Friday Feb 14, 2014

What You Should Look for in a Social Listening Tool

Today’s guest post is from Oracle VP eCommerce and Social, CX Applications Business Group Bill Hobbib, offering up some clarity in a space increasingly crowded with vendors, both large and small, about what features and functions you should look for when shopping for a social listening tool. Beware of incomplete solutions.


Social ListeningFrom time to time, you’ll see analyst rundowns of enterprise listening platforms, each using their own criteria, definitions and methodology. In the midst of these varied approaches, yielding varied results, how can a listening platform best be evaluated?


Buyers now require broader capabilities from their social solutions that extend beyond a single department or group within a large enterprise to address the needs of organizations that want to leverage social, such as Marketing, Sales, Customer Service, and Commerce. Enterprises want solutions that support the integration of social data across the business to understand customers at a transactional and an intention & lifestyle level. They are looking for not just listening alone, but listening integrated with engaging, publishing, and analytics.


When considering listening and sentiment technologies, it’s important to note that all are not equal. For example, while different automated approaches to sentiment analysis may yield similar results from an identical dataset, for sentiment analysis to be accurate, the initial data must be clean of irrelevant results.


Cutting through the noise to get the best social data for analysis is challenging. This is where different listening technologies make a difference. And this is why many customers have moved from keyword/Boolean listening technology to more sophisticated latent semantic analysis (LSA) - to avoid the noise, errors, and time to separate signal from noise associated with the keyword/Boolean approach.

social listening table


The best solution is to blend all of the above for optimum results. Important considerations with social listening are: the amount of time it takes to onboard and build dictionaries, the effort to remove irrelevant content, and the automatic pulling of common words. Of what value is social data for business analysis if it takes excessive manual effort to find the signal through the noise, or if the data is noisy or just plain wrong?


Another consideration for a listening platform is out-of-the-box availability of indicators that can capture and filter conversations based on intentions (e.g. purchase, switching, sale/coupon), activities & interests, product attributes like price/quality/customer service, and brand health measures. These get you beyond tracking buzz to actionable insights, such as a customer service rep engaging with an unhappy customer, passing competitive or product insights to a product development organization, or using the insights gleaned from customers to create more compelling content the customers can engage with on social media. Also, given the importance of selling and marketing on a global level, support for listening in multiple languages should be considered, especially for enterprise businesses.


Further considerations important to many customers are the amount of time a listening tool has been available and proven in the market, the amount of time the vendor has been in business, and the financial stability of the vendor.


One last aspect: Altimeter Group looked at innovations in the social space and has written about the trend of integrating social with other customer engagement channels for the best data, targeting, and context. “The result: a technology suite that goes beyond just social, designed to entice CMOs with one-stop shopping convenience.” Altimeter sees further consolidation as tech keeps coming together in larger suites and consolidation occurs as the market evolves.


Over time, the market won’t be able to support so many smaller players. Several social vendors have already ceased operation. Altimeter observes, “This left their customers high and dry and needing to start the search for vital tools all over again. That has been another reason why some companies are looking to the big players.”


In summary, buyers considering social listening solutions must assess several factors. The vendors’ offering should be evaluated for a proven track record with the deepest listening technology to quickly, easily, accurately separate signal from noise and categorize conversations based on intentions. The product or solution strategy should include integration of social with other customer engagement channels. And the vendors’ market presence and financial stability should be assessed on multiple dimensions to ensure they have the customer traction and financial resources to be there for you over the long haul.


Happy shopping.

Photo: imagerymajestic/freedigitalphotos.net

Tuesday Feb 11, 2014

Were the Super Bowl Social Winners REALLY the Winners?

Super Bowl socialWe know who won the Super Bowl.  (In fact we knew that pretty early in the game.) But every year comes the inevitable post-game analysis of which ads performed best, and, more recently, analysis of which brands executed best on social around the event.


IEG’s annual sponsorship survey shows that social enjoyed the highest increase (+14%) in importance of any channel for activation (+14%), putting it just shy of PR.


It was interesting to watch the various strategies in play. More than ever, ads did not debut during the Super Bowl. They were released either in part or in full prior to the event on digital and social to get buzz started so the buzz would hit its zenith on the big night.


Other brands formed real-time “war rooms” in the hopes of capturing a moment such as Oreo had last year when the lights went out in the stadium. Such a big brand moment however did not occur this year, despite being baited by Joe Namath’s coat and premature coin flipping.


Some brands did big giveaways. Others staged stunts while still others stuck with the tried-and-true user generated content submissions. And, oddly, a great many brands all adopted the same tactic at once, which was to monitor and inject themselves into other brands’ social conversations.


Well? What worked?


Traditionally, social success is measured on a volume metric, Share of Voice (SOV). If that’s the way you’d like to judge, here are your winners:

  1. Budweiser 12.92%
  2. T-Mobile 11.63%
  3. RadioShack 10.37%
  4. Microsoft 6.58%
  5. Coca-Cola 6.17%


But wouldn’t it help to know if the ads made viewers experience generally positive emotions toward and around the brands? That’s a measure of sentiment. Here again, Budweiser wins with 70% positive sentiment. T-Mobile also did well with Tim Tebow’s help, as did RadioShack with its use of nostalgic celebrities.


Beyond these social metrics, we think the deeper you can look, the better. So our friends at Oracle Social Strategy Consulting conceived the Oracle Brand Tracker Index (BTI) to use indicators in the Oracle SRM Listen component to understand the reaction of “engaged consumers” to Super Bowl ads.


The BTI takes positive ad attributes from the SRM indicators (Awesomeness, Humor, Favorite, etc.), subtracts negative ad attributes, then divides the result by each brand's mentions to see whose ads drove awareness + preference.


The results illustrate “buzz” isn’t everything.


Big Game social BTI

#1 on the BTI was Squarespace, which certainly didn’t enjoy the volume of others but performed relevantly and positively with its engagers. The depiction of characters you find on the Internet was relatable and social posts called out specific ones.


RadioShack was a winner in both SOV and BTI (#2), thanks to characters that brought fond memories. Social users had fun spotting each celebrity and calling out their faves on social. #3 Chevrolet left a heartfelt impression with its “Life” commercial around World Cancer Day. Viewers began sharing their own cancer survival stories on social.


#4 Bud Light’s “Epic Night” prank was praised for its use of a non-celebrity, non-actor average guy. Viewers could easily see themselves as that guy. And they continue to love (for the 8th year) Doritos’ (#5) “Crash the Super Bowl” spots. They like that members of the very funny public get the opportunity to be on such a huge stage.


What the Oracle BTI teaches us is what we actually write about quite often. Emotionally connecting with your audience, relating to them, knowing them, and meaning something to them is what will extend your social reach and power. If your fans and followers feel understood, invited and welcome, your brand will be taking home the trophy.


Feel free to take a full look at Oracle Social Strategy Consulting’s Super Bowl report.


@mikestiles
Photo: stock.xchng

Friday Feb 07, 2014

Social Media Metrics Explained

social media metricsWhen it comes to social media metrics, a wealth of info can turn into an embarrassment of riches. Embarrassing because you’re looking at all these figures, assuming they’re all important, but perplexed over which ones to care about and what those numbers are trying to say.


And if you’re confused, you can only imagine what happens when the bosses look at those numbers.


So assisted by definitions from the Oracle Social Cloud’s analytics, let’s explain just what some of those more prominent figures are.


  • New Fans – Oh look, here’s how many people Liked my Page in a set period.
  • Average Number of New Fans – Average number of people who Liked our page in a set period.
  • Removed Fans – Rats, this number of people unliked our page.
  • Fan Sources – Hey, now we know where the people who Liked us came from, be it it our Page profile, recommended pages, mobile page suggestions, search, etc.
  • Page Stories – Here’s the number of times our Page was Liked, our posts were engaged with, someone checked in, mentioned our Page, tagged a photo of us, etc.
  • People Talking About This - The average number of unique users who created a story about our Page in a set period. That was nice of them.
  • Average Engaged Users – This is a really important number. It’s the average number of unique users who created a story or clicked on content from our Page during a set period.
  • Negative Feedback – Okay, it’s painful, but it shows us how many people unliked us, hit the “X” button on our posts, reported us as spam, and hid one post or even all of our stuff.
  • Top Engaged Users – It helps to know who our real friends are so we can treat them special.
  • Referral Sources – Hmm, if that’s where our visitors are coming from, let’s go there more often and invite them!
  • Impressions – How many times content associated with our Page showed up on a browser. This can be Paid like a Sponsored Story or ad, Organic like being seen in News Feeds or on our Page, or Viral like stories about our Page by a friend of a Fan or a non-Fan.
  • Page Virality – Pretty important. People Talking About This divided by Unique Impressions (the number of people who’ve seen content associated with our Page).
  • Average Reach – Also a biggie. The average number of unique users who saw content associated with our Page during a set period, including paid, organic and viral.
  • Engagement Rate – Pay attention to this one. It’s the percentage of users who interacted with our post when exposed to it. To get it, you add Likes, comments, Shares, link clicks, video plays, photo views, and answers, then divide by Reach.
  • Top Posts – See that top performing post? Let’s do more of that.
  • Best and Worst Performing Times - Based on the ratio of posts to interaction over a 90 day rolling period. Maybe we shouldn’t post when our target is asleep.
  • Total Twitter Engagement Rate - The total percentage of people who interacted with our Twitter stream when exposed to it during a set period.
  • Total Retweet Rate - The percentage of people who retweeted a tweet from our stream when exposed to it during a set period.
  • Total Mention Rate - The percentage of people who mentioned our stream during a set period.


Oracle Social Analytics

Which of these statistics rise above the others in importance depends on your immediate goals for social. You might still be in the audience-building phase, you may be trying to activate your existing audience, or you might be trying to show leads, conversions and service successes from social (in which case you’ll probably want to do some integration with other enterprise systems like CRM).


But at least now you’ve got a fine start in being able to listen to what those numbers are trying to tell you.


@mikestiles
Photo: stock.xchng

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 27, 2013

Predictive Analytics: Your Marketing Magic 8-Ball?

No doubt you have at one time or another called a customer service line and were met with a voicemail tree assigning options to various numbers.  Often, and amazingly, the reason you’re calling is not listed among the options. That company’s predictive analytics came up short.


After keeping track of and studying why most people call, the brand whose help you need determined the top options their customers need on the service line. You just fell through the cracks. You have to select “Other.” A likely but even more disturbing possibility is that the brand didn’t study their data at all and simply guessed amongst themselves what options customers would need.


Misfires like that are more impactful now than they used to be thanks to the empowered consumer. They willingly give organizations an enormous amount of personal and consumer data, from a variety of sources and in a variety of ways. In return, they fully expect brands to use that data intelligently to know what customers want, and to provide seamless customer sales and service experiences no matter what the touch point.


Customers really notice when you don’t know them and don’t appear to care about them. They don’t like it, and it’s a short hop to them not liking you.


Predictive analytics not only shows that you know them, it shows you care enough about them to know in advance what they will like or need. It’s not terribly unlike someone who can predict with great accuracy what their significant other will like for a birthday present. Get it wrong and you’re sleeping on the couch.


Predictive Analytics World runs down the definition as “combing through past info to derive models and analyses that help project future outcomes.” It can be used to learn what customers want and learn ways to optimize operations. That means efficiencies, cost savings and happy consumers. What makes that possible? Something we talk about a lot these days, big data.


We’ve also talked a lot about the merging roles of the CMO and CIO. Traditionally, data has been the domain of IT. But increasingly, marketing is being held accountable for the measurable, effective use of that data, which includes predictive analytics. The volume of data and the speed at which it comes in is now an enterprise reality that’s forcing historic structural and operational changes internally. Predictive analytics can intelligently inform those changes.


Managed properly, real-time actions, reactions, and changes in the marketplace can now be added to historical data in models. Managed poorly, or if predictive analytics models are requested but then not deployed successfully in the business, potential benefits are lost. Best Buy learned under 10% of customers constituted almost 45% of its sales and redesigned stores accordingly. Predictive analytics are used to forecast how patients will feel about drugs and treatments. And you’ve probably seen news reports about how city police departments are using predictive analytics to prevent crime before it happens (cue comparisons to “Minority Report.”)


If you determine that customer acquisition, customer sales, customer service, customer retention, customer reactivation, and operational efficiencies are relevant to your organization, it’s time to make sure your social engagement and monitoring tool is pulling in the social data that can be integrated with enterprise data so that quality predictive analytics models can be run.


@mikestiles
Photo: James Barker, freedigitalphotos.net

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

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