Big Data, PR and the Future, 9/2014

Big Data, PR and
The Future
Forrest W. Anderson
Independent Communications
Research and Strategy Consultant
Forrest W. Anderson – fanderson@forrestwanderson.com – 415-513-5042 - @ForrestAnderson

What We’ll Talk About Today
 What is Big Data?
 How Big Data is being used in PR and communications
today
 How Big Data will be used in PR in the future
 How you can use (or prepare to use) Big Data today
2

What Is Big Data?
3

What is Big Data?
 According to Wikipedia
 Big data is an all-encompassing term for any collection of
data sets so large and complex that it becomes difficult to
process using on-hand data management tools or
traditional data processing applications.
4

What is Big Data?
 According to Wikipedia
 The trend to larger data sets is due to the additional information
derivable from analysis of a single large set of related data, as
compared to separate smaller sets with the same total amount of data,
allowing correlations to be found to "spot business trends, prevent
diseases, combat crime and so on.”
 The data is coming from traditional sources, the Internet and the
Internet of Things
 Government data
 Product warranty forms
 Internet
 Smart phones
 Cars
 Smart houses
 Personal health monitoring
 etc.
5

What is Big Data
Big
Data
Data Base 1
(e.g.
Facebook)
Data Base 2
(e.g. Patient
History)
Data Base 3
(e.g. Grocery
Purchase
Data) Date Base 4
e.g. (Drug
Store Purchase
Data)
Data Base 5
(e.g.
Contribution
History)
6
Many to One

Big Data Challenges
 How can you be sure the data is what is purports to be?
How do you clean up the data?
 Is all the data on individuals, really specific to those individuals?
 How do you format the data base
 How do you manage the data base to deliver good data?
 How do you visualize and grasp the data?
 How do you manage the data to get insights that can drive
communications programs?
7

Big Data for PR
 The challenges I list above are real challenges
 But, in my view, they are not your challenges
 In almost all cases, other people will ensure the quality
of the data
 It will behoove you to have a way to confirm they are right
 Trust
 Think critically about the data
 Some insight into how the data bases work
 Questions you can use to determine whether there are flaws
8

Big Data for PR
 What PR people must do is learn to ask questions of
the data
 Big data is just a big database that can answer lots of
questions
 Your job as communications professionals is to ask the
questions that will
 Give you insights that will enable you to execute
communications programs
 Drive behaviors
 Help your organizations achieve their goals
9

Big Data for PR
 You must become adept in:
 Information-driven communications
 Analytics
 KPIs
 Thinking critically about data
 These are really one and the same
 It’s not so much Big Data as it is Analytics!
10

Thinking Critically
About Data
11
1.0
5.0
7.0
15.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
No exposure Consumer Ad Manufacturer's Ad Both
Google Ad Sales Increase (%)
Credit: Florian Zettlemeyer

How Big Data
Is Being Used in PR Today
12

PR Examples Sparse
 Difficult to find examples of PR using Big Data at this
point
 Anecdotally hear about Fortune 100 companies using it
(IBM, Chevron)
 Influential PR leaders
 Know what questions to ask (insights they need) to drive
successful communications campaigns
 Have access to data scientists within companies
 Manage data bases
 Apply statistics and other analytics to derive insights
 Big resources, including big budgets
13

MasterCard
Conversation Suite
14

MasterCard
Conversation Suite
 Web-based analytics tool supported by a global team of
social media experts who monitor, analyze and engage
in conversations around the world, in real-time, 24/7
 MasterCard uses social intelligence to
 Shape ideation and product development
 Spur brand engagement and awareness
 Track brand performance and reputation
 Provide context around what consumers really want
15

MasterCard
Conversation Suite
 Earlier this year, released results from first ever global
study of mobile payments space, based entirely on
insights gleaned from
 85,000 different comments
 Scattered across social channels like Twitter, Facebook,
blogs and forums
 43 markets
 26 languages
16

MasterCard
Conversation Suite
 Those who had already used a mobile money service
were less positive and enthusiastic about the idea than
consumers who hadn’t tried one yet.
 Early adopter sentiment was 58% positive, while for
those yet to adopt was 76% positive. Also differences
by global region
 MasterCard then used these insights to
 Refine its MasterPass e-payments service
 Re-orient marketing campaign
17

The 2012 Obama Campaign
Source: “The real story of how big data analytics helped Obama win,”
InfoWorld, February 14, 2013
18

The Obama Campaign
19

The Obama Campaign
 "We were going to demand data on everything, we
were going to measure everything ... we were going to
put an analytics team inside of us to study us the entire
time to make sure we were being smart about things."
 [To ensure everything was measured, staff were
evaluated on whether they entered data. The mantra
became:] "If you didn't enter the data, you didn't do the
work.”
-- 2012 Obama Campaign Manager, Jim Messina
20

The Obama Campaign
 100 analytics staffers
 50 worked in a dedicated analytics department
 20 analysts spread throughout the campaign's various
headquarters
 30 in the field interpreting data
21

The Obama Campaign
 According to Chris Wegrzyn, director of data
architecture for the Democratic National Committee
 The key measurements centered on
 The data itself—the facts about the electorate and the
campaign operation
 Modeling—to understand the electorate at the individual
voter level
 Experimentation—to help the campaign learn how its actions
actually influenced people
22

The Obama Campaign
 According to Chris Wegrzyn, director of data
architecture for the Democratic National Committee
 The key performance indicator for the campaign
 The number who planned to vote for Obama, divided by
those who planned to vote overall
 Three levers to maximize that number:
 Registration—encourage target voters to register
 Persuasion—persuade the undecided to vote for Obama
 Turnout—Do all they could to ensure that Obama voters would
show up to vote on Election Day
23

The Obama Campaign
 Organization
 The field team, the personal face of the campaign:
 The people on the ground organizing volunteers
 Handling registrations
 Encouraging turnout, and so on
 The digital team responsible for
 Online presence
 Email campaigns
 Online fundraising
 Social media, and more
24

The Obama Campaign
 Organization (cont.)
 The communications and media teams, responsible for
 Obama's personal messaging with interviews
 Ad buying, and so on
 Finance focused on the overall campaign fundraising
strategy
 Executive buy-in from campaign manager Messina was
essential
 Without that authority, any ambitious initiative might have
been sidestepped or dropped altogether
 Team chose HP Vertica to drive Big Data Engine
25

The Obama Campaign
 Analytics in action examples
 AirWolf
 Built to integrate the field and digital teams' efforts
 Common problem in prior campaigns was that
 Field teams' actions, such as recording a person's particular interest
in voting issues
 Could not be easily followed up by the Digital team (for example,
with email correspondences)
 With AirWolf
 Voter contacted by the field team in a door-to-door campaign,
 Voter's particular interests were recorded and fed back to Vertica
 Digital team ran email blasts from the local organizer to voters, each
corresponding to a voter's favorite campaign issues
 Greatly enhanced the ability to pinpoint messaging and made it
more feasible to sway voters
26

The Obama Campaign
 Analytics in action examples (cont.)
 Media Optimizer--enable much more targeted ad
purchases
 Prior to Media Optimizer, TV ad buys were based on broad
demographics--costly and inefficient
 With Media Optimizer, the campaign
 Used statistical analysis to identify the target voters in the DNC
database
 Enriched voter data with demographics data from TV ratings as
well as advertisement pricing data
 Results were fed back into Vertica and reanalyzed for further
tuning.
27

The Obama Campaign
 Analytics in action examples (cont.)
 Media Optimizer (cont.)
 Overall picture combined
 Likely voters for Obama
 Shows they watch
 Prices of the ads
 Analysis feedback loop
 One result: Obama campaign purchased twice as many
cable TV advertisements as the Romney campaign
 Many during niche programs
 Targeted the precise demographic slices the Obama campaign
was trying to reach
28

The Obama Campaign
 Campaign lessons
 All analytic solutions
 Were a combined effort of analysts, engineers and communicators
 Were time-sensitive, implemented in weeks rather than months
 Were built around an unconstrained, yet centralized environment with
Vertica.
 The analyst-driven organization empowered the team to achieve a
number of key objectives
 All the data from the disparate teams was brought together within
Vertica, enabling a 360-degree view of the data
 Analysts could answer nearly any question quickly and easily, no matter
where the data originally came from
 The platform was continually improved thanks to its built-in feedback
loop
29

The Obama Campaign
 Campaign lessons
 A unified big data analytics environment seems sure to
take its place as a standard requirement for campaigns to
come
30

Stephanie Sharp
Explains VoteSharp
31

How Big Data Can and Will
Be Used in PR Tomorrow
32

The Promise of Big Data
 When you look for applications of Big Data in PR, most
of what you see is “We’ve monitored trends in Twitter
and Facebook data.”
 That is not the promise of Big Data
33

The Promise of Big Data
 An extraordinarily refined understanding of target
audience wants, needs, and activities, including media
habits
 The ability to communicate with specific individuals
keyed to those specific wants, needs, activities and
media habits
 Making not only communications but business
decisions based on this data
 MasterCard product design and marketing decisions
 Obama election targeting and messaging
34

How You Can Use
(Or Prepare to Use)
Big Data Today
35

Recipe for Communications
Success with Big Data
1. Ask the right questions
A. Who is my target?
B. What must they do to help my organization achieve its
goals?
C. How can I translate that into KPIs?
D. What do they know, want, do, use, think?
E. What media do they use? How? Why?
F. And more
G. Think creatively, but constrain yourself by your
organization’s business objectives
36

2. Find people who know how to manage data bases and
extract insight from them
A. Data base building and grooming
B. Data base analysis using advanced statistics and other
analytics
C. Look inside your organizations, the data doesn’t need to
be big, it needs to be accurate and applicable
D. Understand what they do
E. Explain to them what you do
F. Think critically about the data
37

3. Put all of that together to come up with efficient and
effective communications programs
4. Get buy-in from your senior executives, so they
A. Approve and support you as you do things differently
B. Accept your measures of results
38

39

Big Data, PR and
The Future
Forrest W. Anderson
Independent Communications
Research and Strategy Consultant
40

Big Data, PR and the Future, 9/2014

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