Learn how to see data as useful business intelligence, pull data together and use it and grow your business.

1. Using Customer Data
to Build Intimacy,
Engagement, and
Loyalty
Presented by
Dr. James Lani, CEO
2627 McCormick Drive, Suite 102, Clearwater, FL 33759

2. Let‟s break that down…
Using Customer Data
to Build Intimacy (2-way street),
Engagement (your engagement with data),
and Customer Loyalty

3. The presentation
•What I believe
•What should be done
•The result

4. What I Believe
RAW DATA IS A COMPLETELY
UNDER-USED COMPANY ASSET

5. What should be done?
PROGRAMS DEPLOYED TO
AGGREGATE DATA, CREATE
PIVOT TABLES AND GRAPHS,
CONDUCT REGRESSION AND
CLUSTER ANALYSIS, TO LINK
AND ORGANIZE YOUR
INFORMATION.

6. What results?
USEABLE BI, CI, YOUR
KNOWLEDGE OF YOUR
CUSTOMER INCREASES
THROUGH ENGAGEMENT WITH
THE DATA, RESULTING IN
GREATER LOYALITY

7. If I’m successful in this presentation:
 Change your mindset. Your mind will
change about your data, you will see data
as usefulness business intelligence, and
you will immediately apply it. Have a
relationship to the data; see patterns and
connections.
 Pull data together and use it. You will
aggregate your company‟s data, conduct
appropriate statistical analyses, and use
the information for marketing initiatives.
 Grow your business. The strategies and
initiatives will inherently lead to greater
understanding and customer intimacy, and
result in marketing programs with greater
ROI, your internal resources are more
appropriately allocated, and your net profit
grows.

8. Why do I believe what I believe?
My experience
 Companies spent a lot of time, money, and effort to collecting or buying
data—the profit potential from that data lays dormant within the company‟s
walls.
 Data sits in different silo‟s in the organization (unmerged marketing
department data with financial department data).
 Companies are not appending 3rd party data to potentially strengthen the
customer intelligence gleaned from in-house data.
 Don‟t see strategic business and customer intelligence, and marketing
intelligence as a propriety asset to organization or competitive advantage.

9. Let‟s Talk
about Data

10. Data is Messy
Number
User ID Region $ Sales Start Date End Date Tokens Items Age Type T1v13 T1v14
37854 N 24840 02/10/2003 02/10/2003 144 42 5 1 5
109450 S 97257 01/09/2010 01/09/2010 233 48 4 2 5
111028 M 99011 02/26/2010 02/26/2010 239 22 3 2 4
120757 M 110282 02/10/2011 02/10/2011 256 41 4 3 4
107447 M 95106 10/21/2009 10/21/2009 243 21 5 1 5
119699 NW 108841 01/13/2011 01/13/2011 260 22 4 3 4
110602 SW 98553 02/08/2010 02/08/2010 267 44 5 1 5
TFZ8-
89148 E 76110 01/21/2008 01/21/2008 75WHPQ 175 36 4 3 4
TFZ8-
95408 E 82158 08/26/2008 08/26/2008 VYYEXV 156 32 5 3 5
E 3GVD-
114378 102630 07/01/2010 07/02/2010 DKJS84 261 22 5 2 5
109653 M 97486 01/15/2010 01/15/2010 253 29 5 3 5
E
60462 48832 06/11/2005 09/11/2005 147 19 5 2 5
53701 M 42205 09/13/2004 09/16/2004 143 21 4 3 5
TFZ8-
107652 Mid 95334 10/27/2009 10/27/2009 FP3BUP 228 19 5 3 5
115355 S 103666 08/03/2010 08/03/2010 262 30 4 4 5
119629 S 108716 01/11/2011 01/11/2011 266 19 4 4 4

11. Data CAN be managed,
then used for
visualization,
segmentation, scoring,
and churn
analysis…which will get
y o u t o l o y a l i t y.

12. Data Management: Case Study
Health Insurance Company
 When we met: Company had only a list of customers
and type of health policy (Medicare, supplement, or
Medicare Advantage).
 What we did: We told them the type of data they should
be collecting (i.e., names of those customers that did not
purchase their insurance), then we appended 3rd party
data (e.g., political affiliation, home equity range, type of
charitable contributions, and 40 other influential variables
to predict customers‟ propensity of purchase.
 The result: Company had a clean, enriched dataset,
which led to a lead quality scoring model for their
marketing call-center, projected to increase conversion
rates by 250% and decrease labor costs by 80%.

13. Talk about
Data Visualization
The greatest value of a picture is
when it forces us to notice what we
never expected to see.
— John W. Tukey
Far better an approximate answer to
the right question, which is often
vague, than an exact answer to the
wrong question, which can always be
made precise.
— John W. Tukey

14. What scares us?

15. Whose twittering?

16. Napoleon‟s Army by the Month

17. Data Visualization: Benefits
 Visualize distribution of a variable
and see patterns (e.g., sales in „000
over 1 year) with line charts,
histograms and trend lines.
 See relationships between variables
with scatter plots (E.g., relationship
between calls and number of sales)
and heat maps.
 Classify variables (e.g., percent of
gross revenue by salesperson) with
bar charts and figures.

18. Data Visualization: Case Study
Foreclosure Home Buying Company
 The result: Table 1 showed the home buying company which banks were
selling homes for relative to the assessed value. This intelligence told
the company how much to consider bidding for a $100,000 from Wells
Fargo compared to Wachovia.
Table 1. Sold Price/Assessed Value Percentage by Bank
Bank Auction priced/
Assessed value
Wachovia Bank 45.73
Bank of New York 42.60
Deutsche Bank 40.36
BAC Home Loans 37.68
US Bank 37.68
Bank of America 36.92
Wells Fargo Bank 34.19

19. Market Segmentation:
Better understand who you‟re selling to
and what message appeals to them

20. Market Segmentation: Benefits
Determining the number of market
segments and defining the
characteristics of the segments.
 Additionally, there‟s strategic
marketing…
 Once you know how many segments
exist, you can decide on how many to
go after.
 Once you decide to go after a particular
segment, you can now develop a
marketing program and message to go
after that segment.
 Once you decide to go after a segment,
you can now position your company
brand between that segment and your
strategy.

21. Market Segmentation:
Customers have differentiated needs/buying process
Segmentation has the advantage of differentiating customers
by profitability and needs/buying process
High
Profitability
Low
Homogeneous Customer Differentiated
needs/buying needs/buying
process process

22. Market Segmentation: Example
 An auto insurance study found its customers to have three segments: Price
conscious, Brand loyalists, and Internet buyers.
 Mercury Auto Insurance company pursued the price conscious customer segment
with “low rates from $29.99/month” and positioned themselves as the “low-cost auto
insurance leader.”
70
60
Percent of Customers
50
40
30
20
10
0
Price conscious Brand loyalists Internet buyers
Type of Customer

23. Market Segmentation: Case Study
Eyeglass Company
 When we met: Company had data (n=947) relating to 45 variables such as staff, location, type glasses, and sale
price of the glasses.
 What we did: We segmented data into 5 distinct segments with descriptions, and built an Excel algorithm so that
future stores could be categorized into one of the 5 segments. Validation using a hold-out sample identified a
misclassification of just 2.42%.
 The results: Smaller free-standing stores have a similarly diverse frame selection as larger stores, and moderate
size stores in strip malls sold largest percent of high-end glasses.
Segment 1 Segment 2 Segment 3 Segment 4 Segment 5
Q1: How is your practice staffed Optometrist:80% Ophthalmologist: Optometrist:79% Optometrist:79% Optometrist:
(Credentials)? 40% 90%
Q2: How is your practice staffed (Presence)? Owner:85% Owner:47% Owner:70% Owner:71% Owner:72%
Q3: Where is this practice located? Free standing Free standing Free standing Strip mall:44% Strip mall:84%
building:94% building:64% building:64%
Q4: In-house lab No in-house In-house lab on- In-house lab on- No in-house In-house lab
services:100% site:87% site:93% services:99% on-site:95%
Q5: In-house Lens Jobs per week 10.0 172.7 57.7 0.0 65.4
Q6: In-house percentage of total unit volume 30.2 54.5 42.5 0.0 43.9
Q7: Square feet 485.6 1249.8 810.2 630.4 768.6
Q8: Optical frames 813.9 870.8 913.1 615.8 955.6
Q9: Sunglasses 74.2 124.2 139.0 96.7 149.9
Q10: Children optical frames 65.1 158.7 79.2 83.4 127.0
Q11: Percentage of frame sales: Under $100 15.8 20.7 17.5 15.0 16.3
Q12: Percentage of frame sales: $100 - $200 52.3 40.1 45.1 48.5 32.5
Q13: Percentage of frame sales: $201 - $300 23.4 24.9 26.2 26.9 39.6

24. Lead Quality Scoring:
Who has the propensity to
buy?

25. Lead Quality Scoring: Getting to the
Right Customers
 Identifying those most likely to buy your
product or service
 Minimizing your marketing efforts to
reach those with a high propensity to
purchase
 Response rate increases
 Cost per sale decreases
 ROI of your campaign increases

26. Lead Quality Scoring: Case Study
Health Insurance Company
 When we met: Company had data on
current clients (n=16,947): name, billing
information, and type of insurance (that‟s
it).
 What we did: First, we requested data on
an additional 2,000 NON-customers, then
appended data to these nearly 19,000
individuals.
The appended data included:
 Age range (18-24, 25-29, …75+)  Political affiliation (Democratic, Republican, Independent…)
 Marital status (single, married,…)  Vehicle manufacturer (Acura, Audi, Buick, …Volvo)
 Gender (Male, Female, Unknown)  Neilson Region (East, metro chicago, West, …)
 Religion (catholic, Hindu, Jewish…)  Mean years of schooling (HS, Some college, Graduate)
 Credit card type (Bank, retail, oil…)  Languages spoken (English, Russian, Spanish…)
 Net worth Rank (Top, 2nd, 3rd, …15th)  Heavy internet user (1=most likely, 10=least likely)
 Home value ($1-$50k, $51k-$100k…)  Boat population type (Inboard, Outboard, Other)
 Mortgage loan type (Cash, FHA, VA…)
 Occupation (Business owner, Prof, health services,
teacher, military)

27. Lead Quality Scoring: Getting to the
right prospects
Response Rate with Lead Scoring
% of leads
purchasing Response Rate without Lead Scoring
product or
service
% of leads contacted

28. Lead Quality Scoring: the results
 New leads scored for
4
quality.
3 Platinum
 Scoring algorithm to use
2 Gold
for new leads as they
Silver
come in. 1
Bronze
0

29. Retention / LTV Analysis

30. Retention Analysis: Benefits
The greatest benefits of retention
analysis are:
 Identify customers who will likely
churn, and implement interventions to
halt churn.
 Identify unprofitable customers and
force churn.
 Identify employees that are likely to
quit.
 Increase retention rates
 Improve customer loyalty by offering
customized incentives.

31. Retention Analysis: Case Study
 Farmer‟s insurance has been in business for over 80 years with 15 million
customers and $15 billion in revenue, and no predictive analytics.
 They found that the top 5% of customers yielded the company about
$16,000 in customer LTV, the bottom 5% yielded just $400!
 Churn analysis and LTV can tell you which customers to shed and which to
keep.

32. Questions about your
business concerns
and Answers