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 IntelligenceMindy Deatrick, Lead Consultant, Decision Sciences

magnify • simplify • amplifyMagnifyAnalytic Solutions

DMA Detroit 2013: Applications of Social Media to 1-1 Marketing

2Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.

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)

http://www.socialmediaexaminer.com/how-to-build-a-free-social-media-monitoring-dashboard/




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.

http://www.slideshare.net/dmadetroit/aims2012-marketing-associates-quantifying-the-buzz-effect







The Pervasive Interest in Social Media

• 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

• (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.

http://twitter.com/#!/DSchawelPhotos/status/61842302892638208

http://twitter.com/#!/DSchawelPhotos/status/56090933577519104




Social Listening

• 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

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

• 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)

• (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 = $14,00040% improvement

Population OrganicTake Rate

OfferTake Rate

Incremental “Takers”

100,000 10% 11% 1,000

Incremental Revenue

$250,000

Model Rank Population Organic Take

RateOffer

Take RateIncremental

“Takers”

1 25,000 18% 19.80% 4502 25,000 14% 15.40% 3503 25,000 6% 6.60% 1504 25,000 2% 2.20% 50

IncrementalCost

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 CorrelationsKeyword Correlation buy 0.528135drive 0.170005want 0.24524love 0.390919new 0.453232boughthot 0.24882proud 0.162253cooltrade 0.689701look 0.237968get 0.516324ins -0.15259sweet 0.307868see 0.409744own 0.501612comp 0.659461will 0.729098opinion 0.157918!!!! 0.285816purchase 0.114163http 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

8-Nov-1

1

5-Dec

-11

1-Jan-12

28-Jan-12

24-Feb-12

22-Mar-

12

18-Apr-12

15-May

-12

11-Jun-12

8-Jul-1

2

4-Aug-12

31-Aug-12

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17-Dec

-12

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9-Feb-13

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15-month sales

Top 4 Keyword trends [15-months]




Tactical Applications: Engage “In Market” Customers With Private Offers

• 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 6The 2012 Lincolns are handsome!

I want to have one :)9/21/2012 17:10 Send offer 1 Send offer 2 Send info Ignore

JAKUNTRYGIRL 252 I want a new Lincoln...just because it parks itself. :\

9/21/2012 11:16 Send offer 1

Send offer 2 Send info Ignore

_AINTSHELOVELY 95 I really want that Black on Black 2012 Lincoln MKZ. Sexy.

9/21/2012 0:19 Send offer 1 Send offer 2 Send info Ignore

CHELSEYMMILLER 46@chamoubooo3 I'm between a 2012

Lincoln MKZ and a Volvo S609/21/2012 19:19 Send offer 1




Tactical Applications: Prospecting Student Loans

• 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)

• 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)

• Sameer Desai, Homer Simpson, and crayons

• Time at address and checking accounts

• Originations risk, behavioral risk, and pricing




Closing Remarks

• 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

• At this point I will listen.


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Sameer [email protected]

Christina [email protected]

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Betsy [email protected]

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

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