Applications of Social Media to 1-1 Marketing: Listen, Then Talk

Applications of Social Media to 1-1 Marketing:
Listen, Then Talk
Magnify Analytic Solutions:
Keith Shields, Chief Analytics Officer
Roni Leibovitch, Manager, Digital Intelligence
Mindy Deatrick, Lead Consultant, Decision Sciences

magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2013: Applications of Social Media to 1-1 Marketing
About the Presentation…
• Applications and Cheerios: Listen, PREDICT, then Talk
• Tons of applications, many of which are devoted to broadcasting messages to
consumers
• “Listen, Then Talk”: Social Listening
• From Dr. Cliff Lampe, Professor, UM School of Information: “Social Media should NOT
just be used as a broadcast channel.”
• From Sandy Carter of The Social Media Examiner: “The first step when considering
social analytics is to establish a listening strategy. In social media, listening acts as a
guide through the ever-changing and interesting world of the blogosphere.”
• Difficult to find a definition of social listening, so I’ll make one up (next slide)
2
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.

Our Perspective…
• Social Listening: The discipline of mining social media outlets for user-provided
textual data containing predictive signals about customer behavior.
• Our aim: predict consumer behavior, enhance 1-1 engagement
• 2012 AIMS: Quantifying the "Buzz" Effect: Integrating Social Media with Loyalty &
Defection Models
• http://www.slideshare.net/dmadetroit/aims2012-marketing-associates-quantifying-
the-buzz-effect
• Retention of customer data, predictive models built off that data, and targeted
campaigns based on those predictions are at the heart of the competitive CRM
advantage.
• In absence of accurate predictions of customers’ behavior, what makes us think they
will accept a particular offer or respond to our messaging?
• Data scraped from social media outlets are highly predictive.
3

The Pervasive Interest in Social Media
4
• A lot of it, I think, is attributable to big, impressive numbers. Again from Dr. Cliff Lampe:
• Facebook has 1.2 billion users, Twitter has 500 million, LinkedIn has 200 million.
• And they are engaged: Facebook users share 684,000 pieces of content per minute, YouTube
users upload 48 hours of video per minute, and Twitter users send over 100,000 tweets.
• BTW, in our 2012 presentation to DMAD, we (Roni Leibovitch, Mindy Deatrick, Keith Shields)
noted that, per year, roughly 35,000 Twitter users express interest in buying a Ford.

Social Listening
5
• (More specific to our clients) The discipline of mining social media outlets for user-
provided textual data containing sentiment about, or potential inclination
towards, relevant products or services.
• Back to our 2012 presentation for an example:
• Got my new computer yesterday and can't wait to get my new 2012 Ford Focus SEL in 4-6
weeks! 23 Apr
• Am test driving Hondas and Fords 7 Apr
 We’d like to have a mechanism for intervening here. On April 7 this person indicated
he was facing a choice between buying a Honda and buying a Ford.

Social Listening
6
• What’s missing from the definition and the example is what you do with the
sentiment. What’s our STRATEGY?
• Sandy Carter of The Social Media Examiner tells us the final step in establishing
a listening strategy is to create a “social business strategy”:
• “Listening is not just a discipline. It is an embodiment of a social business strategy
to address the mass volume of data every company faces today and offer a
means for evaluating this information to drive business results.”

Our Social Business Strategy
7
Before discussing tactical applications we’ll take a stab at a social business strategy based on
the work we’ve done for clients to date. There are a few elements to it:
1. Understand that sentiment captured through social listening is a strong signal about likely customer
behavior (from a purchasing standpoint).
2. Establish processes for retaining as much customer data as possible (twitter
handle, email, name, interests) as well as his/her tweets, statements, opinions, etc…
3. Use the signal in the customer’s data and sentiment to accurately predict the likely customer behavior
(“social analytics”) and, on a 1-1 basis, create more enlightened and targeted communications with
customers.
4. Create links from social media data repositories to operational systems at customer touch-points so
“social analytics” can be used in real time.
5. Enable measurement and then measure.

Tactical Applications: Improve Your Predictive Models
8
• Take the emails from the customer base, and using the “find friends” feature, find the
matching Twitter handles…where we can.
• Follow your customers and LISTEN. Use text mining to find tweets related to your
product, classify the sentiment as good, bad, or neutral. Store the statement and
sentiment in the customer data warehouse, just as you would store a customer’s survey
response.
• Rebuild your suite of CRM models – include the sentiment variable. Empirically determine
the nature of the relationship between good / bad sentiment and loyalty (or defection).
• Result: good tweeters are more loyal than neutral tweeters, who are more loyal than bad
tweeters, who are better than customers who don’t tweet about your product.
• Why is it a big deal to improve CRM predictive models? Next slide a refresher.

Why You Want to Improve Your Predictive Models (1)
9
• (Forgive the bandwagon technique) Well…everyone else wants to:
• A recent survey of business technology professionals (see below) indicates that much of
the interest in Big Data and Analytics is driven by (or at least correlated with) the desire
to predict customer behavior:
CRM
Predictive
Analytics

No Model
Use a Model
Profit = $10,000
Profit = $14,000
40% improvement
Population
Organic
Take Rate
Offer
Take Rate
Incremental
“Takers”
100,000 10% 11% 1,000
Incremental
Revenue
$250,000
Model
Rank
Population
Organic Take
Rate
Offer
Take Rate
Incremental
“Takers”
1 25,000 18% 19.80% 450
2 25,000 14% 15.40% 350
3 25,000 6% 6.60% 150
4 25,000 2% 2.20% 50
Incremental
Cost
Incremental
Profit
$240,000 $10,000
Incremental
Revenue
$112,500
$87,500
$37,500
$12,500
Incremental
Cost
Incremental
Profit
$104,000 $8,500
$82,000 $5,500
$38,000 ($500)
$16,000 ($3,500)
Why You Want to Improve Your Predictive Models (2)
• An example involving a pre-approval program: An untargeted (no model), incentivized loan
offer to 100,000 customers increases the “take rate” by 10%.
• The revenue per incremental loan is $250.
• The cost of communicating the offer to 100,000 consumers is $20,000.
• The cost of the incentive is $20 per loan.
• When we apply a predictive model, we split the population into 4 groups (1=most likely to
take…4=least likely to take)

Correlations of Keywords to Action:
Interpreting the Digital Body Signal
15 Month Correlations
Keyword Correlation
buy 0.528135
drive 0.170005
want 0.24524
love 0.390919
new 0.453232
bought
hot 0.24882
proud 0.162253
cool
trade 0.689701
look 0.237968
get 0.516324
ins -0.15259
sweet 0.307868
see 0.409744
own 0.501612
comp 0.659461
will 0.729098
opinion 0.157918
!!!! 0.285816
purchase 0.114163
http 0.735769
What do we have?
• 45% blather (jokes, conversational)
• 35% news driven (forwarded / cl. ads)
• 15% display interest towards product
• 5% high interest / desire towards purchase} Top 4 High-interest key terms:
Buy, Want, Love, Will
0
5000
10000
15000
20000
25000
12-Oct-11
12-Nov-11
12-Dec-11
12-Jan-12
12-Feb-12
12-Mar-12
12-Apr-12
12-May-12
12-Jun-12
12-Jul-12
12-Aug-12
12-Sep-12
12-Oct-12
12-Nov-12
12-Dec-12
12-Jan-13
12-Feb-13
0
200
400
600
800
12-Oct-11
12-Nov-11
12-Dec-11
12-Jan-12
12-Feb-12
12-Mar-12
12-Apr-12
12-May-12
12-Jun-12
12-Jul-12
12-Aug-12
12-Sep-12
12-Oct-12
12-Nov-12
12-Dec-12
12-Jan-13
12-Feb-13
15-month sales
Top 4 Keyword trends
[15-months]

Tactical Applications: Engage “In Market” Customers With Private Offers
12
• LISTEN to tweets: Use text mining to find Ford “in market” tweets, route the tweets, along
with Twitter handles, to the customer relationship center.
• THEN TALK: Engage the customers through Twitter with pre-determined offers for
customers expressing “in market” sentiment.
• Design the communication so that response can be easily attributed and measured:
AUTHOR FOLLOWERS COMMENT DATETIME
Send offer 2 Send info Ignore
YOSHRAMOS 6
The 2012Lincolns are handsome!
I want to have one :)
9/21/201217:10 Send offer 1 Send offer 2 Send info Ignore
JAKUNTRYGIRL 252
I want a new Lincoln...just
because it parks itself. :
9/21/201211:16 Send offer 1
Send offer 2 Send info Ignore
_AINTSHELOVELY 95
I really want that Black on Black 2012
Lincoln MKZ. Sexy.
9/21/20120:19 Send offer 1 Send offer 2 Send info Ignore
CHELSEYMMILLER 46
@chamoubooo3I'm between a 2012
Lincoln MKZ and a Volvo S60
9/21/201219:19 Send offer 1

Tactical Applications: Prospecting Student Loans
13
• Use the API’s to find people of the “student age”. Gather their interests and hobbies is also a good
thing to do – remember that prop schools (DeVry, ITT Tech, Art Institutes, etc…) are also expensive and
are Title IV eligible.
• Obviously here the sentiment of the tweets and posts is also telling.
• The need for forward-flow programs for private student loans is increasing but the rate at which they
are offered is decreasing. The student lending industry is virgin territory for database marketing and
CRM analytics.
• And the student lending is not a trivial piece of the financial services sector: $1 trillion in outstanding student
debt, #2 behind mortgages.
• 70% of current college students have a loan, 90% of those loans are government originated.
• Since 1986 the cost of living has doubled in the US, and the cost of education has increased by almost 500%.
• Who is more active in social media than people of “student age.” No one quite frankly.
• 83% of 18-29 year olds use social networking sites.

Tactical Applications: Evaluating Credit Risk (1)
14
• Using the student lending example as a springboard, can we use social listening more
broadly in the financial services industry?
• The answer is yes if we can use it to help us evaluate risk.
• Analytics in the Financial Services industry is all about evaluating risk for the purpose of
understanding how to price and set reserves.
• Back to the Google Twitter search (this guy has 470 followers): My loan servicer
threatened to default on my loan. I found a way around it though. I logged into their site
and typed loan.preventDefault();
• Rightly or wrongly, many employers think what you post on Facebook, what you say
, and how you say it are predictive of what kind of employee you’ll be.
• So by extension…

Tactical Applications: Evaluating Credit Risk (2)
15
• Sameer Desai, Homer Simpson, and crayons
• Time at address and checking accounts
• Originations risk, behavioral risk, and pricing

Closing Remarks
16
• Social Media contain powerful predictive signals about customer behavior. Capturing
those signals through social listening can help you create more informed, more fruitful
1-1 engagements with customers.
• Social listening strategies must align with, and be integrated into, your predictive
analytics and CRM strategies.
• Acquiring and maintaining social listening capabilities requires investment.
• Dr. Cliff Lampe (again): “don’t simply outsource to the youngest member of your
organization.”
• But much of this investment is simply and extension of the investment you’re already
making in CRM: data retention, infrastructure, analytics, and measurement.
Connect with and mention @MA_Detroit on Twitter and stop by the Magnify booth to
stay up-to-date on social listening news and updates!

Questions
17
• At this point I will listen.

777 Woodward Ave
Suite 500
Detroit, MI 48226
p 313.202.6324
f 313.965.2800
Sameer Desai
sdesai@marketingassociates.com
313.202.6306
Christina DeVey
cdevey@marketingassociates.com
313.202.6357
Betsy Berens
bberens@marketingassociates.com
313.202.6343
18Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.

Applications of Social Media to 1-1 Marketing: Listen, Then Talk

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