rd
Performance Lawn Equipment (PLE) recently received data from a 3 party survey of purchasing managers, their customers, related to predicting the level of business and service. We will use Hierarchical Clustering a data mining technique to reduce the data into groups and analyze the ratings for the distinct groups. We will also use the data to determine and improve satisfaction drivers and usage level. The survey performed has seven questions to evaluate the level of service from PLE to the purchasing managers. Each manager was asked to rate PLE on a scale from 0 to 10 (poor to excellent) graphically to evaluate: delivery speed, price level, price flexibility, manufacturing image, overall service, sales force image, and product quality. With Hierarchical Clustering we will group purchasing managers into groups with similar responses. responses. Our goal is to be able to e valuate segments of our customer base to improve our customer relationships. We will start with the data responses and using Hierarchical Clustering with average group linkage (using the average of each group from all data points) to produce a dendrogram so we c an properly evaluate the most relevant number of group divisions. The dendrogram is a backwards (top to bottom) division division of the data from one large group to each individual observations showing the division division of data into similar groups. For our analysis we will determine a number of groups by drawing a horizontal line through the response level of 2.75, which divides the data into 4 groups (see be low):
Dendrogram(Average Dendrogram(Average group linkage) 4
3.5
3
2.5 e c n a t s i D
2
1.5
1
0.5
0
1 11 9 28 12 14 5
Group 1 (which consists of)
7 13 18 15 23 17 24 21 2 19 6 25 8 16 4 20 26 22 29 3 10 27 30
Row Id.
Sub Cluster Id
Cluster Id
Delivery speed
Price level
Price flexibility
Manufacturing image
Overall service
Salesforce image
Product quality
4.1
0.6
6.9
4.7
2.4
2.3
5.2
1
1
1
5
1
5
6
0.9
9.6
7.8
3.4
4.6
4.5
7
1
7
4.6
2.4
9.5
6.6
3.5
4.5
7.6
9
1
9
5.5
1.6
9.4
4.7
3.5
3
7.6
11
1
11
2.4
1.6
8.8
4.8
2
2.8
5.8
12
1
12
3.9
2.2
9.1
4.6
3
2.5
8.3
14
1
7
3.7
1.5
8.6
5.7
2.7
3.7
6.7
15
1
14
4.7
1.3
9.9
6.7
3
2.6
6.8
19
1
7
5.3
1.4
9.7
6.1
3.3
3.9
6.8
20
1
14
4.7
1.3
9.9
6.7
3
2.6
6.8
28
1
7
5.2
1.3
9.7
6.1
3.2
3.9
6.7
38
1
12
4
0.9
9.1
5.4
2.4
2.6
7.3
42
1
5
5.9
0.9
9.6
7.8
3.4
4.6
4.5
49
1
9
5.8
0.2
8.8
4.5
3
2.4
6.7
51
1
1
3.7
0.7
8.2
6
2.1
2.5
5.2
58
1
28
5.4
2.5
9.6
5.5
4
3
7.7
63
1
12
4.1
1.1
9.3
5.5
2.5
2.7
7.4
66
1
12
3.7
1.4
9
4.5
2.6
2.3
6.8
67
1
7
4.2
2.5
9.2
6.2
3.3
3.9
7.3
74
1
9
5.2
1.3
9.1
4.5
3.3
2.7
7.3
76
1
12
4.2
2.4
9.4
4.9
3.2
2.7
8.5
77
1
1
3.8
0.8
8.3
6.1
2.2
2.6
5.3
85
1
11
2.6
3
8.5
6
2.8
2.8
6.8
87
1
11
2.4
2.9
8.4
5.9
2.7
2.7
6.7
90
1
7
4.3
2.5
9.3
6.3
3.4
4
7.4
95
1
1
4
0.5
6.7
4.5
2.2
2.1
5
97
1
9
6.1
0.5
9.2
4.8
3.3
2.8
7.1
100
1
11
2.5
1.8
9
5
2.2
3
6
(Row Id.) – Customer identifier has varying opinions about PLE, but more on the positive side. The delivery speed and price flexibility are ranked high compared to other customers. They believe manufacturing and salesforce image is good. They are not as satisfied as other customers with overall service and quality. They also feel the price is the biggest obstacle in the business relationship. Group 2 (which consists of) Row Id.
Sub Cluster Id
Cluster Id
Delivery speed
Price level
Price flexibility
Manufacturing image
Overall service
Salesforce image
Product quality
3
6.3
6.6
2.5
4
8.4
2
2
2
1.8
4
2
4
2.7
1
7.1
5.9
1.8
2.3
7.8
6
2
6
1.9
3.3
7.9
4.8
2.6
1.9
9.7
8
2
8
1.3
4.2
6.2
5.1
2.8
2.2
6.9
17
2
16
3.2
4.1
5.7
5.1
3.6
2.9
6.2
23
2
19
3
4
9.1
7.1
3.5
3.4
8.4
24
2
20
2.4
1.5
6.7
4.8
1.9
2.5
7.2
27
2
20
2.4
1.5
6.6
4.8
1.9
2.5
7.2
32
2
19
2.8
3.8
8.9
6.9
3.3
3.2
8.2
36
2
6
1.8
3.3
7.5
4.5
2.5
2.4
7.6
39
2
22
0
2.1
6.9
5.4
1.1
2.6
8.9
40
2
20
2.4
2
6.4
4.5
2.1
2.2
8.8
41
2
6
1.9
3.4
7.6
4.6
2.6
2.5
7.7
45
2
6
2
2.6
6.5
3.7
2.4
1.7
8.5
52
2
25
2.6
4.8
8.2
5
3.6
2.5
9
54
2
26
2.8
2.4
6.7
4.9
2.5
2.6
9.2
56
2
19
2.9
2.6
7.7
7
2.8
3.6
7.7
60
2
25
2.3
4.5
8
4.7
3.3
2.2
8.7
64
2
16
3
3.8
5.5
4.9
3.4
2.6
6
65
2
29
1.1
2
7.2
4.7
1.6
3.2
10
68
2
8
1.6
4.5
6.4
5.3
3
2.5
7.1
70
2
25
2.3
3.7
8.3
5.2
3
2.3
9.1
75
2
4
3
2
6.6
6.6
2.4
2.7
8.2
79
2
29
1
1.9
7.1
4.5
1.5
3.1
9.9
83
2
2
1.6
2.8
6.1
6.4
2.3
3.8
8.2
84
2
6
2.3
3.7
7.6
5
3
2.5
7.4
86
2
6
2.5
3.1
7
4.2
2.8
2.2
9
88
2
6
2.1
3.5
7.4
4.8
2.8
2.3
7.2
89
2
4
2.9
1.2
7.3
6.1
2
2.5
8
91
2
19
3
2.8
7.8
7.1
3
3.8
7.9
94
2
26
1.9
2.7
5
4.9
2.2
2.5
8.2
96
2
22
0.6
1.6
6.4
5
0.7
2.1
8.4
98
2
26
2
2.8
5.2
5
2.4
2.7
8.4
99
2
4
3.1
2.2
6.7
6.8
2.6
2.9
8.4
(Row Id.) – Customer identifier is the group that is most dissatisfied with PLE’s partnership. They only rank PLE in the top half of customer responses in product quality. Price, price flexibility, manufacturing image, and salesforce image are all ranked below the average. They are most dissatisfied with delivery speed and overall service. Group 3 (which consists of) Row Id.
Sub Cluster Id
Cluster Id
Delivery speed
Price level
Price flexibility
Manufacturing image
Overall service
Salesforce image
Product quality
3.4
5.2
5.7
6
4.3
2.7
8.2
3
3
3
10
3
10
4
3.5
6.5
6
3.7
3.2
8.7
30
3
10
4.1
3.7
5.9
5.5
3.9
3
8.4
31
3
10
3
3.2
6
5.3
3.1
3
8
34
3
10
3.4
3.7
6.4
5.7
3.5
3.4
8.4
37
3
10
3.6
4
5.8
5.8
3.7
2.5
9.3
48
3
10
3.4
3.9
5.6
5.6
3.6
2.3
9.1
53
3
10
4.5
4.1
6.3
5.9
4.3
3.4
8.8
57
3
27
4.9
4.4
7.4
6.9
4.6
4
9.6
71
3
3
3.6
5.4
5.9
6.2
4.5
2.9
8.4
82
3
30
3.4
4.6
5.5
8.2
4
4.4
6.3
93
3
30
3.1
4.2
5.1
7.8
3.6
4
5.9
(Row Id.) – Customer identifier is the group that seems well pleased with their business relationship with PLE. They rank at the top of the customer surveys in price level, manufacturing image, ove rall service, salesforce image, and product quality. Delivery speed and price flexibility are also ranked above average. Group 4 (which consists of)
Row Id.
Sub Cluster Id
Cluster Id
Delivery speed
Price level
Price flexibility
Manufacturing image
Overall service
Salesforce image
Product quality
13
4
13
2.8
1.4
8.1
3.8
2.1
1.4
6.6
16
4
15
3.4
2
9.7
4.7
2.7
1.7
4.8
18
4
17
4.9
1.8
7.7
4.3
3.4
1.5
5.9
21
4
13
3.3
0.9
8.6
4
2.1
1.8
6.3
22
4
18
3.4
0.4
8.3
2.5
1.2
1.7
5.2
25
4
21
5.1
1.4
8.7
4.8
3.3
2.6
3.8
26
4
21
4.6
2.1
7.9
5.8
3.4
2.8
4.7
29
4
15
3.5
2.8
9.9
3.5
3.1
1.7
5.4
33
4
21
5.2
2
9.3
5.9
3.7
2.4
4.6
35
4
13
2.4
1
7.7
3.4
1.7
1.1
6.2
43
4
17
4.9
2.3
9.3
4.5
3.6
1.3
6.2
44
4
21
5
1.3
8.6
4.7
3.1
2.5
3.7
46
4
17
5
2.5
9.4
4.6
3.7
1.4
6.3
47
4
23
3.1
1.9
10
4.5
2.6
3.2
3.8
50
4
24
5.4
2.1
8
3
3.8
1.4
5.2
55
4
18
3.8
0.8
8.7
2.9
1.6
2.1
5.6
59
4
21
4.3
1.8
7.6
5.4
3.1
2.5
4.4
61
4
23
3.1
1.9
9.9
4.5
2.6
3.1
3.8
62
4
21
5.1
1.9
9.2
5.8
3.6
2.3
4.5
69
4
24
5.3
1.7
8.5
3.7
3.5
1.9
4.8
72
4
24
5.6
2.2
8.2
3.1
4
1.6
5.3
73
4
15
3.6
2.2
9.9
4.8
2.9
1.9
4.9
78
4
15
3.3
2.6
9.7
3.3
2.9
1.5
5.2
80
4
17
4.5
1.6
8.7
4.6
3.1
2.1
6.8
81
4
24
5.5
1.8
8.7
3.8
3.6
2.1
4.9
92
4
17
4.8
1.7
7.6
4.2
3.3
1.4
5.8
(Row Id.) – Customer identifier has varying opinions about PLE, but mostly in the below average region. They only rank PLE above average in price level and overall service. The purchasing managers feel PLE is slightly below average in delivery speed. The rank price flexibility, manufacturing image, salesforce im age, and product quality at the bottom of the customer groups. Given this data and analysis PLE knows the areas they need to focus on with each customer group to improve the business relationship. It also shows how PLE is perceived in the industry by the select purchasing managers. The group statistics show several different levels of perceptions regarding PLE. For instance, the group average range in price (1.5 to 4.15) differs dramatically than the averages in quality ranging (5.18 to 8.25). This fact illustrates PLE is a premium company with the focus of the company being quality. With this knowledge PLE will be able to target the purchasing managers’ dissatisfaction and areas of concern while maintaining better relationships with others in their core business mission. We are also going to explore the usage and satisfaction level of the purchasing firms by evaluating different characteristics from the survey. The characteristics are: size of the firm, purchasing structure (centralized or decentralized), industry classification (resale or nonresale), and buying type (new, modified rebuy, or straight rebuy). We will use correlation analysis to evaluate the driving factor in usage level and satisfaction level to derive a cause-and-effect model
Correlation Analysis:
Usage Level Satisfaction Level Size of firm Purchasing Structure Industry Buying Type
Usage Level 1 0.710697543 -0.365171196 -0.402517394 -0.080503479 0.828454966
Satisfaction Level
Purchasing Structure
Size of firm
1 -0.456556445
1
-0.466362543 -0.074007154 0.709427536
0.816496581 9.06493E-18 -0.594088526
1 0.04 -0.5820855
Industry
Buying Type
1 0.048507125
As you can see from the above data, usage level is highly corre lated with satisfaction level (as you expect) and buying type. It is also strongly correlated in the negative direction with the size of the firm and the purchasing structure. The satisfaction level is strongly correlated with buying type and negatively with the size of the firm and the purchasing structure. The size of the firm and purchasing structure are very strongly correlated with buying type also showing a relationship. It doesn’t appear Industry type is correlated with any other factors evaluated, so we did not add it to the Cause-and-Effect evaluation. Finally the purchasing structure is negatively correlated with buying type. This would leave you to believe that buying type is a driving factor in both usage level and satisfaction level. The data also show a significant negative relationship between the size of the firm, purchasing structure with both usage level and satisfaction level. Our evaluation leads to the chart below:
Cause-and-Effect Chart from Correlations:
Size of Firm
Purchasing structure
Satisfaction level
Usage Level
Buying type
In conclusion the size of the firm determines purchasing structure which leads to buying type. These 3 factors most effect the satisfaction level, w hich is the main determining factor in usage level. PLE now has groups to further analyze and work to improve performance. They also have a Cause-and-Effect flow chart determining Usage level, which is the determining factor in PLE success. With the information provided PLE can implement a business plan to improve business relationships and increase the amount of goods being sold to purchasing managers, which will lead to future business success.
1
