Big Data and Warehousing

Social Business: The Power of Big Data in Agile Engagement

Why you should humanize everything your business says—to all stakeholders, on all social networks

People often ask me what relevance big data has to their lives. For me, that’s an easy question to answer.

I simply point to Facebook, which, like most social networks, is powered by big data at all levels. I note that, for example, every ad you see displayed on the right side of your Facebook page is there because of big data. In other words, what Facebook’s systems choose to display there is the result of analytics that plow through every piece of data that you, your friends, and people like you and your friends have ever posted, linked, “liked,” viewed, and otherwise interacted with from any within Facebook and other partner sites. The same applies, in their own spheres of operation, to other advertising-supported social networks that you access.

Once I’ve driven home the unsung but fundamental role of big data in public social networks, I widen the scope of discussion (if people are still paying attention). I point out that many organizations now engage with customers, partners, employees, and other stakeholders over social channels: public, industry-specific, company-proprietary, and various blends of these community models. More and more of us are on various and sundry social networks night and day, at home, and at work—even if we don’t think of many of these communities as “social” in the same way that Facebook is.

Social business is the fabric of modern life. So what exactly is it, and does it always depend on big data?

IBM defines social business as the incorporation of social tools, media, and practices into an organization’s external and/or internal interactions. Social business enables fluid interaction among you and your customers, employees, suppliers, and other stakeholders. Within social networks of various shapes and sizes, members can connect, converse, listen, publish, and share directly with each other, eschewing centralized oversight, rigid workflows, hierarchical access controls, and other control-heavy features of traditional business collaboration tools.

Regardless of the specific online community within which it takes place, what makes any engagement “social” is that it leverages these core engagement principles, each supported by shared infrastructure within the online community:

  • Identity: The social network requires new members to declare their online identity and various profile attributes within a shared directory or registry.
  • Interaction: The social network supports publish-and-subscribe, peer-based interactions among members.
  • Sharing: The social network enables sharing of member-posted content on a common site, space, or forum.
  • Privileges: The social network allows members to declare which other members (aka “friends”) have special privileges to view their posts, send them messages, and so on.
  • Personalization: The social network allows each member to create a continuously updated, personalized view of the common forum, in terms of which other members’ posts they wish to see.

The inevitability of big data volumes in social business comes from the core social principle: user-driven content sharing. However, we recognize that social networks don’t always depend on all of the “3 Vs” of big data to serve their core functions. The smaller social networks, for example, may not yet incorporate petabyte volumes of server-resident user data.

However, all social networks involve unstructured data of various sorts—especially user posts, page impressions, and clicks—and most are geared to real-time high-velocity interaction. And most social networks, if they’re successful and attract more members, applications, and usage, will almost certainly accumulate a colossal volume of stored data—especially the bit-intensive unstructured varieties and streaming media—before long.

Within any customer-facing social business infrastructure, businesses can leverage the power of big data to drive the following applications:

  • Monitoring social networks for marketing, customer, and brand intelligence: Social media analytics leverage advanced analytics tools—reporting, dashboarding, visualization, search, predictions, text mining, and so on—to find patterns of awareness, sentiment, and propensity among current and potential customers, as surfaced up from social media such as Twitter and Facebook.
  • Mining social networks for patterns of influence and expertise: Graph analysis is advanced analytics that is specifically focused on identifying and forecasting connections, relationships, and influence among individuals and groups. It mines transactions, interactions, and other behavioral information that may be sourced from social media, and/or just as often from CRM, billing, and other internal systems.
  • Integrating real-time social intelligence into internal processes: Social media monitoring is real-time analytics that uses stream computing and complex event processing to acquire, filter, and display issues, exceptions, and other events surfaced from social media, so that alerts can be forwarded, workflows triggered, and response loops set up in internal operations.

Where customer engagement is concerned, big data infrastructure can drive targeted recommendations, offers, conversations, and experiences throughout social business channels. We sometimes refer to this pattern as “next best actions.” In practice, next best actions powers social business in the following principal ways:

  • Outbound customer engagement: This refers to the practice of monitoring social network traffic for stakeholder intelligence (awareness, sentiment and propensity) and using that feed to trigger next-best-action models that send finely targeted outbound response messages. In a business-to-consumer social network, inbound intelligence might be used to trigger next-best-action models that target outbound marketing promotions or respond to specific product issues. In an employee-to-employee social network, the next-best-action models might generate reminders to take particular HR actions by a specific deadline or to address a specific technical issue that an employee is having with a piece of equipment. In a business-to-business social network, the triggered messages might provide guidance to partners inquiring about the delivery status of particular shipments. In any of these scenarios, the outbound response message might be transmitted inline through the same social network where the stakeholder generated the triggering message, or through existing non-social messaging options.
  • Inbound customer engagement: This involves tuning social-channel conversations through automatically generated scripts, screens and apps that shape how employees interact with external stakeholders and with each other. In a call center environment, for example, customers interact with channel personnel who speak from online scripts and other guidance that is auto-generated by the next-best-action infrastructure. In social channels, you might have diverse human and automated agents handling diverse interaction scenarios that span a wide range of customer, employee, and/or partner segments. Furthermore, you might be orchestrating these social interactions to achieve diverse business objectives, such as reducing customer and employee churn, boosting sales and profits, and achieving greater efficiency throughout the supply chain.

Regardless of whether it’s an outbound or inbound engagement scenario, it’s not truly social if it feels like there’s a robot on either end of the conversation. To the extent that you can humanize your next-best-action-powered social channels, you’re likely to boost experience, satisfaction, retention, productivity, efficiency, influence and loyalty all around.

The next best action of social-business engagement must always be to humanize the next thing you say to any stakeholder at any time, even if in reality it’s a bot pretending to be a human or a human reading a bot-scripted response.

How will you individualize, personalize and naturalize every utterance—even those driven by embedded statistical models, business rules, and other algorithmic logic? Please read this blog for tips on how to keep the human feel in your social business engagements, and let me know your thoughts on this topic in the comments.
 

 
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James Kobielus

James Kobielus is a big data evangelist at IBM and the editor in chief of IBM Data magazine. He is an industry veteran, a popular speaker, social media participant, and a thought leader in big data, Apache Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management, and next-best action technologies.