1.
Social Data Intelligence
An Altimeter Group Webinar
Susan Etlinger, Industry Analyst
September 5, 2013

2.
Agenda
I. The State of Social
Analytics
II. Making Social Data
Actionable
III. Building A Data-Driven
Organization
IV. Six Dimensions of
Analytics Maturity
V. What’s Next
2

3.
The State of Social Analytics

4.
Social data is not an island
4

5.
It is used across the organization

6.
Organizations want context

7.
Source: Altimeter Group
It has a large and diffuse ecosystem
7
Publishers
(Social Networks, Community, Enterprise Collaboration)
Social Data Platforms
Social Applications
Listening/Moni
toring
Engagement
SMMS)
Publishing Analytics
Enterprise Applications
CRM BI
Market
Research
Email
Marketing
Fraud
Detection/Risk
Mgt
Supply Chain

8.
Manny’s steakhouse is celebrated for
its quality steaks, but when a sudden
change in sentiment related to its
meat quality surfaced via social
media, the company was able to
pinpoint the precise dates, times, and
incidents of faulty product.
Social data turned up the heat for Manny’s
Steakhouse, prompting action
8

9.
Parasole and Manny’s quickly
identified 6 suspect samples, lined
them up, tasted them, and
immediately discovered the problem.
Parasole uses social data opportunistically, to
protect product (and brand) quality
Using social data to optimize supply
 Cut ties with the meat supplier
 Provided employee training to smooth
the transition
 Updated employee incentive programs
to incorporate social ratings and reviews
9

10.
So…what is social data intelligence?
Social data intelligence is insight
derived from social data that
organizations can use confidently, at
scale and in conjunction with other
data sources to make strategic
decisions.

11.
Challenges of integrating social data
Multiple internal
constituents and
interests
• Community managers
& customer service
• Marketing and digital
• Risk, compliance,
legal, HR
• Market research
Requires new
analytical
approaches
• It’s big data!
• Variety
• Velocity
• Volume
Social Data (and
sometimes
analysts) lack
enterprise
credibility
• Social data is new
• It lacks standards
• Analyst roles are new

12.
Characteristics to consider

13.
Making Social Data Actionable

14.
1. Identify your business goals

15.
2. Define core social media metrics
Business Goal Social Media Metric
Brand Health Brand sentiment over time
Marketing Optimization Impact of campaign X on awareness
Revenue Generation Impact of social media on conversion
Operational Efficiency Impact of social media on call deflection
Customer Experience Impact of social media on NPS
Innovation Impact of social media on speed to market

16.
3. Prioritize Your Metrics
16

17.
Prioritization Process
1. List the core set of metrics you would like to evaluate
2. Score them as follows, on a scale of 1-5, where 1 is the
lowest, and 5 is the highest
• How useful this metric is to your organizationValue
• Your organization’s ability to deliver this metricCapability
• The time and staff power it will take to deliver this
metric
Resource
• The degree to which other metrics or future
decisions rely on this metric
Dependency
17

18.
Symantec has operationalized social data
Symantec harvests social data from across the web.
They route data to the central social business team, where
they determine the business function best equipped to
serve the customer. They classify Actionable Internet
Mentions (AIMs) into seven categories comprising different
business functions. The seven classifications are:
1. Case: Request for help resolving real-time issue
2. Query: Question that doesn’t require support resource
3. Rant: Criticism that merits brand management
consideration
4. Rave: Praise from Symantec brand advocate
5. Lead: Pronouncement of near-term purchase decision
6. RFE: Request to enhance a product with a new feature
7. Fraud: Communication from an unauthorized provider of
Symantec products
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• Marketing
• Customer Support
• Engineering
• PR
• Product Management
• Legal

19.
Results across the enterprise
Customer Experience
Numerous support cases resolved
Converted many ‘ranters’ to ‘ravers’
Product Improvement
Rapidly identifies key areas to
prioritize R&D
Lead Generation & Nurturing
Generated hundreds of business &
consumer leads
Risk Mitigation
Uncovered hundreds of fraudulent
product pilots

20.
Building A Data-Driven Organization

21.
Aspire to a (more) holistic strategy
21

22.
Scope: The number of internal groups that work with social data
and the scope of data to be measured: which platforms, which data points, and
why.
Define what you’ll do and
what you won’t do.
Inventory
Documented
methodology
Documented
success criteria

23.
Mastery means you can easily answer questions
such as:
• What social data do we have at our disposal?
• What do we track? What is our methodology for social data?
• What are the critical success factors to scale this across the
organization?
1. SCOPE
What success looks like
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24.
Strategy: The extent to which social data — and metrics — is in
alignment with strategic business objectives across the organization.
Demonstrate the connection to the
outcomes the C-Suite cares about.
Brand reputation, revenue
generation, operational savings,
customer satisfaction, etc.

25.
Maturity means every social media initiative
— however small or short-term — has a clear
set of goals and metrics that define success.
2. STRATEGY
What success looks like
25

26.
Context: The extent to which the organization is able to view
social data in various contexts to understand what is typical, what is unusual,
and the drivers for each.
Learn what “normal” looks like.
How social data
changes over TIME
Multiple outliers
gain significance
Look at existing
metrics
Consider the
competition– but not
too much

27.
3. CONTEXT
What success looks like
The top maturity marker is the existence of clear
benchmarks against:
• Past history
• Enterprise signals
• The competition
27

28.
Governance: The extent to which the organization has
developed, socialized, and formalized processes related to workflow,
collaboration, and data sharing.
Identify the areas where you have
inadequate processes or policies.
Data sharing
Executive support

29.
4. GOVERNANCE
What success looks like
Governance maturity means that:
• Social data measurement processes are documented,
socialized, and understood company-wide
• Workflows are clear, automated, and scalable
• Approach in context of organization’s cultural norms
29

30.
Image by coreburn used with Attribution as directed by Creative Commons http://www.flickr.com/photos/coreburn/487357814
Metrics: The extent to which metrics have been defined and
socialized throughout the business
Define, contextualize, and prioritize
core metrics.
Ability to articulate all criteria and
process by which metric is
evaluated
Benchmarks & KPIs: decision-
making vs. performance

31.
5. METRICS
What success looks like
The keys to metrics maturity:
• Definition
• Prioritization
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32.
Data: A strategic approach to the data and platforms at your disposal
Know thy social data, platforms, and roadmap.
Understand social action
vs. social text
Know your platforms
(capabilities, limitations,
TOS, APIs, etc.)
Warehouse social data

33.
6. DATA
What success looks like
Maturity in the data dimension requires:
• Understanding of data types, sources, context, influence
• Resources who understand and make best use of
platforms, and conform to their terms of service
• Approach to integrating social data into other business
critical data streams, big and small
33

34.
Caesar’s to integrate social data across 50+
casinos, hotels, and golf courses worldwide
Across a vast empire of brands and
locations, Caesar’s realizes the value of its
data lies in its ability to inform the customer
journey across channels and touchpoints.

35.
Aggregate, then analyze
Caesar’s is undergoing a mass integration project, aggregating
data across offline and online advertising channels, such as
display, email, organic, search, and affiliate.
“The goal is to understand both online and
offline touchpoints along the customer
journey and how they vary across segments,
media types, and brands.”
–Chris Kahle, Manager of Web Analytics, Caesar’s

36.
The goal: understand the customer journey
Building preference models
Using previous purchase data +
engagement history (online and
offline)
Gaining insights
Aggregating behavioral preference
data informs more efficient,
strategic, and timely investments, at
customer and organizational level
Driving loyalty
Tying pre-purchase + rewards data
Online + offline behavior earns customers points
towards rooms, shows, discounts, etc.36

37.
Final Thoughts

38.
Implications and Trends
1. View from the customer in, not the organization out
• Holistic view of customer drives ‘real-time’ and ‘right-time’
engagement
2. Social data is “big data”
• Embracing volumes, variety, and velocity of social data will help
prepare organizations for other data streams to come
3. Big data disrupts organizations
• Consider the HiPPO phenomenon and democratization of decision-
making based on data (vs. intuition)
4. The real-time enterprise is getting more real
• Demand for data at the point of action
38

39.
"Everything should be made as
simple as possible, but not
simpler."
− Albert Einstein
39

40.
Susan Etlinger
susan@altimetergroup.com
susanetlinger.com
Twitter: setlinger
THANK YOU
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