The Value of Social Data

White Paper

As the variety of social data continues to grow, many organisations are looking for cost-effective ways to use this data to get a better understanding and more holistic view of their customer. Social provides a unique channel to learn about your customer, and offers real-time insights like interests, actions, likes and dislikes that can provide invaluable behavioral and predictive data patterns. This white paper offers a brief overview of how organisations can make best use of social data by implementing a framework to integrate social data with other enterprise data to create long-term, profitable customer relationships.

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The Value Of Enterprise-specific Social Data

Today, most organizations have social-media-management tools that allow them to do various tasks including publishing, listening and engagement. More often than not, however, these social engagements and insights remain siloed from other customer-facing applications. What’s required for success today are social platforms that not only provide the essential social tools and capabilities but also integrate with key enterprise applications like marketing, service, sales and commerce to power comprehensive CRM systems.

Many organizations, particularly in the financial services, consumer products, retail, and technology industries, have made significant progress in deploying social customer relationship management (social CRM) capabilities, but now they are looking for more automated and powerful ways to socially enable their external-facing functions.

These organizations want to do more with their social data. With enterprise data growth expected to continue at 40 percent through 2020, largely driven by consumer-generated content, getting value from this data is becoming increasingly important in corporate strategies.

The Social Relationship Management Process

To begin with, let’s look at the basics of a social relationship management platform and the value enterprise-specific social data can offer.

The diagram below shows a general process for leveraging social data as part of an overall social relationship management approach. This process tracks an organization’s social efforts across customers’ social activities and communities, starting with listening and engagement, through to broadcast communications efforts that are repeatable and flexible.

[Download PDF to see Diagram]

SRM + CRM = Social CRM

(Social Relationship Management) + (Customer Relationship Management) = The integration of social within CRM systems that can be for marketing, sales, commerce, and other lines of business that need to understand and manage their customer relations.

“Social CRM is a philosophy and a business strategy, supported by a technology platform, business rules, processes and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment. It is the company’s programmatic response to the customer’s control of the conversation.”—Paul Greenberg, CRM Industry Thought Leader

Socially-enabled Big Data Analytics And Customer Experience Management

Once social technology has been integrated within CRM systems, or social CRM is in place, it becomes possible to amplify social value by using social data in analytics, business intelligence, and customer experience (CX) management systems. Expanding social listening capabilities to not only capture customer and prospect signals, but also their key profile information, along with the results of social engagement, opens up a comprehensive approach to socially-enabled big data analytics and CX management.

Integrating and extending social insights across the enterprise allows businesses to get a complete picture of their customers and take a giant step forward towards becoming a social business.

Social Data Integration Framework

[Download PDF to see Diagram]

The diagram above shows how a social data integration framework could operate.

At the core of this framework is a socially-enabled consumer data store, providing a 360-degree view of customers by integrating:

  • Unstructured content that captures customers’ intentions, interests, and needs, along the customer lifecycle information from social and internal data sources
  • Quantified transactional, behavioral, and customer-profile data from CX-management applications

This data repository should be architected to support high performance and horizontal scalability for both structured and unstructured data, combining Hadoop, map reduction, and relational database technologies for complete and seamless data access. Within this environment, customer and prospect signal data should be enriched with other structured enterprise data on a continual, near-real-time basis.

This socially-enabled big data analytics and CX management framework provides the ability to integrate current enterprise data with new sources of public data and corresponding signals for faster decision-making and real-time, ROI-oriented action.

A unified view of customer data can support a number of key capabilities.

Insight Discovery

Analysts can uncover new insights across structured and unstructured data by conducting contextual drill-down into customers, prospects, and key business data. Rather than trying to impose structure on unstructured data, a high-performance insight-discovery engine should be used to blend and filter the data to access previously unavailable insights.

These insights provide very clear guidelines on how best to approach different types of customers and can be used to define entirely new, high-value key performance indicators (KPIs) within business intelligence systems for faster decision-making and real-time business management.

Business Intelligence and Real-Time Analytics

Repeatable, near-real-time dashboards and reporting on existing and newly discovered KPIs make it easier to identify trends, determine important variances and outliers, and track overall performance.

This kind of analysis also offers the ability to provide real-time alerts based on pertinent conditions, which can be tracked on a periodic basis for trends across KPIs. This enables data-driven, objective decision-making for line-of-business executives and their teams.

CX Management

CX management for sales, marketing, service, and commerce allows business functions to act in near real time on any newly generated signals and alerts. Engagement can be managed through the CX management application’s workflow to match a customer need to the appropriate, company-determined response.

In addition, marketing automation solutions will allow the results achieved in specific customer-experience interactions to be amplified through hyper-targeted segment communications.

Augmenting Social Efforts With Data As A Service

An organization with a functional social CRM platform is now continuously listening and learning from customers and key constituents in social media, identifying relevant posts and following up with direct engagement where warranted, and starting to integrate signals for communication into appropriate CX-management systems, as well as insights for analysis into business-intelligence applications. The next step is to augment social data with other public data for more advanced analytics.

Advanced analytics means understanding causality and correlation from a wide variety, volume, and velocity of data to optimize its business value. And in some cases, it can be used to predict future performance and make appropriate course corrections to improve outcomes. But the data that must be acquired, processed, and analyzed is very complex.

It can vary across structured, semi-structured, and unstructured data.

It can span across content, profile, and communities of profile data.

It is increasingly public, curated, and user-generated.

The key is not just getting the data, but using it to help discover the insights to connect to and improve KPIs.

There is now a need for more business applications to have the ability to ingest and use high-quality curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been providing an easily accessible system for contextually integrated data, enriched with insights, to be exported into business applications.

[Download PDF to see Diagram]

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Data as a Service (DaaS)

Many organizations are starting to use cloud services that provide high-quality data and insights uniformly to all necessary applications. These services offer a number of key advantages

  • Agile and easy to use. Business applications can obtain data on demand quickly and simply.
  • Cost-effective. Pre-integration with applications reduces time and effort.
  • High-data quality. There is single-point access to reference data and links to transactional, curated, and social data.
  • Simpler data governance support. Data privacy and compliance are centralized.

Just as the cloud has transformed how enterprises manage their IT from an infrastructure, platform, and software perspective (IaaS, PaaS, and SaaS), the next step is Data as a Service (DaaS).

Oracle’s Don Springer says DaaS is for enterprises to take advantage of growing quantities of social and public data. “No one wants to have to store 50 to 60 petabytes of data a year,” he says. “What you’re going to see in the future will be data hosted in the cloud, but maintained by the client. That way, you only need to store and maintain the data you actually need. You can access it on demand, when you need it and how you need it.”

Cloud-based data services are currently gaining traction in the market, with two delivery options emerging: one option is an “appliance” service that provides a single, reliable source of accurate business data, alongside social information about accounts, leads, contacts, etc. The other trend is towards an online market “exchange” where ISVs and data publishers can sell premium datasets, with the exchange providing a rich set of Web interfaces to ease data integration.

When deciding on the best approach, an organization should develop an elastic schema to support multidomain applicability across business functions. This allows it to take the most flexible approach to harness the speed and breadth of public data to achieve business value.

The key tenet of this approach is that an enterprise carefully federates common utility, master reference data endpoints, mobility considerations, and content processing, so that they are pervasively available. Value-added data, achieved through enrichment with specialized algorithms, as well as by applying business know-how to weight-factor KPIs, based on innovative combinations of data, will be where real value is achieved.

In essence, DaaS becomes a single entry point for public data, with enrichment and capabilities to extract and integrate the right data from the right sources with the right factoring at the right time, for faster decision-making and action within core business applications. As more data becomes available (and in many cases commoditized), this value-added data-processing approach can provide ongoing competitive advantage.

This approach to integrating and enriching data, combined with enhanced analytics, opens up a host of new capabilities for transforming processes across lines of business.

Integrated Social And Enterprise Data = Enhanced Analytics—why A Savvy Cmo And Experienced Cio Are Necessary To Succeed

Collaboration between the CMO and CIO is essential to integrate social and enterprise data into a data pool that all departments in the organization can leverage according to their specific needs.

The CMO has become the primary owner for social (earned, owned, and paid media) within the enterprise and is leading the effort to create more compelling customer experiences by listening, learning, and engaging with customers in a meaningful way. The CMO buys the social CRM tools, selects the data, and hires the staff to drive social relationship management within the enterprise.

The CIO has always been the owner and provider of the enterprise’s traditional data (including transactional, operational, and behavioral customer records). In addition, the CIO typically leads the technical architecture decisions to acquire, store, process, and make available new forms of consumer-generated information.

Other lines of business within the enterprise also need access to unified and enriched data, and this is made more valuable by blending social and enterprise data together intelligently. The enterprise’s departments are looking to the CMO to drive business requirements and social know-how and to the CIO to manage data, and technical architecture and integration interfaces. As a team, the CMO and CIO are being called on to lead the charge toward socially enabling their organization.

[Download PDF to see Graph]

Integrating Social And Enterprise Data

Let’s first review the basic steps of the data-integration process:

Step 1: Identify The Data

This will be a mix of

Traditional sources (customer profile data and transactional data, including orders, service requests, digital campaign response history, surveys, etc.)

Social data (unified social profiles, Tweets, posts, pictures, videos, etc.)

In this step, the CMO will work alongside the CIO to identify what data is currently available and in what format. Any discovered gaps in data will need to be further researched to identify potential sources or solutions.

Step 2: Plug That Data Into A Data Exchange

For new sources of public data (e.g. digital, curated, social, etc.), this can be outsourced to cloudbased DaaS provider. For proprietary data, this can be stored in a private cloud environment or on premises. In either approach, the office of the CIO will look for a solution allowing access to all data through a unified architectural approach, so new data pools can leverage existing enterprise data pools.

Step 3: Enrich The Data

It is essential to enrich the combination of traditional data and social data to gain insights based on a more complete view of the customer. The CIO leads the delivery of these services to meet the requirements of the CMO.

Step 4: Analytics And Nextgeneration Data Pull

By creating a shared data pool and sharing best practices, the CMO and CIO can help all functions across the enterprise detect new actionable insights on an ongoing basis through a variety of CX and CRM solutions.

The Social Crm Process

Use Case: Improving Campaigns with Analytics that Leverage Social and Enterprise Data

A shared data pool of integrated social and enterprise data allows for an analytics-based approach toward optimizing the campaign across digital, social, and traditional media channels.

[Download PDF to see Diagram]


With the right platform in place for leveraging social data, enterprises can start to gain a much clearer and more detailed understanding of their customers. By understanding customers better and tracking the results of interactions, organizations can successfully monetize consumer relationships throughout their lifecycle. Communications can be hyper-targeted to get relevant, personalized content to customers and prospects, and lessons learned can be shared and repeated across all external-facing lines of business.

Organizations with the deep insight to make changes on the fly will gain a significant competitive advantage.

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