Social Data Part 2: Socially Enabled Big Data Analytics and CX Management
By Mike Stiles on May 21, 2013
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:
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:
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):
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.
- 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)
- 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).
- 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 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.