Friday Jan 24, 2014

How Orgs Can Set Up an Analytics Framework that Leverages Social Data

social marketing, social mediaIn our last blog we discussed how a good bit of our social marketing focus should be on social listening. The wonderful product of all that listening is a wealth of social data. But what do you do with it? How do you employ it? How do you turn it into something actionable that speaks to business goals?


The answers lie in setting up a framework in the organization to move and process not just social data, but social data combined with enterprise and public and curated data. We wouldn’t want to withhold that kind of knowledge from you, so we have a new and FREE Oracle White Paper, “The Value of Social Data,” available for download on the subject.


While it certainly doesn’t cover all the bases (that’s why you need a White Paper), here are a few points from Oracle Social VP Product Development Don Springer.


  • Orgs have made significant progress in deploying social CRM, but want stronger, more automated ways to socially enable customer-facing functions.

  • Enterprise data growth is expected to continue at 40% through 2020, driven by consumer generated content.

  • The social CRM process involves listening, engaging (1-on-1), creating relevant content, publishing, establishing and managing workflows, and analyzing.

  • When that process is set up, you then amplify the social value you get by integrating with other core applications.

  • A Socially Enabled Consumer Data Store can provide a 360-degree view of your customers.

  • This store consists of unstructured content that captures customers intentions, interests and needs from social/internal data sources; plus quantified transactional, behavioral & customer profile data in your CX Management Applications.

  • Additional “public” data can be integrated via a cloud-based Data-as-as-Service platform (DaaS).

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

  • We’ve seen a need for more business applications to ingest and use “quality” curated, social, transactional reference data and corresponding insights.

  • The problem for orgs is getting this data into an easily accessible system and having the contextual integration of the data/insights exportable to business applications.

  • Essentially, DaaS becomes a single entry point for public data, able to extract and integrate the right data from the right sources with the right factoring at the right time.

  • The CMO and CIO are collaborating out of necessity to integrate social and enterprise data into a data “pool” so all departments can leverage it.

  • Over time, these analytics become your knowledge base for a data-driven approach to optimization and continuous improvement.


Don’t forget to download the full “The Value of Social Data” paper at your convenience and start pondering what your enterprise’s framework might look like.


@mikestiles
Photo: stock.xchng

Tuesday Aug 06, 2013

Integrated Social and Enterprise Data = Enhanced Analytics. Why a Savvy CMO + Experienced CIO are Necessary to Succeed

This is the fourth in a series of posts on the value of leveraging social data across your enterprise, with Oracle Social VP Product Development Don Springer and Oracle Social Analytics Product Manager Kaylin Linke.

handful of screensIn today’s post, we are going to explore the recent trend, really a necessity, on the collaboration between the CMO and CIO to integrate social and enterprise data into a data “pool” so all departments in the organization can leverage it according to their specific needs. Why is this happening?

  • 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 meaningfully. The CMO buys the social CRM tools, selects the data and hires the staff to drive social relationship management within the enterprise. Usually, marketing are the social experts within the company.
  • The CIO has always been the owner and provider of the enterprise’s traditional data (including customer records such as transactional, operational, and behavioral). In addition, the CIO typically leads the technical architecture decisions to acquire, store, process and make available new forms of consumer generated information to the enterprise.
  • The rest of the enterprise needs access to unified and enriched data, 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 the CIO to manage data & technical architecture and integration interfaces. As a team, they’re being called on to lead the charge on socially enabling their organization.

As discussed in previous posts, the value proposition for big data analytics is already recognized. The hard part can be getting started.

So, you want to integrate Social + 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 be working 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 mechanism.  
For new sources of public data (e.g. digital, curated, social, etc.), many are looking to migrate and outsource this to a cloud-based data-as-a-service provider or DaaS. For proprietary data, this can be stored in a private cloud environment or on-premise. 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 already implemented enterprise data pools (e.g. MDM records).

Step 3: Enrich the data.  
As explained in a previous post on DaaS, the enterprise will want 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 & next generation data pull. 
By creating a shared data pool and sharing best practices, the CMO & CIO can help all functions across the enterprise conduct new insight detections and ongoing actionability through a variety of CX and CRM solutions.

Use Case – Improve Campaigns with Analytics that Leverage Social + Enterprise Data…

Let’s explore one of the most popular use cases for the office of the CMO, a campaign. Assuming the shared data pool is now in place (social + enterprise data), the following analytics-based approach toward optimizing the campaign across digital, social and traditional media channels is improved:

don blog graphic

Pre-Campaign
There are two important areas to analyze for data insights, prior to preparing the campaign:

  • Current Content Performance: what type of content are consumers engaging with the most across your digital & social assets? What times/days of the week are optimal for communication, and is it different between social, digital and traditional media? What is the demographic breakdown of your customer base, fan base?
  • Current Consumer Conversation: what are consumers saying about your brand/products? Is there language that you can echo back, are there current conversations happening that you should be aware of (e.g. a problem with a product, or specific questions, or a gap that my latest campaign could help address), are your competitors doing something similar, what are their current taglines, how are consumers reacting to their products & language vs. your own?

Launch
Leverage the pre-campaign analysis to inform the campaign’s overall strategy & success metrics. Then, do the campaign creative, corresponding content, schedule, and launch.

During Campaign
Perform real-time monitoring to identify opportunities for campaign shifts to improve the outcome while you still can (adjust messaging, profile targeting media mix and media sequencing). Monitoring includes:

  • Quantitative – Track what is working across owned and paid media (reach, impressions, engagement metrics, responses, growth in fans, etc.)
  • Qualitative – Track why the campaign is working by listening to/polling targeted consumers for their themes of interest, desired response propensity, likes/dislikes, why resonating/irritating by targeted profiles, etc.

Post-Campaign
The post-campaign analysis then becomes the learning basis for your next pre-campaign work, along with re-starting your consumer analysis anew because social is ever-changing along with consumer perspectives. So stay fresh.

In addition, the insights learned may also feed into other opportunities – such as identifying key advocates, new, previously unknown opportunities, or new messaging platforms to extend or launch a campaign. By listening to “earned” conversations outside of your normal “owned” channels, you will find new influencers, brand advocates and loyal customers. These relationships can be an advantage for early testing during the soft release of a new product or promotion.

Also, insights viewed alongside the sales results of your campaign can provide you with analytics that provide a more complete picture of success. Over-time, these analytics become your knowledge base to deploy best practices and institute a data-driven approach to get on a path of optimization and continuous improvement.

It will be fascinating to watch how more executives join forces with the CMO and CIO to socially enable their various business functions and leverage the combination of social and traditional data to provide better customer experiences. We are already seeing this from some of our customers that are including Sales, E-Commerce and Support executives into their social corporate guidance teams. In the future, we will continue to shares trends where we see interesting use cases that leverage enterprise data alongside social data.

Photo: SOMMAI, freedigitalphotos.net

Tuesday Jun 25, 2013

Augmenting your Social Efforts via Data as a Service (DaaS)

rack keyThe following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo.

In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management.

Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application.

What is the next step?

Augmenting Social Data with other Public Data for More Advanced Analytics

When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced:

  • It can vary across structured, semi-structured, and unstructured data
  • It can span across content, profile, and communities of profiles data
  • It is increasingly public, curated and user generated

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

As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications.

The following diagram shows the requirements for this next generation data and insights service or (DaaS):

data1

Some quick points on these requirements:

Public Data, which in this context is about Common Business Entities, such as -

  • Customers, Suppliers, Partners, Competitors (all are organizations)
  • Contacts, Consumers, Employees (all are people)
  • Products, Brands

This data can be broadly categorized incrementally as -

  • Base Utility data (address, industry classification)
  • Public Master Reference data (trade style, hierarchy)
  • Social/Web data (News, Feeds, Graph)
  • Transactional Data generated by enterprise process, workflows etc.

This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes -

  • Change management of Public Reference Data across all categories
  • Applied Big Data to extract statics as well as real-time insights
  • Knowledge Diagnostics and Data Mining

As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is:

  • Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply
  • Cost-effective: Pre-integrated into applications so customers don’t have to
  • Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data
  • Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place

Data-as-a-Service (DaaS)

Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS).

Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration.

Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur.

How do you decide the best approach?

Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value.

The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.).

Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved.

data2

Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined 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 your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage.

Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications.

data3

In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM.

In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc.

This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with.

DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.

Photo: Erik Araujo, stock.xchng

Tuesday May 21, 2013

Social Data Part 2: Socially Enabled Big Data Analytics and CX Management

This is the second in a series of posts on the value of leveraging social data across your enterprise, from Oracle Social VP Product Development Don Springer

In this post, I will cover more advanced “next” steps in how to leverage social data within your enterprise’s Big Data Analytics, Business Intelligence and Customer Experience Management deployed applications and systems. This is a follow-up to a post I wrote in April around the first step in implementing a Social CRM approach and the value for your enterprise specific social data.

Social Data Integration Framework to Socially Enable Big Data Analytics and CX Management.

Once you have successfully deployed a Social-CRM Platform as described in the post referenced above, it’s possible to know more than ever before about your customers, prospects and key target segments. Expanding your social listening capabilities to not only capture customer and prospect signals, but also their key profile information along with your results from social engagement, opens up a comprehensive approach to socially enabled big data analytics and CX Management.

The framework diagram below shows a representation of how this infrastructure could look within your enterprise:

Springer1

At the core, is a Socially Enabled Consumer Data Store to provide a 360 view of your customers, integrating:

  • Unstructured content that captures your customers intentions, interests and needs along the ‘Customer Lifecycle Journey’ from social and internal data sources
  • Quantified transactional, behavioral and customer profile data within your CX Management Applications.

As you delve deeper into this new data store, your data starts to have the following characteristics:

Springer2

As this unified view of your customer data comes together, you have the ability to support the following key capabilities in regards to Big Data Analytics and CX Management (leveraging the initial diagram in this post):

Springer1

Let’s dig a bit more into each of the core components within this framework:

Social & Enterprise Unstructured Data - Signal Detection

  • Social. The ability to quickly and consistently filter through all the noise in the publicly available online environment and capture highly targeted, relevant customer/prospect signal information,
  • Enterprise Text (Call Center Transcripts, Chat and Email Logs). Additionally, capture signal detection from your internal customer-to-company internal data sources to provide a unified, consistent and repeatable approach for all customer & prospect real-time and historical signal detection.
Socially Enabled Consumer Data Store (Next Generation)
  • The data repository should be architected to support high performance and horizontal scalability for both structured and unstructured data. The data model should be designed to support your specific CX Management and Business Analytics data models, combining hadoop, map reduction (for unstructured data) and r-base (for structured data) for complete and seamless data access. 
  • Within this environment, customer & prospect signal data should be enriched with your other structured enterprise data (Via CX Management systems and other Business Intelligence customer data) in a continual, near real-time basis.
  • What’s new in this data-model: A combined content perspective – social and transactional.  And a combined profile perspective – profile (marrying internal client profile information with social profile information) and behavioral (demographics and psychographics)
Insight Discovery (built on the consumer centric data repository)
  • An ability for your analysts to uncover new insights across structured and unstructured data by conducting contextual data drill-down about your customers, prospects and key business data.
  • Take these insights and determine if new, unique, high-value Key Performance Indicators (KPIs) can be generated within your business intelligence systems for faster decision making and real-time business management (action via CX Management). 
Business intelligence & Real-time Analytics
  • Repeatable, near real-time dashboard and reporting on existing and newly discovered KPIs to easily see trends, determine important variances & outliers, and track overall performance. “Correlations and patterns from disparate, linked data sources yield the greatest insights and transformative opportunities” - Gartner
  • Real-time alerts based on pertinent conditions. For example, a client may have indicated in Social Media that they are investigating a competitor’s offerings. Analytically, tracking this on a periodic basis for trends across filtered and group KPIs is important for data-driven, objective decision-making across the line of business for executives and their teams. 
CX Management
  • CX Management for Sales, Marketing, Services and Commerce allows your suitable business functions to act on any newly generated signals (alerts). For example, take action on the customer’s signal when they are evaluating a competitor.  
  • Engagement can be managed via your CX Management application’s workflow to match that customer need to the appropriate, company determined response. 
  • Broadcast Delivery, via Marketing Automation solutions, will allow results achieved through specific customer experience interactions to be amplified through targeted segment communication efforts.

At the core, this socially enabled Big Data Analytics and CX Management framework allows your enterprise the ability to integrate your current enterprise data with new sources of public data and corresponding signals for faster decision-making and real-time ‘ROI-oriented’ action.

This post covers some pretty advanced concepts. In my customer interactions, the more savvy and advanced enterprises are just now looking to consolidate their successful experiments into a unified approach described in the diagram above. Hopefully, this post provides you with a suitable a framework to begin thinking about your own enterprise approach for socially enabling your key external facing business functions.

Based on reader feedback, I’ll plan on writing some additional posts highlighting ‘best practices’ from where we are seeing specific customer value from the above approach.

In future posts, I’ll also be bringing in other colleagues to discuss in more detail aspects of socially enabled big data analytics and CX Management including topics like: public Data-as-a-Service (DaaS), lessons learned on data enrichment, a market perspective on data-matching (connecting offline to online profile information), etc.

Tuesday Apr 16, 2013

The Value of Enterprise Specific “Social Data” - Social Data within Social Customer Relationship Management (Social CRM)

This is the first in a series of guest posts from Don Springer, VP Product Development for Oracle Social and Pat Ma, Principal Product Marketing Director for CX and CRM on the value of leveraging social data across your enterprise.

shopperLately, we have been meeting with marketing, sales, services and IT executives at very large Financial Services, Consumer Products, Retail and Technology companies. They have all made significant progress in deploying social customer relationship management (Social CRM) capabilities, but are looking for more automated and powerful ways to socially enable their external customer facing functions. In essence, they want to do more with their Social Data. With enterprise data growth expected to continue at 40% through 2020, driven by consumer generated content, getting value from this data is becoming increasingly and strategically important.

In this post, we’ll cover the basics of first implementing a Social CRM approach, and the value your enterprise specific social data. In a future blog post, we will cover more advanced “next” steps in how to leverage social data within your enterprise’s Big Data Analytics, Business Intelligence and Customer Experience Management deployed applications and systems.

Below is a diagram that highlights a general process for leveraging Social Data as part of an overall Social CRM approach. Think of this as a process that tracks your social efforts across your customers’ life-cycles, starting with listening and point-to-point engagement to more broadcast communications efforts in a repeatable and flexible fashion.

Social CRM Process

chart

1. Listen.  The enterprise wants to listen to what people (customers, prospects, and influencers) are saying about their brand on social media channels.

  • Your customers are talking about your brand on social media channels. They are posting, tweeting, commenting, sharing, complaining and liking your brand.
  • Through Social Listening, the enterprise should figure out what their constituents are saying en mass, analyze sentiment, hear what they like and don’t like about your product, and know if they intend to purchase your product or not.
  • Your social listening approach needs to be accurate and filter out the irrelevant “noise”, to get to pure customer signal for analytics and engagement.

2. Engagement (1-on-1) The enterprise wants to engage with relevant social signals to interact with their customers, and determine how those 1-on-1 engagements perform. 

  • This can be done by asking your customers various questions, responding to their posts and comments, and creating engagement applications like contests and polls. 
  • Your social engagement should be used to listen and respond to social posts. Social posts should be automatically categorized by your Listen engine and flow from multiple social networks into one “inbox” designed to make managing your community easy and efficient, within your appropriate business function (sales, marketing and support).

3. Content and Apps (within your Enterprise’s Social Assets) The enterprise should leverage the lessons learned from your 1:1 engagements to scale what works within relevant content and apps you create, whether it’s user-generated contests, polls, videos, or other interactive content.

4. Publish (message through your social channels’ communities) The enterprise should continue to build on its learning on all your interactions with your fans and followers to publish and amplify relevant content to multiple social media channels.

  • Create great looking landing pages and publish to multiple social networks or embed on any website. 
  • This should be done specifically within your various channels focused on marketing, sales, service, and commerce.

5. Managed Workflows The enterprise should develop and deploy specific workflows so your assigned business functions (Sales, Marketing, Service and Commerce) are communicating the right message to the right customer at the right place and the right time.  

  • Social media teams are growing and becoming more global. Why take the risk of someone in your organization publishing off-brand information?
  • By using your listening engine to auto-tag customer signals, managed by function appropriate workflows, you can better control your points of communication (1:1, through content, apps and publishing) to improve ROI.

6. Analytics. The enterprise should create a culture that always analyzes your results and metrics to quickly capture lessons learned to establish a continuous improvement process.

  • This will enable you to show ROI on all your social media investments, pre, during & post-campaign across your owned & earned media to improve social performance.
  • This helps you optimize your efforts over time to get more lift and value from your resource and communications spend.

Makes Sense?

Once your enterprise has this Social CRM approach in place and functioning, you can take the broader “next” step to amplify your social value through integration into your other core applications, which we’ll cover in a future post.

To whet your appetite, you can socially enable your enterprise by creating a 360o view of your enterprise customers (both content and profile) to support:

*Business analytics across all forms of structured (customer transactional and behavioral data), semi-structured (enterprise text sources that capture your internal customer conversations via chat, email, call center, etc.), and Social CRM unstructured data for:

  • Big Data insight discovery – finding insights you did not know existed
  • Business Intelligence - developing dynamic, real-time dashboards, reports and alerts for rapid decision-making.
*Customer Experience Management applications already deployed and in use by your enterprise’s Customer Service, Sales and Service/Support functions for near real-time action (customer experience management).

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