Connector Pda Case Analysis

ConneCtor PDA Case Analysis

By Group 3

02/28/2016

Executive summary

Netlink, a joint venture between a major U.S. wireless carrier, Conglomerate Inc. and  a PC manufacturer, was about to launch a new hybrid product called ConneCtor integrating Personal Digital Assistant (PDA) with a “smart” cellular phone. This first product, the ConneCtor would converge functionality of both devices to offer a fairly lightweight device that can transmit and receive data and is powered by the common Palm Operating System. Previously, PDA’s from competitors Apple and Palm Inc came to market but failed to deliver on reliability, usability and functionality. ConneCtor had combined various features in different types of device, which could have a great market potential by performing a SWOT analysis (See Exhibit 1). The SWOT analysis results were helpful for us to continue our research.Then we started our report providing analysis about how Connector could find distinct segments which are easily identifiable and differentiated from each other by using segmentation and classification tool for excel. The data that is used in this analysis is collected by Happy Valley Consultants and all the tables are available in the appendix.

Recommendations

Assumptions of recommendations

1. The data available is representative of the overall population and, by extension, the full market.
2. Focusing on a market segment that is too narrowly defined would hinder success of the product launch.
3. Focusing too narrowly requires rapid, intense adoption across the entire segment while not focusing too broad will leave the product floundering in the market without a place. The balance is in focusing enough to develop a realistic. achievable place in the market while leaving space for adoption to increase over time. Our group was shooting for market segments of 25-30% total market population.

Recommendations for ConneCtor

After analyzing the customer base through conjoint analysis, we have divided the population into four distinct segments. Of these segments, we recommend targeting two groups, Urban Achievers and Upward Bounders, based on total segment profitability and product fit. We also recommend that Netlink position two distinct ConneCtor products (ConneCtor PIM and ConneCtor Pro) against each of the target segments, more specifically the PIM for Urban Achievers, and the Pro for Upward Bounders.

Rationale

1. Justification of 4 Segments Decision

An important decision in segmenting a population is how many groups to retain after clustering. Cluster Analysis is exploratory research and will not deliver precise recommendations for cluster size or dendrogram cutoff.

We decided on retaining four segments from the data set. The software provides limited guidance on making this decision. We employed a two-step process and the indicator that we relied on most was distance in both steps. Any number of clusters retained will meet our objective situation, and be consistently evident across our two-step analysis.

First, the group reviewed the dendrogram for significant “jumps” from one cluster to another upwards along the lines. To familiarize our group with the data available, we ran a 9-cluster segmentation to visualize the cluster types by distance on dendrogram and discern cluster size. Running a 9-cluster segmentation delivers a reference dendrogram and cluster-size statistics. The initial 9-cluster dendrogram is illustrated in Exhibit 2. Additionally, the group ran a 12-cluster Exhibit 3, 6-cluster Exhibit 4, and 4-cluster Exhibit 5 segmentation. The exhibits are valuable in illustrating where disparate clusters join together as the number of total clusters decreases. Though the dendrograms can give an indication of appropriate grouping levels, the dendrogram is only a “familiarization” step. This is where the second step goes further.

Secondly, our group visualized the resulting data in a graph, resulting in Exhibit 6. On the x-axis, the total number of clusters was plotted, 1 through 9. On the y-axis, the distances from the dendrogram were plotted, 0 to 3. From this graph, the group searched for a distinctive change in the line, where the increase or decrease in cluster numbers has an identifiable effect upon the distance. This identifiable jump occurs at choosing 3 clusters. Additionally, progressing to 5 clusters provides negligible value-add to the segmenting process. Therefore, the optimal choice should be 4 clusters.

Also, by running the k-means clustering analysis in R, graph (See Exhibit 7) illustrates the “between sum of squares/ total sum of squares” ratio, which is basically a measure of the goodness of the classification that k-means has found. Usually, the higher the ratio, the better the model would be. However, the optimal choice of k will strike a balance between maximum compression of the data using a single cluster, and maximum accuracy by assigning each data point to its own cluster. In other words,  to make our clustering that has the properties of internal cohesion and external separation, we decided the number of clusters by using the Elbow Method, which means choosing a number of clusters so that adding another cluster doesn't give much better modeling of the data. As shown in Exhibit 7, the "elbow" is indicated by the yellow dashed line. The number of clusters chosen should therefore be 4.

Segmentation variable analysis

By mathematically doing clustering analysis, we arrived at a final 4-cluster (segment) result. At this moment, we still need to further analyze the segment variables to check whether our segmentation is practical and actionable in solving our business problems. We assigned our four clusters to “Group A”,”Group B”, “Group C”, “Group D” and tried to look at their scores across different variables. Using Nielsen’s PRIZM consumer segments, we have named those four groups: Urban Achievers, Greenbelt Sports, Bedrock America,Upward Bounders(See Reference).

Based on our analysis on segmentation variables for 4 clusters. We found that those 4 groups have distinguishable preferences over PDA features, which proves that our segmentation is reasonable so far. In Exhibit 8, it shows that Urban Achiever and Upward Bounders have both above average Innovator quality,which means that they are both very likely to adopt new technologies. On one hand, they share some similarities in cell phone usage, email and web access, and use their cell phones more often than other two Groups. On the other hand, Group A and D have dissimilarities in many other aspects, like price that they are willing to pay, ergonomic, PIM usage, remote access, etc. Therefore, at this point, Group A and D become our optimal choice since we can cater to two target markets which encompass similar needs and slightly dissimilar features. Specifically, by targeting these two groups, we can use basically similar marketing strategies with slight difference in price and functionality. Besides, Group A and D both have large market size, which is very important because segments should be large enough to be profitable. We didn’t choose Group B and C not only because they have lower Innovator scores, but also majorly because of their small market size (See Exhibit 9).