By the Numbers
Moving Download Customers with Analytics Figure 6
Taylor MALE
AGE
FEMALE
0
1.979%
0.421%
5
2.430%
0.800%
10
4.414%
1.719%
15
5.401%
0.285%
1.251%
0.224%
25
0.112% 0.045%
35
0.010%
0.028%
40
0.004%
Figure 7
10-19
0.532%
0.000%
20-29
1.050%
0.000%
30-39
2.816%
0.053%
40-49
8.542%
0.086%
50-59
19.474%
0.119%
60-69
27.793%
0.169%
70-79
24.220%
80-89
13.274%
0.004%
90-99
1.442%
0.000%
100-109
0.008%
Florence
Figure 11
Figure 9
Harry AGE
FEMALE
0-9
0.000%
3.786%
10-19
0.000%
5.175%
20-29
0.042%
3.700%
7.757%
30-39
0.073%
40-49
8.184%
14.399%
40-49
0.082%
0.023%
50-59
15.817%
24.448%
Figure50-59 19
0.095%
0.084%
60-69
24.157%
23.850%
60-69
0.100%
0.033%
0.118%
70-79
25.042%
11.719%
70-79
0.076%
80-89
AGE
FEMALE
0-9
1.021%
3.030%
0.000%
10-19
1.410%
0.000%
20-29
2.148%
0.077%
0.000%
30-39
40-49
0.086%
0.052%
21.219%
50-59
0.058%
14.250%
60-69
0.048%
70-79
AGE
FEMALE
0-9
0.010%
7.870%
10-19
0.016%
10.330%
20-29
0.070%
12.536%
30-39
21.233%
MALE
AGE
FEMALE
0-9
2.296%
0.010%
10-19
3.758%
0.030%
20-29
4.737%
0.032%
30-39
6.136%
0.061%
40-49
16.705%
0.062%
50-59
27.389%
0.067%
60-69
22.619%
0.048%
70-79
11.627%
5.179%
0.005%
0.357%
0.000%
Figure 8
John
Mary MALE
FEMALE
0-9
0.060%
0.038%
30
AGE
0.000%
MALE
1.055%
20
0.360%
Figure 10
Ethel
5.299%
MALE 0.000%
MALE
80-89
4.103%
80-89
0.015%
0.051%
16.467%
4.936%
80-89
0.045%
0.000%
90-99
0.303%
0.104%
90-99
0.001%
0.003%
90-99
1.716%
0.382%
90-99
0.003%
0.000%
100-109
0.001%
0.000%
100-109
0.000%
0.000%
100-109
0.007%
0.001%
100-109
0.000%
0.011%
1.565%
Figure 13
Figure 12
Figure 14
Clara
Clarence
Figure 15
Jessie
Sarah
AGE
FEMALE
FEMALE
MALE
FEMALE
MALE
AGE
FEMALE
2.453%
0-9
0.000%
0.007%
0-9
16.808%
0.026%
0-9
14.418%
4.602%
0-9
4.670%
4.280%
10-19
0.009%
0.007%
10-19
8.145%
0.041%
10-19
25.962%
7.611%
10-19
8.329%
6.822%
20-29
0.070%
0.023%
20-29
4.775%
0.127%
20-29
31.304%
9.291%
20-29
8.938%
9.567%
30-39
0.115%
0.006%
30-39
4.228%
0.048%
30-39
13.468%
6.382%
30-39
3.539%
14.977%
40-49
0.144%
0.012%
40-49
8.105%
0.016%
40-49
4.813%
6.463%
40-49
3.263%
22.466%
50-59
0.152%
0.034%
50-59
14.777%
0.011%
50-59
4.069%
6.743%
50-59
5.499%
20.507%
60-69
0.137%
0.057%
60-69
18.461%
0.010%
60-69
3.267%
5.071%
60-69
6.966%
12.556%
70-79
0.102%
0.086%
70-79
16.529%
0.009%
70-79
1.734%
2.906%
70-79
5.648%
5.207%
80-89
0.047%
0.026%
80-89
7.213%
0.002%
80-89
0.621%
1.016%
80-89
2.719%
0.383%
90-99
0.003%
0.002%
90-99
0.696%
0.000%
90-99
0.054%
0.068%
90-99
0.274%
0.001%
100-109
0.000%
0.000%
100-109
0.003%
0.000%
100-109
0.000%
0.000%
100-109
0.001%
MALE
MALE
AGE
Figures 7 through 15 show a selection of the surviving gender/age breakdowns for a selection of names with the granularity of age reduced to 10 years. Figure 10 shows that if you meet an Ethel she is most likely to be between 60 and 69 years of age. (Even though Ethel was a more popular name earlier than that, sadly there are not many Ethels still alive who were born in 1920 or before.) These histograms show the probability density that a person you meet of that name is of that age and gender. Using the data in Figure 6, we can predict that, if you meet a Taylor, there is a 44 percent chance that she is a girl between the age of 10 and 19. If you meet a Mary, the highest probability is that she is between the ages of 50 and 59; John would most likely be between 40 and 59, Florence between 70 and 79, Clarence 50 and 59, Sarah 20 and 29, and so on.
18 Casual Connect Summer 2011
AGE
Practical Demonstration: Poker Cards So how would we use this sort of analysis on a customer database? I’ll show you how I applied this technique at my company GreatPokerHands. The first step was to process the sales data to extract just the first name from the sales records. By doing this, I removed any potential privacy implications associated with the storage and use of personally identifiable information, as first name alone is not sufficient information to uniquely identify a person. We only need the first name for this analysis. In addition, using just first name makes for smaller file sizes, which can be important if the database is large! Figure 16 shows a breakdown of the most popular names of my customers (the bars show the relative volume of sales for each name). At first glance, the names appear entirely male-biased. However, we need to be careful not to draw wrong conclusions from this. This table only represents the most popular (modal) names. There is a very long