week3_slides_060721.pptx

Data Samples & Data Analyses

NYU | SCPS
Database Management & Modeling
Edward Colet
[email protected]
Asynchronous Session 3, week of June 7 2021

Class material and homework so far
You should be through text chapters 1-3 (introduction), 4-5 (database fundamentals), and the supplemental readings on RDMBS’s and BigData;
HW submissions were a short write-up about yourselves (hw1), a relational database design exercise, and a BigData discussion (hw2)

Questions from the material?
Please feel free to also use the discussion section on our NYU Discussion site to ask, answer, comment on material from this week (for this week, this will be part of hw3)

Content for this week
Chapter 6: The Analysis Sample
Chapter 7: Analyzing and Manipulating Customer Data
Online Khan Academy content to Introduce SQL

Week3 Overview

‹#›

‹#›

Key themes for this week (and the course)
Databases are important for storing data (obviously), but you have to analyze the data as well otherwise you just have a “data tomb”. The analysis of data to gain insights is what gives the data it’s power and makes it really valuable.

This week we’ll learn about some fundamental analytic concepts operations associated with analysis; We’ll review Correlation, a foundational basis for analytics and modeling; We’ll learn some of the fundamental operations to slice and dice data, and we’ll write basic SQL (Structured Query Language) code to create a table, populate it with records, and query the table to extract and summarize information.

Week3 Overview

‹#›

‹#›

The Analysis Sample
Chapter 6

Key Point of the Chapter:
Data analyses are usually (almost always) done on subsets of the data in the database. As such, the following are the key concepts and points to understand about working with subsets of data
Representative samples
Random samples
Frozen files
Test and validation data sets

Chapter 6: The Analysis Sample

‹#›

‹#›

Know some common marketing scenarios that would be suitable to use a sample and test…

To gauge new product offering/response
Price elasticity
Impact of a creative / change
Identify target market for new test
Gain insights on specific groups/segments

. . . Any decision about your product in the market can be tested and analyzed to minimize/gauge risk

Chapter 6: The Analysis Sample

‹#›

‹#›

What is a representative sample?
A sample accurately reflecting the population of interest from which the marketer wants to draw inferences.
Can not extend or apply results from one population to another
Can not purposely exclude names except for “permission opt-outs”, or other recently promoted per rules/regulations

What is a random sample?
When every member equally likely to be chosen
Nth selects is one approach (select every nth name)

Chapter 6: The Analysis Sample

‹#›

‹#›

What is a frozen file?
A file containing a snapshot view of the customer(s) at the time of the promotion, updated with response data to the promotion

Why is a frozen file important?
Analysis of non-frozen data of customers that have responded may lead to misleading conclusions.
Because the data (and drivers) associated with that customers response may be different now than what they were during or prior to the promotion

What if we can’t manage (store) frozen files for every promotion?
Alternative to using a frozen file for each test is to pull analyses datasets from database backups (prior to test).

Chapter 6: The Analysis Sample

‹#›

‹#›

What are test (analysis) and validation samples?
Analyses are based on the test file, and calibrated to see if they hold up on the validation file
2/3rds for Test, 1/3rd for validation is a common rule of thumb for splitting the file
The reason for doing this is because a sample may have a certain level of error associated with them.
The validation sample is used to ensure the analyst does not make erroneous conclusions based on the error variance associated with the sample.
If the conclusions hold up on the validation sample, then there’s more confidence that the conclusions from the analyses are sound, and will generalize in-market.

We can put all of this together in an “analytic workflow” . . .

Chapter 6: The Analysis Sample

‹#›

‹#›

Define test segment

Names pulled and saved from the database
Sample split 2/3 for analysis

Application of analysis findings to the database for roll-out

Customer Database
Validate findings and refine results

Responses matched to create the frozen analysis file

Names sent test promotion
Analysis of responders vs.
non-responders
Sample split 1/3 for validation

“The Frozen Analysis File”

Chapter 6: The Analysis Process

‹#›
‹#›

Analyzing and Manipulating Customer Data
Chapter 7

Key Point

It is critically important to be able to explore and analyze the data – how info is captured, updated, maintained so that what we get out of the data accurately reflects the state and processes of the business

Chapter 7:Analyzing & Managing Customer Data

‹#›

‹#›

What is a univariate tabulation?
Generating a table to summarize the categories of values from a single variable or field.
Age (example below)
Effect is expressed as an Index to some standard of comparison
Or other variables of interest
Past promotion responses,
Total number of s ever,
Total number of promos. . .

Chapter 7: Analysis

‹#›

‹#›

Statistical Correlation
Section

What are some of the reasons a univariate analyses may not be the full answer?
Judgments and decisions are made about how to cut / slice the data for a univariate analyses
Deciding on the population of interest,
How to categorize values in buckets,
Determining a threshold/size criteria,
Etc.
…. What if the judgments or decisions made about the above are not the best?
… And also because it’s univariate, i.e. there may be much more information available that could be used

Chapter 7: Analysis

‹#›

‹#›

What is a cross-tabulation analyses?
A way to view two or more data elements in combination – e.g. total promos x total s

Chapter 7: Analysis

‹#›

‹#›

How would a decision be different with a cross-tabulation vs. a univariate analyses?
(look at the univariate distribution of each dimension below, >= 3%)

Chapter 7: Analysis

‹#›

‹#›

How would a decision be different with a cross-tabulation vs. a univariate analyses?
(you could promote to 28.27% of customers rather than 14.09%, i.e. 2x the volume, where >= 3%)

Chapter 7: Analysis

‹#›

‹#›

What are logic-counter variables?
A logical combination of several data elements that represent or share a common underlying dimension
Each distinct variable can be combined to form a new single variable
Oftentimes, you can get more lift out of this new variable, than you could with each single component  more predictive power when put into models, and/or more stable in coefficients in a regression equation
Also reduces the amount of data to analyze (e.g. variable reduction)

Notes and considerations:
It is assumed that you know how individual data elements are related to each other, and/or how these individual elements map onto the underlying dimension
It may or may not be the case that each component variable is weighted equally

Chapter 7: Analysis

‹#›

‹#›

Note how the following example provides better information on who to promote, once logic-counter variables were created:

21% of database names can now be promoted (up from 8.7%)
Source: Optimal Database Marketing, Exhibit 7.7 and 7.8
Customer
Name
Customer
Address
Total $
Paid

RRP
TSRR
ERL
ELR

Rock Music

Counter

S. Jones
123 Main
$356.34

PD
PNO
PNO
PD

2

B. Smith
8
th
Ave.
$643.22

PNO
PD
PD
PD

3

K. Brown
45 Oak St.
$264.98

NP
NP
PNO
PD

1

LEGEND: RRP=Rock and Roll Party, TSRR=The Soul of Rock and Roll, ERL=Early Rock Legends, ELR=Easy Listening
Rock, PD=Paid, PNO=Promoted but not ed, NP=Not promoted.
Chapter 7: Analysis

‹#›

‹#›

How are ratio variables created and used?
Create a new variable, in which one data element is divided by another
How are ratio variables related to cross-tabs?
The component data elements need to be continuous measures, while cross-tabs can work for categorical variables.
Note how the use of a ratio variable can provide more information than a univariate consideration:

Chapter 7: Analysis

‹#›

‹#›

What are longitudinal variables?
Conceptually similar to a time-series, longitudinal variables allow marketers to see customer responses over time
When are longitudinal variables most useful?
Predicated on the assumption that “Recency” of a customer’s transaction matters “most”
When coded well, they are useful for seeing which customers are becoming “better” or “worse” over time
Example

Chapter 7: Analysis

‹#›

‹#›

Is there a relationship between two variables, e.g. Age and Income?

Can we measure or quantify this relationship? How “much” of a relationship is there?

Correlation (re-visited)

‹#›

‹#›

24

Age
Distribution
Income
Distribution
Perfect Correlation (Positive)
When z-scores exactly match
When scores exactly match

25

Age
Distribution
Income
Distribution
Perfect Correlation (Negative)
When z-scores exactly match, but their signs are reversed

How can we quantify (i.e. measure) an association between 2 variables?
The “intuition”
If exact same score on x, and y, then perfect correlation.
But must the scores on x and y be the same value? (e.g. Age and Income can be perfectly correlated but not match).
Perfect correlation = exact same place on distribution
But can we measure where you are in a distribution, regardless of scale or units?
Yes, with z-scores
A perfect correlation is when z-scores match exactly
A perfect negative correlation is when the size of z-scores match, but not the sign
No correlation, or a zero correlation, means the association is random – i.e. where a value is on one distribution is very unlike it’s corresponding position on the other distribution.
Neat fact:
The sum of the cross products of pairs of numbers are highest when matched;
An average of cross products, is a definition of correlation

Correlation (re-visited)

‹#›

‹#›

Linking the intuitive understanding of correlation to the math . . .
Think of the correlation formula as:

Intuitive translation: Correlation is a measure of the association between X and Y, calculated by multiplying the position of X and the position of Y from their respective distributions and then taking the “average” of these cross products.

Reminder: The Z’s are the conversion of raw data into a standardized score:
Intuitive translation: Take each value, subtract it from the average of it’s set of values, then divide by the standard deviation of those values. Do this for all X’s, and do this for all Y’s.
By converting to z-scores, the underlying scale of raw values (e.g. age in years, income in $, etc.) no longer matter. Any variables can now be compared in terms of “standard deviations away from the mean”, i.e. z-scores.
Neat Fact: The average of these “new” z-scores is equal to 0 (always). And the standard deviation of z-scores is equal to 1 (always).
Footnote: the “mu” and the “sigma” in the z-score formula are the population mean, and the population standard deviation, rather than the sample mean and sample standard deviation. (Refer to your past Stats text on this difference if nec.). The sample standard deviation involves a division by (n-1), and the population standard deviation divides by (n). When dealing with large numbers of records, this difference becomes immaterial.

Correlation (re-visited)

‹#›

‹#›

Database Language: SQL
Analysis: Statistical Correlation
HW 3

Data Analysis (using Microsoft Excel)

Using Microsoft Excel, enter the Age and Income data (textbook exhibit 7.17) into a spreadsheet.
Using the “intuition” (see slides 25-26) of what a statistical correlation is, calculate the correlation between Age and Income. Insert columns to show intermediate steps in calculating z-scores.
Verify that this is equivalent to the correlation output in Excel’s Data Analysis routines
Excel has the following function to correlate values in cells A1:A10 with B1:B10
= CORREL(A1:A10,B1:B10)
If correct, then your calculated answer should match Excel’s function . . . and you should enjoy the “AHA!” magical moment of insight experience!
Upload your file to NYU Brightspace

Homework assignment #3.1 (Correlation)

‹#›

‹#›

Introduction to SQL (Structured Query Language)

SQL is the programming language commonly used when working with Relational Databases.   
For this part of the HW assignment we’ll use an online lesson and exercise from Khan Academy to introduce SQL coding. Starting with the following link, work through the series of short online modules.  The modules are based on the “Design a Store Database” Project in where you learn to how to create, populate and query a database.   
For this part of the HW, your database will be an implementation of what you designed last week for HW2 – your database of student information and job opportunities.
The HW submission will be an uploaded screen capture/slide showing your code, the database schema, and the query results.

https
://www.khanacademy.org/computing/computer-programming/sql/sql-basics/v/welcome-to-sql 

Homework assignment #3.2 (SQL)

‹#›

‹#›

Head of Household Age
Number
% of
Sample
Number of
Orders
Response
Rate
Index to
Total
30 and under
1,529
15.29%
67
4.38%
175
31-40
1,775
17.75%
63
3.55%
142
41-50
1,879
18.79%
46
2.45%
98
51-60
2,054
20.54%
29
1.41%
56
61 and over
1,785
17.85%
18
1.01%
40
No age info available
978
9.78%
27
2.76%
110
Total
10,000
100.00%
250
2.50%
100
Sheet: Sheet1
Sheet: Sheet2
Sheet: Sheet3
Sheet: Sheet4
Sheet: Sheet5
Sheet: Sheet6
Sheet: Sheet7
Sheet: Sheet8
Sheet: Sheet9
Sheet: Sheet10
Sheet: Sheet11
Sheet: Sheet12
Sheet: Sheet13
Sheet: Sheet14
Sheet: Sheet15
Sheet: Sheet16
Tabulations for the title Rock Music of the 80’s
Head of Household Age
Number
% of Sample
Number of Orders
Response Rate
Index to Total
30 and under
1529.0
0.1529
67.0
0.04381948986265533
175.27795945062132
31-40
1775.0
0.1775
63.0
0.03549295774647887
141.9718309859155
41-50
1879.0
0.1879
46.0
0.024481106971793506
97.92442788717402
51-60
2054.0
0.2054
29.0
0.014118792599805257
56.47517039922103
61 and over
1785.0
0.1785
18.0
0.010084033613445379
40.33613445378151
No age info available
978.0
0.0978
27.0
0.027607361963190184
110.42944785276072
Total
10000.0
1.0
250.0
0.025
100.0
Rock N Roll Party (RRP)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
Promoted & Ordered
877.0
0.0877
51.0
0.05815279361459521
232.61117445838084
Promoted & Not Ordered
3967.0
0.3967
93.0
0.02344340811696496
93.77363246785984
Not Promoted
3911.0
0.3911
73.0
0.01866530299156226
74.66121196624904
Not Available
1245.0
0.1245
33.0
0.02650602409638554
106.02409638554215
Total
10000.0
1.0
250.0
0.025
100.0
Total Number of Orders Ever (all Prod. lines)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
0
0.0
0.0
0.0
0.0
0.0
1-5
3312.0
0.3312
62.0
0.018719806763285024
74.8792270531401
6-10
3074.0
0.3074
68.0
0.022121014964216004
88.48405985686401
11-15
2205.0
0.2205
64.0
0.029024943310657598
116.09977324263039
15 plus
1409.0
0.1409
56.0
0.0397444996451384
158.97799858055356
Total
10000.0
1.0
100.0
Total Number Promotions Ever (all Prod. lines)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
1-5
0.0
0.0
0.0
0.0
0.0
6-10
768.0
0.0768
16.0
0.020833333333333332
83.33333333333333
11-20
2544.0
0.2544
57.0
0.02240566037735849
89.62264150943395
21-30
3563.0
0.3563
108.0
0.03031153522312658
121.24614089250632
31 plus
3125.0
0.3125
69.0
0.02208
88.32
Total
10000.0
1.0
250.0
0.025
100.0
Total Promotions Ever:
Total Orders Ever:
1-5
6-10
11-20
21-30
31 plus
Total
0
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
1-5
0.00% (0/0)
1.63% (8/491)
1.76% (17/967)
2.34% (20/856)
1.60% (16/998)
1.87% (62/3,312)
6-10
0.00% (0/0)
2.89% (8/277)
1.85% (14/756)
2.51% (29/1,154)
1.80% (16/887)
2.21% (68/3,074)
11-15
0.00% (0/0)
0.00% (0/0)
3.03% (14/462)
3.03% (29/956)
2.67% (21/787)
2.90% (64/2,205)
15 plus
0.00% (0/0)
0.00% (0/0)
3.34% (12/359)
5.03% (30/597)
3.53% (16/453)
3.97% (56/1,409)
Total
0.00% (0/0)
2.08% (16/768)
2.24% (57/2,544)
3.03% (108/3,563)
2.21% (69/3,125)
2.5% (250/10,000)

Rock Logic: (RRP, TSRR, ERL, ELR)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
Purchased 0
7856.0
0.7856
117.5386
0.014961634419551935
60.0
Purchased 1
945.0
0.0945
47.3445
0.0501
200.0
Purchased 2
633.0
0.0633
40.1322
0.0634
254.0
Purchased 3
365.0
0.0365
27.156
0.0744
298.0
Purchased 4
201.0
0.0201
17.8287
0.0887
355.0
Total
Customer Actions to Last Three Promotions Sent (Prom-2, Prom-1, Prom)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
(Pay, Pay, Pay)
356.0
0.0356
19.0
0.05337078651685393
213.48314606741573
(NR, Pay, Pay)
422.0
0.0422
16.0
0.037914691943127965
151.65876777251185
(NR, NR, Pay)
528.0
0.0528
18.0
0.03409090909090909
136.36363636363635
(Cancel, Pay, Pay)
427.0
0.0427
20.0
0.0468384074941452
187.3536299765808
(Cancel, Cancel, Pay)
229.0
0.0229
9.0
0.039301310043668124
157.20524017467247
……
……
……
……
……
……
(NR, NR, BD)
150.0
0.015
2.0
0.013333333333333334
53.333333333333336
(NR, NR, NR)
298.0
0.0298
2.0
0.006711409395973154
26.845637583892618
Total
100.0
NR = Non-Response, BD = Bad Debt
1-5
6-10
11-20
21-30
31+
TOT
0
1-5
0.02240325865580448
0.02171664943123061
0.02102803738317757
0.018036072144288578
0.020531400966183576
68.0
3312.0
0.02010516548097742
6-10
0.02527075812274368
0.023809523809523808
0.021663778162911613
0.020293122886133032
0.022121014964216004
68.0
3074.0
0.025444596443228454
11-15
0.032467532467532464
0.028242677824267783
0.02795425667090216
0.029024943310657598
64.0
2205.0
0.028616852146263912
15 plus
0.03899721448467967
0.035175879396984924
0.033112582781456956
0.035486160397444996
50.0
1409.0
0.031020408163265307
TOT
0.0234375
0.025550314465408806
0.024417625596407522
0.0256
0.025
250.0
10000.0
0.025
250.0
10000.0
0.0
0.0
0.0
0.0234375
18.0
768.0
0.026729559748427674
68.0
2544.0
0.025540275049115914
91.0
3563.0
0.02336
73.0
3125.0
0.025
250.0
10000.0
Total Promotions Ever:
Total Orders Ever:
1-5
6-10
11-20
21-30
31 plus
Total
0
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
1-5
0.00%
(0/0)
1.63%
(8/491)
1.76%
(17/967)
2.34%
(20/856)
1.60%
(16/998)
1.87%
(62/3,312)
6-10
0.00%
(0/0)
2.89%
(8/277)
1.85%
(14/756)
2.51%
(29/1,154)
1.80%
(16/887)
2.21%
(68/3,074)
11-15
0.00%
(0/0)
0.00%
(0/0)
3.03%
(14/462)
3.03%
(29/956)
2.67%
(21/787)
2.90%
(64/2,205)
15 plus
0.00%
(0/0)
0.00%
(0/0)
3.34%
(12/359)
5.03%
(30/597)
3.53%
(16/453)
3.97%
(56/1,409)
Total
0.00%
(0/0)
2.08%
(16/768)
2.24%
(57/2,544)
3.03%
(108/3,563)
2.21%
(69/3,125)
2.5%
(250/10,000)
Sheet: Sheet1
Sheet: Sheet2
Sheet: Sheet3
Sheet: Sheet4
Sheet: Sheet5
Sheet: Sheet6
Sheet: Sheet7
Sheet: Sheet8
Sheet: Sheet9
Sheet: Sheet10
Sheet: Sheet11
Sheet: Sheet12
Sheet: Sheet13
Sheet: Sheet14
Sheet: Sheet15
Sheet: Sheet16
Tabulations for the title Rock Music of the 80’s
Head of Household Age
Number
% of Sample
Number of Orders
Response Rate
Index to Total
30 and under
1529.0
0.1529
67.0
0.04381948986265533
1.7527795945062132
31-40
1775.0
0.1775
63.0
0.03549295774647887
1.4197183098591548
41-50
1879.0
0.1879
46.0
0.024481106971793506
0.9792442788717401
51-60
2054.0
0.2054
29.0
0.014118792599805257
0.5647517039922103
61 and over
1785.0
0.1785
18.0
0.010084033613445379
0.40336134453781514
No age info available
978.0
0.0978
27.0
0.027607361963190184
1.1042944785276072
Total
10000.0
1.0
250.0
0.025
1.0
Rock N Roll Party (RRP)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
Promoted & Ordered
877.0
0.0877
51.0
0.05815279361459521
2.3261117445838084
Promoted & Not Ordered
3967.0
0.3967
93.0
0.02344340811696496
0.9377363246785984
Not Promoted
3911.0
0.3911
73.0
0.01866530299156226
0.7466121196624904
Not Available
1245.0
0.1245
33.0
0.02650602409638554
1.0602409638554215
Total
10000.0
1.0
250.0
0.025
1.0
Total Number of Orders Ever (all Prod. lines)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
0
0.0
0.0
0.0
0.0
0.0
1-5
3312.0
0.3312
62.0
0.018719806763285024
0.748792270531401
6-10
3074.0
0.3074
68.0
0.022121014964216004
0.8848405985686401
11-15
2205.0
0.2205
64.0
0.029024943310657598
1.1609977324263039
15 plus
1409.0
0.1409
56.0
0.0397444996451384
1.5897799858055357
Total
10000.0
1.0
1.0
Total Number Promotions Ever (all Prod. lines)
Number
% of Sample
Number of Orders
Response Rate
Index to Total
1-5
0.0
0.0
0.0
0.0
0.0
6-10
768.0
0.0768
16.0
0.020833333333333332
0.8333333333333333
11-20
2544.0
0.2544
57.0
0.02240566037735849
0.8962264150943395
21-30
3563.0
0.3563
108.0
0.03031153522312658
1.2124614089250632
31 plus
3125.0
0.3125
69.0
0.02208
0.8831999999999999
Total
10000.0
1.0
250.0
0.025
1.0
Total Promotions Ever:
Total Orders Ever:
1-5
6-10
11-20
21-30
31 plus
Total
0
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
0.00% (0/0)
1-5
0.00% (0/0)
1.63% (8/491)
1.76% (17/967)
2.34% (20/856)
1.60% (16/998)
1.87% (62/3,312)
6-10
0.00% (0/0)
2.89% (8/277)
1.85% (14/756)
2.51% (29/1,154)
1.80% (16/887)
2.21% (68/3,074)
11-15
0.00% (0/0)
0.00% (0/0)
3.03% (14/462)
3.03% (29/956)
2.67% (21/787)
2.90% (64/2,205)
15 plus
0.00% (0/0)
0.00% (0/0)
3.34% (12/359)
5.03% (30/597)
3.53% (16/453)
3.97% (56/1,409)
Total
0.00% (0/0)
2.08% (16/768)
2.24% (57/2,544)
3.03% (108/3,563)
2.21% (69/3,125)
2.5% (250/10,000)

Rock Logic: (RRP, TSRR, ERL, ELR)
Number
% of Sample
Number of Orders
Response Rate
Index to W/L
Purchased 0
7856.0
0.7856
117.5386
0.014961634419551935
0.5984653767820773
Purchased 1
945.0
0.0945
47.3445
0.0501
2.004
Purchased 2
633.0
0.0633
40.1322
0.0634
2.5359999999999996
Purchased 3
365.0
0.0365
27.156
0.0744
2.9759999999999995
Purchased 4
201.0
0.0201
17.8287
0.0887
3.548
Total
Customer Actions to Last Three Promotions Sent (Prom-2, Prom-1, Prom)
Number
% of Sample
Number of Orders
Response Rate
Index to W/L
(Pay, Pay, Pay)
356.0
0.0356
19.0
0.05337078651685393
2.134831460674157
(NR, Pay, Pay)
422.0
0.0422
16.0
0.037914691943127965
1.5165876777251186
(NR, NR, Pay)
528.0
0.0528
18.0
0.03409090909090909
1.3636363636363635
(Cancel, Pay, Pay)
427.0
0.0427
20.0
0.0468384074941452
1.873536299765808
(Cancel, Cancel, Pay)
229.0
0.0229
9.0
0.039301310043668124
1.5720524017467248
……
……
……
……
……
……
(NR, NR, BD)
150.0
0.015
2.0
0.013333333333333334
0.5333333333333333
(NR, NR, NR)
298.0
0.0298
2.0
0.006711409395973154
0.2684563758389262
Total
NR = Non-Response, BD = Bad Debt
1-5
6-10
11-20
21-30
31+
TOT
0
1-5
0.02240325865580448
0.02171664943123061
0.02102803738317757
0.018036072144288578
0.020531400966183576
68.0
3312.0
0.02010516548097742
6-10
0.02527075812274368
0.023809523809523808
0.021663778162911613
0.020293122886133032
0.022121014964216004
68.0
3074.0
0.025444596443228454
11-15
0.032467532467532464
0.028242677824267783
0.02795425667090216
0.029024943310657598
64.0
2205.0
0.028616852146263912
15 plus
0.03899721448467967
0.035175879396984924
0.033112582781456956
0.035486160397444996
50.0
1409.0
0.031020408163265307
TOT
0.0234375
0.025550314465408806
0.024417625596407522
0.0256
0.025
250.0
10000.0
0.025
250.0
10000.0
0.0
0.0
0.0
0.0234375
18.0
768.0
0.026729559748427674
68.0
2544.0
0.025540275049115914
91.0
3563.0
0.02336
73.0
3125.0
0.025
250.0
10000.0
Total Promotions Ever:
Total Orders Ever:1-56-1011-2021-3031 plusTotal
0
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
0.00%
(0/0)
1-5
0.00%
(0/0)
1.63%
(8/491)
1.76%
(17/967)
2.34%
(20/856)
1.60%
(16/998)
1.87%
(62/3,312)
6-10
0.00%
(0/0)
2.89%
(8/277)
1.85%
(14/756)
2.51%
(29/1,154)
1.80%
(16/887)
2.21%
(68/3,074)
11-15
0.00%
(0/0)
0.00%
(0/0)
3.03%
(14/462)
3.03%
(29/956)
2.67%
(21/787)
2.90%
(64/2,205)
15 plus
0.00%
(0/0)
0.00%
(0/0)
3.34%
(12/359)
5.03%
(30/597)
3.53%
(16/453)
3.97%
(56/1,409)
Total
0.00%
(0/0)
2.08%
(16/768)
2.24%
(57/2,544)
3.03%
(108/3,563)
2.21%
(69/3,125)
2.5%
(250/10,000)

Sheet1

Tabulations for the title Rock Music of the 80’s

Head of Household Age Number % of Sample Number of Orders Response Rate Index to Total

30 and under 1,529 15.29% 67 4.38% 1.75

31-40 1,775 17.75% 63 3.55% 1.42

41-50 1,879 18.79% 46 2.45% 0.98

51-60 2,054 20.54% 29 1.41% 0.56

61 and over 1,785 17.85% 18 1.01% 0.40

No age info available 978 9.78% 27 2.76% 1.10

Total 10,000 100.00% 250 2.50% 1.00

Rock N Roll Party (RRP) Number % of Sample Number of Orders Response Rate Index to Total

Promoted & Ordered 877 8.77% 51 5.82% 2.33

Promoted & Not Ordered 3,967 39.67% 93 2.34% 0.94

Not Promoted 3,911 39.11% 73 1.87% 0.75

Not Available 1,245 12.45% 33 2.65% 1.06

Total 10,000 100.00% 250 2.50% 1.00

Total Number of Orders Ever (all Prod. lines) Number % of Sample Number of Orders Response Rate Index to Total

0 0 0.00% 0 0.00% 0.00

1-5 3,312 33.12% 62 1.87% 0.75

6-10 3,074 30.74% 68 2.21% 0.88

11-15 2,205 22.05% 64 2.90% 1.16

15 plus 1,409 14.09% 56 3.97% 1.59

Total 10,000 100.00% 250 2.50% 1.00

Total Number Promotions Ever (all Prod. lines) Number % of Sample Number of Orders Response Rate Index to Total

1-5 0 0.00% 0 0.00% 0.00

6-10 768 7.68% 16 2.08% 0.83

11-20 2,544 25.44% 57 2.24% 0.90

21-30 3,563 35.63% 108 3.03% 1.21

31 plus 3,125 31.25% 69 2.21% 0.88

Total 10,000 100.00% 250 2.50% 1.00

Total Promotions Ever:

Total Orders Ever: 1-5 6-10 11-20 21-30 31 plus Total

0 0.00% (0/0) 0.00% (0/0) 0.00% (0/0) 0.00% (0/0) 0.00% (0/0) 0.00% (0/0)

1-5 0.00% (0/0) 1.63% (8/491) 1.76% (17/967) 2.34% (20/856) 1.60% (16/998) 1.87% (62/3,312)

6-10 0.00% (0/0) 2.89% (8/277) 1.85% (14/756) 2.51% (29/1,154) 1.80% (16/887) 2.21% (68/3,074)

11-15 0.00% (0/0) 0.00% (0/0) 3.03% (14/462) 3.03% (29/956) 2.67% (21/787) 2.90% (64/2,205)

15 plus 0.00% (0/0) 0.00% (0/0) 3.34% (12/359) 5.03% (30/597) 3.53% (16/453) 3.97% (56/1,409)

Total 0.00% (0/0) 2.08% (16/768) 2.24% (57/2,544) 3.03% (108/3,563) 2.21% (69/3,125) 2.5% (250/10,000)

Rock Logic: (RRP, TSRR, ERL, ELR) Number % of Sample Number of Orders Response Rate Index to W/L

Purchased 0 7,856 78.56% 118 1.50% 0.60

Purchased 1 945 9.45% 47 5.01% 2.00

Purchased 2 633 6.33% 40 6.34% 2.54

Purchased 3 365 3.65% 27 7.44% 2.98

Purchased 4 201 2.01% 18 8.87% 3.55

Total 10,000 100.00% 250 2.50% 1.00

Customer Actions to Last Three Promotions Sent (Prom-2, Prom-1, Prom) Number % of Sample Number of Orders Response Rate Index to W/L

(Pay, Pay, Pay) 356 3.56% 19 5.34% 2.13

(NR, Pay, Pay) 422 4.22% 16 3.79% 1.52

(NR, NR, Pay) 528 5.28% 18 3.41% 1.36

(Cancel, Pay, Pay) 427 4.27% 20 4.68% 1.87

(Cancel, Cancel, Pay) 229 2.29% 9 3.93% 1.57

…… …… …… …

Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more

Order your essay today and save 30% with the discount code HAPPY