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KM past and present: Five megatrends

This article appears in the issue January 2015, [Vol 24, Issue 1]
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One reason for that is the diversity of enterprise applications. Some are custom, some are SaaS and some are off-the-shelf, and the technology for accessing each one is different. Therefore, development of mobile apps for such applications is needed, but organizations are hampered by the high cost. More efficient development techniques would be a big benefit.

The proliferation of mobile devices has also spawned a number of other supporting sectors beside mobile application management (MAM), including mobile content management (MCM) and mobile device management (MDM). Each of them has a touchpoint to knowledge management and should be viewed in conjunction with an overall KM strategy.

Social analytics

Social analytics is a booming market, expected to triple over the next five years to nearly $9 billion and showing a growth rate of nearly 25 percent per year. Initially based on simple counts of the number of times a brand was mentioned in social media, analytics has evolved to the point where it is using sophisticated algorithms that support the use of social data for targeted marketing and for initiating customer service.

“Over the last five years, social media analytics tools have come a long way,” says Wilson Raj, global director of customer intelligence at SAS. “They have also moved from hindsight to insight and now to foresight, with predictive capabilities.” SAS social media solutions include integration and storage of social data, general text analytics and analysis of comments for sentiment, and a social conversation module that can work directly or integrate with third-party engagement solutions.

Real-time analyses allow marketing or brand campaigns to be synchronized with the topic threads that are emerging. “Decision trees allow ‘what-if’ scenarios such as the impact of increasing the frequency of an ad, or combining customer segments,” adds Raj. “These analyses allow the user to determine the relationships among various factors and to present visualizations of the relationships for better marketing decisions.”

The value of social media analytics is also increased by meshing it with data such as purchasing information from the data warehouse, to compare customers’ stated intentions with actual behavior. “We see tremendous growth in analyzing social media information along with data from the Internet of Things, such as the Nike FuelBand—which measures physical activity—or other wearables,” Raj says, “to build a profile not just of transactions but of tone and behavior along the customer journey.”

Customer engagement

The driving force for all of the above is customer engagement—collecting and managing big data, keeping information secure, enabling mobility and analyzing social media inputs. The ultimate goal is to engage the customer, whether for marketing, customer support, participation in loyalty programs or some other outcome.

Key right now for customer engagement is “omni-channel.” Whether the interaction is initiated by the customer or the organization, customers want options in the delivery channels. “When customers get to the engagement stage, companies need to think about how best to engage them,” says Sid Banerjee, CEO and co-founder of Clarabridge, which provides customer experience and customer care solutions.

Clarabridge solutions collect and analyze information from social media, surveys and recorded conversations. The software can incorporate data from customer relationship management (CRM) systems to develop a complete profile, and can export data into third-party systems used by their clients. “Social media analytics should not be an island,” Banerjee says. “The information should be tightly connected to upstream data so different departments can use it to drive the customer experience.”

Customer engagement is not a static business area; witness the evolution of “call centers” to “contact centers” and then to “customer engagement centers.” The feedback obtained through social analytics and traditional business intelligence can now be merged to explain both what customers are doing and why. That information can guide the delivery of marketing materials and help provide better customer service—two functions that have historically been separate.

“Neighborhood retail stores used to know what a customer wanted because they knew the customer,” Banerjee says. “Mass merchandising shifted that power to the manufacturer and distributor, who knew what the customers wanted in aggregate form because they tracked inventories. But now, the emphasis is coming back to the individual. What recommendation is a customer reading in a review, written by another individual? Is the company sending an appropriate recommendation or offer to the customer? In a different way, customer support has become personal again.

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