Presented by Dr. James Lani, CEO

2627 McCormick Drive, Suite 102, Clearwater, FL 33759

Using Customer Data to Build Intimacy, Engagement, and Loyalty

Using Customer Data to Build Intimacy (2-way street), Engagement (your engagement with data), and Customer Loyalty

Let’s break that down…

The presentation

•What I believe•What should be done•The result

What I Believe

RAW DATA IS A COMPLETELY UNDER-USED COMPANY ASSET

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.

What results?

USEABLE BI, CI, YOUR KNOWLEDGE OF YOUR CUSTOMER INCREASES THROUGH ENGAGEMENT WITH THE DATA, RESULTING IN GREATER LOYALITY

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.

If I’m successful in this presentation:

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.

Let ’s Talk about Data

Data is MessyUser ID Region $ Sales Start Date End Date Tokens

NumberItems Age Type T1v13 T1v14

37854 N 24840 02/10/2003 02/10/2003 144 42 5 1 5109450 S 97257 01/09/2010 01/09/2010 233 48 4 2 5111028 M 99011 02/26/2010 02/26/2010 239 22 3 2 4120757 M 110282 02/10/2011 02/10/2011 256 41 4 3 4107447 M 95106 10/21/2009 10/21/2009 243 21 5 1 5119699 NW 108841 01/13/2011 01/13/2011 260 22 4 3 4110602 SW 98553 02/08/2010 02/08/2010 267 44 5 1 5

89148 E 76110 01/21/2008 01/21/2008TFZ8-

75WHPQ 175 36 4 3 4

95408 E 82158 08/26/2008 08/26/2008TFZ8-

VYYEXV 156 32 5 3 5

114378E

102630 07/01/2010 07/02/20103GVD-

DKJS84 261 22 5 2 5109653 M 97486 01/15/2010 01/15/2010 253 29 5 3 5

60462E

48832 06/11/2005 09/11/2005 147 19 5 2 553701 M 42205 09/13/2004 09/16/2004 143 21 4 3 5

107652 Mid 95334 10/27/2009 10/27/2009TFZ8-

FP3BUP 228 19 5 3 5115355 S 103666 08/03/2010 08/03/2010 262 30 4 4 5119629 S 108716 01/11/2011 01/11/2011 266 19 4 4 4

Data CAN be managed, then used fo r v i sua l i za t ion ,

segmenta t ion , scor ing , and churn ana lys is…which w i l l ge t you to

loya l i t y.

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%.

Talk about Data Visual izat ion

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

What scares us?

Whose twittering?

Napoleon’s Army by the Month

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.

Data Visualization: Benefits

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.

Bank Auction priced/Assessed value

Wachovia Bank 45.73Bank of New York 42.60Deutsche Bank 40.36BAC Home Loans 37.68US Bank 37.68Bank of America 36.92Wells Fargo Bank 34.19

Table 1. Sold Price/Assessed Value Percentage by Bank

Data Visualization: Case Study

Market Segmentation: Better understand who you’re selling to and what message appeals to them

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.

Market Segmentation: Benefits

Customer

Pro

fitab

ility

Differentiated needs/buying process

Homogeneous needs/buying process

High

Low

Segmentation has the advantage of differentiating customers by profitability and needs/buying process

Market Segmentation: Customers have differentiated needs/buying process

Price conscious Brand loyalists Internet buyers0

10

20

30

40

50

60

70

Type of Customer

Perc

ent o

f Cus

tom

ers

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.”

Market Segmentation: Example

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 5Q1: How is your practice staffed (Credentials)?

Optometrist:80% Ophthalmologist:40%

Optometrist:79% Optometrist:79% Optometrist: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

building:94%Free standing building:64%

Free standing building:64%

Strip mall:44% Strip mall:84%

Q4: In-house lab No in-house services:100%

In-house lab on-site:87%

In-house lab on-site:93%

No in-house services:99%

In-house lab on-site:95%

Q5: In-house Lens Jobs per week 10.0 172.7 57.7 0.0 65.4Q6: In-house percentage of total unit volume 30.2 54.5 42.5 0.0 43.9Q7: Square feet 485.6 1249.8 810.2 630.4 768.6Q8: Optical frames 813.9 870.8 913.1 615.8 955.6Q9: Sunglasses 74.2 124.2 139.0 96.7 149.9Q10: Children optical frames 65.1 158.7 79.2 83.4 127.0Q11: Percentage of frame sales: Under $100 15.8 20.7 17.5 15.0 16.3Q12: Percentage of frame sales: $100 - $200 52.3 40.1 45.1 48.5 32.5Q13: Percentage of frame sales: $201 - $300 23.4 24.9 26.2 26.9 39.6

Market Segmentation: Case Study

Lead Quality Scoring: Who has the propensity to

buy?

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

Lead Quality Scoring: Getting to the Right Customers

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+) Marital status (single, married,…) Gender (Male, Female, Unknown) Religion (catholic, Hindu, Jewish…) Credit card type (Bank, retail, oil…) Net worth Rank (Top, 2nd, 3rd, …15th) Home value ($1-$50k, $51k-$100k…) Mortgage loan type (Cash, FHA, VA…) Occupation (Business owner, Prof, health services,

teacher, military)

Political affiliation (Democratic, Republican, Independent…) Vehicle manufacturer (Acura, Audi, Buick, …Volvo) Neilson Region (East, metro chicago, West, …) Mean years of schooling (HS, Some college, Graduate) Languages spoken (English, Russian, Spanish…) Heavy internet user (1=most likely, 10=least likely) Boat population type (Inboard, Outboard, Other)

Lead Quality Scoring: Case Study

Lead Quality Scoring: Getting to the right prospects

% of leads purchasing product or

service

% of leads contacted

Response Rate with Lead Scoring

Response Rate without Lead Scoring

New leads scored for quality.

Scoring algorithm to use for new leads as they come in.

Lead Quality Scoring: the results

Series10

0.51

1.52

2.53

3.54

PlatinumGoldSilverBronze

Retention / LTV Analysis

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.

Retention Analysis: Benefits

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.

Retention Analysis: Case Study

Questions about your business concerns

and Answers