In by features/attributes, demographic groups, and discrimination. It

this graph, we can clearly see that the data analysis was done based on
discrimination. This is a clear indicator which segment of people based on age,
usage preference, education level, etc. would likely to accept the PDA product. In question 2, my recommendation was Segment C (cluster:
2,5,9). In this graph, we see a mixer of age groups from 28 to 51. But mostly
48 to 51 in cluster 2 and 5. Their level of income is much higher than cluster
9. No one from cluster 9 has ever owned a PDA, their usage/demand on cellphone
and PC are also relatively lower than other clusters. A better choice of focus
group may be segment B, which the income level for cluster 3 is slightly above
average and cluster 7’s income level is off the chart. Cluster 7 has higher
income level, better education, deal more with technology such as cell phone
and computers. Many of them also have experiences with PDA already. A similarly
good recommendation may be cluster 6, similar attributes with cluster 7 but
much younger professions, more innovative than cluster 7. However, I noticed
that from the discrimination spreadsheet, the Dendrogram categorize clusters
differently. If I was to re-categorize the segments, it would have been:

Segment A (1,8,5) Distance (8,727.07)

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Segment B (4,9) Distance (2,139.58)

Segment C (2,7) Distance (4,507.81)

Segment D (3,6) Distance (4,513.54)Question 4:            This analysis helped me to identify
each segments by features/attributes, demographic groups, and discrimination.

It allows me to compare the data and see which cluster or segment is best
suited for targeting by considering both demand and supply point of view. The
method of data collection is effective and easy to understand. It helped me to
identify which segments of consumers would have a need in conneCtor’s PDA and
prioritize these segments based on profit maximization. In addition, the tool
helps to analyze which features should ConneCtor focus on future products and
what the consumer tastes and preferences are. Overall, it is a helpful
tool/method based on the data provided above . Question 5:            Since the data/sample is fairly
limited, it may not reflect the market in the bigger view. Also, setting up
cluster size could be difficult. If the cluster size is set too high the result
is scattered and hard to measure, if too low, the result is too crowded and hard
to measure. Managers would have to go through several trail and errors to find
the suitable measurement size. I also believe that more questions may be asked
to eliminate some of the biases during the data collection process. Another
problem is that the analysis always segments clusters with 2 end different
outliers, and the attribute variables are sometimes not similar but rather
extreme. For example, in question 3, I would rather segment cluster 6 and 7
together than (1,6) or (3,7). It is simple but sometimes it doesn’t make sense,
like placing 2 extreme clusters together. Question 6:            I would recommend the company to
conduct exploratory research on consumer preference/POV on PDA in general, just
to see how consumer feel about the idea or generate suitable questions to ask
in descriptive research. Then conduct descriptive research with larger sample
size. After they found the focus and problems of the current PDA products, they
can then improve their product to meet the demands of their targeting segment.

It would best for them to just improve their market research and found out
their suitable segment and launch a product to meet the demand of the targeting
segment.The data collected within the analysis would provide
the company with a means of roughly understanding which group of people to
target but not the  needs of the consumers. For now, I would
recommend the company to focus on their current software and hardware updates
to create better customer and company relationship to build customer loyalty.

At the same time, the can continue to focus on targeting segment that are
innovative/professions/high income level consumers.