Three "Real Time" Analytics Solutions

After-Warranty Assistance, Social Media Engagement,
and Contract Pricing:
Three “Real Time” Analytics Solutions

Ford Analytics Conference, 2012
Marketing Associates: Keith Shields, Managing Director, Magnify
Analytics Solutions

2/6/2013 1

Outline

 After Warranty Assistance
 Distributing after-warranty assistance based on LTV and “Expector”
models

 Social Media Engagement
 1-1 marketing to “in-market” consumers via Twitter

 Loan Pricing
 Calculating the right “advance” for subprime auto loans

2/6/2013 2

After-Warranty Assistance (CLP):
The Business Questions

 CLP (Customer Loyalty Program), is a program that allows for out-of-
warranty repairs to be paid for by Ford Motor.

 Several salient questions from Ford Customer Service Division (FCSD)
about CLP:
 The program is intended to win back the loyalty of customers who would
otherwise defect. Is it doing that?
 Are we giving it to the right customers? Who are the right customers?
 Currently we spend $60 million. Is that too much? Too little? What should the
CLP budget be?

2/6/2013 3

Why the business questions are hard to answer…
 In the data we know who received CLP and who didn’t.

 So is the “CLP effect on loyalty” just the difference in repurchase rates between those
that received CLP and those that didn’t? We wish it were that easy…

 There has historically been a non-random selection of customers to receive AWA.
 The bottom left graph shows that, on the surface, AWA customers are more loyal.
 The bottom right table shows they are also richer in the variables that make customers more
loyal (prior purchases, recent purchases, etc…).

All Customers:
AWA Awards Received No AWA
70% 7.0% Variables AWA AWA % "Better"
61.0%
60%
6.4%
6.0% New FLM Purchases 1.08 1.33 23%
Used FLM Purchases 0.35 0.4 14%
New Vehicle Purchase

50% 5.0%
% of Customers

5.6%
In-Service New 0.54 0.64 19%
40% 34.1% 4.7% 4.0%
Months Since Last FLM 60 65 -8%
Rate

30% 3.0% Recommend Ford 0.1 0.17 70%
20%
2.4%
2.0% Recommend Vehicle Quality 0.093 0.132 42%
10% 1.0%
Recommend Dealer 0.127 0.189 49%
3.8%
1.0% Warranty Repairs 3.5 7.3 109%
0% 0.0%
Terminating Loan 0.033 0.042 27%
No Warr 0 1 2+
Terminating Lease 0.012 0.011 -8%
# of AWA Awards

2/6/2013 4

Just when you think you’ve found a nugget…

 Overall it looks like, when we control for vehicle disposal (disposal is often used as a proxy
for in-market) AWA significantly increases purchase rates, particularly for the group of
customers that is on the brink of disposal.
 This would be a convenient finding, then we simply give CLP to repair customers who are in-
market for a new vehicle.

5% “AWA lift”
0.7% “AWA lift” for for disposers?
non-disposers?

2/6/2013 5

Just when you think you’ve found a nugget…

 BUT we see in the table below that the AWA effect disappears when we
control for “loyalty score” (comes from a model that explicitly predicts the
likelihood of Ford repurchase).

Cust Group: Disposer

Decile AWA Repurch Non-AWA Repurch AWA Lift
1 0.0% 0.0% 0.00%
2 2.7% 3.2% -0.55%
3 3.1% 3.4% -0.30%
4 5.5% 4.8% 0.71%
5 7.1% 6.9% 0.25%
6 8.9% 8.9% 0.02%
7 12.1% 11.8% 0.38%
8 15.4% 16.2% -0.82%
9 20.5% 21.7% -1.12%
10 34.5% 34.1% 0.36%
All 19.2% 13.8% 5.38%

2/6/2013 6

When you hit a wall, consult with Dr. Lund…

 Bruce Lund astutely points out that conducting a survey of recent paid-repair
customers would address the following barriers to our analysis:
1. Helps us know how we impact loyalty when we deny assistance.
 We don’t know how often assistance is asked for and how often it is denied.
 With the current available data, we can only identify who didn't receive
assistance, but not receiving assistance is not the same as being denied.

2. We have no idea what fraction of the retail repair customers expect assistance,
and the rate at which we deny assistance.
 Is it 90% or closer to 10%?
 Answering the above question will give us some insight as to how big the
CLP budget should be.
 The CLP budget should be reflective of the demand for assistance.

2/6/2013 7

We get lots of answers from the survey…

Those that we were curious about…
 About 20% of paid repair customers either expect or ask for assistance.
 Roughly 8% of paid repair customers are denied assistance (40% of those who expect it).
 This is just over 30,000 customers per month.

And some that we weren’t as curious about, but are worth knowing…
 Of the customers that received CLP, roughly 25% believed their repairs were covered by
warranty. The dealer had not communicated that the repair was being paid by Ford
Motor Company.
 This issue was particularly pronounced among “bought used” customers: close to
40% thought their repairs were warranty covered.
 Of the CLP customers who realize their repairs weren’t covered by warranty, only 28%
think the assistance came only from Ford. 38% think the assistance came from Ford and
the dealer.

2/6/2013 8

The most important finding from the survey…
 CLP creates a substantial increase to a customer’s “attitudinal loyalty”
when the customer:
1. Is highly loyal (high LTV score) AND
2. Expects assistance.
 The table and graph below highlights the finding:

The lift in intended loyalty resulting from CLP spend is most
Expected Got Intended Loyalty: pronounced among high-LTV customers who expect asssistance.
Assistance? Asssistance? Top 2 Box 25 20
CLP Lift: Expect=No 21
No No 78.5% 18
20 CLP Lift: Expect=Yes 16

Expectation Effect
Yes 78.2% Expect - Non-Expect 14
CLP Lift (ppts) -0.3

CLP Lift:
CLP Lift
15 14 12
Yes No 49.2% 10
11 10
9
Yes 75.6% 10 8
6
CLP Lift (ppts) 26.4
5 4
2
2
0 0
Low Medium High

LTV

2/6/2013 9

Creating a tool for real-time decisions…
 The actionable item resulting from the study is straightforward: award CLP to loyal
customers who expect it. But how to implement that item is not as easy. There are
two problems:
1. We are not comfortable implementing a process whereby a customer is asked if he expects
assistance. We likely won’t get a truthful answer.
2. We know the correlation between attitudinal loyalty and actual loyalty is not 1:1. What is it?

 We address #1 by building a model that predicts the likelihood a customer expects
assistance (“Expector” Model). That model is implemented in CKS and scores the
entire U.S. FLM customer base weekly.
 Note: the Expector Model score is also passed to CuDl every week and is used in the
CRC…with hopes of eventually using it in the dealerships.

 We address #2 with the validation study presented here in the next few slides. We take
the customers surveyed in 2009 and tracked their purchase behavior to see if
customers behaved in line with the survey results.
 Note: assuming the relationship between attitudinal and actual loyalty is 1:1, then a high LTV
customer who has a 20% chance of expecting assistance should be eligible to receive:
 .264 (the loyalty lift)* $10,000 (profit per sale) * .2 (prob of expect) = $528 of CLP

2/6/2013 10

Epilogue…Senator and Mrs. John Blutarsky…
 The actionable item resulting from the study is that we can determine the amount of CLP to award
based on two model scores: LTV and Expector
 We have the following formula:
 If LTV >= 80 then Amount of CLP = $10,000 * loyalty lift from CLP * Expector Score

 So what is the real loyalty lift from CLP? It’s actually 7 ppts, not 26 ppts.
 A sales match done on the surveyed customers 2 years post survey reveals the following…
Got
Expector Asssistance? Repurchase Rate
No No 28.1%
(Probability<31%) Yes 24.1%
CLP Lift (ppts) -4
High No 33.8%
(Probability>31%) Yes 41.1%
CLP Lift (ppts) 7.3

 And that there really is a correlation between actual and attitudinal loyalty:
Repurchase Customers Actual
Intent (Disposers) Repurchase Rate
Definitely/Probably Would Not 73 16.4%
Maybe Would 130 24.6%
Probably Would 178 28.0%
Definitely Would 201 41.8%

2/6/2013 11

Outline

 After Warranty Assistance
models

 Social Media Engagement

 Loan Pricing

2/6/2013 12

Social Media Engagement:
The Business Questions…

 Measuring the “Consumer Experience”
 Alan Mulally and Apple…
 The Dealership Experience: Sales and Service
 The Ownership Experience
 How do people share experiences? Traditionally by talking to each other. But how
much today is done through Twitter, Facebook, Blogs?

 By analyzing the comments and sentiment expressed through Social Media
outlets can we glean meaningful insights about the Ford Consumer
Experience?

 Can we make inference about a consumer’s affinity for Ford…or an existing
customer’s loyalty to Ford?
 If no, then we’re probably not trying hard enough.
 Examples next slide.

13

Google Twitter Search - Ford Comments

 Search: “My Ford Focus is great.”

 I love my Ford Focus, but not so much Ford Service in Northampton Mass.
Thieves.
 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.

 Does this mean we can simply scrape Twitter for the words “test drive”? Seems
like it would be predictive of future behavior…

14

Social Listening Through the Customer Resolution Center

 We create a process by which “in-market” sentiment is mined from Twitter on a nightly
basis.

 The relevant tweets are sent as a batch file to the Customer Resolution Center (CRC).

 CRC agents, when not handling calls, are working with the following web-based
application (this is a simulated version)…note that selecting “Send Offer 1” amounts to
tweeting them a URL that contains the coupon / offer.
 Having the “clickthru” data makes campaign measurement easy.

AUTHOR FOLLOWERS COMMENT DATETIME
@chamoubooo3 I'm between a 2012
CHELSEYMMILLER 46 9/21/2012 19:19 Send offer 1 Send offer 2 Send info Ignore
Ford Focus and 2012 Mazda 3

I really want that Black on Black 2012
_AINTSHELOVELY 95 9/21/2012 0:19 Send offer 1 Send offer 2 Send info Ignore
Ford Focus. Sexy.

I want a 2012 ford focus...just
JAKUNTRYGIRL 252 9/21/2012 11:16 Send offer 1 Send offer 2 Send info Ignore
because it parks itself. :

Ford focus 2012 is handsome!
YOSHRAMOS 6 9/21/2012 17:10 Send offer 1 Send offer 2 Send info Ignore
I want to have one :)

15

The Opportunity

 Through Twitter alone, roughly 35,000 customers per year express inclination to
buy Ford.

 Applying a result from an analysis of "handraiser campaigns", we assume 15% of
the 35,000 will purchase FLM. This is 35,000 * 15% = 5,250 sales.

 Assuming 20% lift from a targeted offer to in-market customers (derived from a
history of CKS-driven campaigns), we estimate that a conquesting campaign
directed at in-market "social-media leads“. This is 5,250 * .2 = 1,050 incremental
sales.

 Assuming $10,000 profit per incremental sale, the "Conquesting" element of the
Social Media initiative is worth 1,050 * $10,000 ~= $10 million per year.

16

Outline

 After Warranty Assistance
models

 Social Media Engagement

 Loan Pricing

2/6/2013 17

Loan Pricing:
The Business Questions

 Subprime auto lenders don’t really reject any applications.

 So, find a way to approve all contracts without compromising profitability per contract
(for a mid-market sub-prime automotive lender).

 “Fit a contract to a customer”. Implemented an algorithm for finding the contract (APR,
loan to value, term…) that guarantees a targeted return, given the credit quality of the
applicant.
 Custom-developed, web-based originations systems (available through Zoot and
Magnify) house this model.

 Epilogue (“Senator and Mrs. John Blutarsky”):
 After implementation of the model the client saw 49% growth in contract
originations, as well as an increase in profitability per contract. The client has
been posting record profits since launching the model.

2/6/2013 18

Loan Pricing:
The Detail

 Establish a customer-level credit score that is a function of credit bureau variables only.

 The credit scores will rank order the customers based on their likelihood of repossession /
charge-off, and that the score gets worse as the dealer attempts to make the contract less
affordable, either through increasing the interest rate,

 Fit a "score to payback rate" model, which will establish an easy mathematical translation of
the score to an expected fraction of the sum of payments that will be paid back:
 [Total $ paid / (Monthly Payment * Term)] = b0 + b1*credit_score +
b2*payment_to_income + b3*term + b4*loan_to_value + b5 * pre-pay_score + b6 *
fraud_alert + …
 The payback rate gets worse as the dealer attempts to make the contract less
affordable, either through increasing the interest rate, or “maxing out the deal
structure” (high LTV, high term, high PTI).
 As the payback rate gets worse, so does the assessment of the future value of the
contract. FV = predicted payback rate * sum of payments =>
 PV = FV * [payback_baseline1 / (1+i)1 + payback_baseline2 / (1+i)2 + … +
payback_baseline120 / (1+ i)120]

2/6/2013 19

Loan Pricing:
A Note on Payback Baselines
 Developing a “payback baseline” can be an interesting curve-fitting exercise, one worth all sorts
of analytical exploration:
 There are clearly cases when we need something non-parametric:
Payback Baseline:
Transformation Node 3, 36-Month Term
1.00%
0.90%
0.80%
0.70%
Collections Rate

0.60%
0.50% Term 36

0.40%
0.30%
0.20%
0.10%
0.00%
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
Exposure

 And there are cases where a parametric curve like the log-logisitc works perfectly (next
slide):

2/6/2013 20

Loan Pricing:
The Punchline

 After developing the mechanism to quantify the present value (PV) of the cash flows for the
proposed contract, simply divide that PV by (1 + targeted return) to arrive at the ADVANCE
AMOUNT that will achieve the targeted return.

 Here’s the example:
 Amount financed of $10,340
 Interest rate of 26%
 36 month term
 $15,000 sum of payments
 Payback rate model prediction = 60% => FV = $9,000
 The present value of the stream of payments associated with this contract is $7,660
(assuming a typical cash flow curve).
 Assuming a targeted ROI of 12%, the ADVANCE AMOUNT = $7,660 / (1.12) = $6,840
 This implies that there is a $10,340 - $6,840 = $3,500 gap between the amount financed
and the advance…so what does that mean?
 The dealer either gets $3,500 cash down, or lowers the price of the vehicle by $3,500, or
pays a $3,500 fee to the lender.

 These calculations can be done easily for every vehicle on the dealer lot. MA has developed
and originations system that does precisely that. Demo available on request.

2/6/2013 22

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