The Power of Big Data Analytics to Drive Agile Social Business

People often ask me what relevance big data analytics 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 analytics 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 are 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 socials that you access.

Once I’ve driven home that unsung but fundamental role of big data analytics in public socials, 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 as Facebook.

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

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

But 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 socials for marketing, customer, and brand intelligence: Social media analytics leverage advanced analytics tools—reporting, dashboarding, visualization, search, predictions, text mining, etc.—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 socials 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, 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, 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, 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 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 in order 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.

James Kobielus,
IBM Big Data Evangelist

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