculations/2015/03/10/salesforce-adds-the-power-of-data-science-to-its-service-and-marketing-clouds/.