Segmentation Case

Submit electronically on Canvas (1 page limit and include annotated screenshots)
Use annotated screenshots to display the information you used to make decisions

Additional guidance and help is available in a YouTube video explaining the details of segmentation analysis using Marketing Engineering for Excel ( needs to watch this video on youtube)

Answer the 5 cass exercise/questions listed on page 4 of the case (SmartwatchSegmentation) .
Below is some more info that may be helpful for some of the questions:

1- To begin the analysis, determine the number of distinct segments present in this market. Consider both the distances separating the segments and the characteristics of the resulting segments. Interpret the dendogram with 9 clusters (ME1_9cluster_Dendogram.xlsx)
see video at 1hr mark

and explain why four or five segments is a reasonable number to represent the market as distinct segments [calculate percent of information lost from going from 6 to 5 segments and from going from 5 to 4 segments]. Then, compare the output of a four segment description (ME1_4segment_sollution.xlsx) and a five segment description (ME1_5segment_sollution.xlsx). Identify the segment in the four segment solution that gets split into two groups in the five segment solution. Decide if the differences between those two groups are important enough (relevant for possible positioning decisions) to justify the added complexity from having an extra segment to consider. Choose which description of the marketplace you will use for the remainder of your analysis (4 or 5 segments).

— Make sure your segment you choose to target would work with a "go-to-market" plan that would leverage the strengths of the partner you select. Describe your "go-to-market" plan. Consider how to best incorporate the information in the discrimination tab. What would you offer the partner to bring them on board? Provide some specific information about the product features of the watch, the price point, the distribution plan, the advertising/promotional plan including the main launch message/slogan to emphasize and what advertising channels to use. Finish with a few sentences that explain why these decisions make sense for the segment you chose to target. Make sure to also highlight any concerns you have with the approach Intel has taken, specifically regarding the survey data collection. You want to state any caveats up front in case the plan does not work.

—————————————————————- more info about the case First, "cluster" is the generic term for grouping of similar individual units, and "segment" is the specific term for a group of customers. I could do "cluster" analysis on species of flowers. "cluster" analysis is the math behind this segmentation analysis. So in this case (and in the ME excel output) cluster and segment are used interchangeably. The point of question 1 on the case is to get you to experience the challenge of selecting how many segments should be used to describe the market. It is a key part of the analyst’s job in segmentation analysis. There is no "right" or "wrong" answer. You are trading off parsimony (a more simple view that can be digested with less effort) with nuance (a more detailed view that can reveal important differences). After understanding how much information is lost as we reduce the number of segments we use to describe this market, you should move towards deciding whether to proceed with the case using 4 or 5 segments to describe the market. Do this by factoring in a close examination of the 5 vs 4 segment solution and consider what specific information is lost as two segments are combined into one to go from 5 segments down to 4. The real fun comes after you settle on 4 or 5 segments and dig into the segment specific info.

My note to the writer, Please make sure you received the attachments files there is 5 of them the first one is the case and there is 5 questions on page 4 that needs to be answer in the 1 page write up the other 4 files are Excel files that has all the informations about the case if you have any questions about the case or need help please send me an email or a text I will be available online for the next 24 hours Thanks!

Classification Coefficients
Coefficients are from each variable in the discrimination function.
This matrix was used internally, and will be required to run further discriminant analysis (i.e., classification) on external data.
Discriminant Variables / Functions Function 1 Function 2 Function 3
iPhone -0.0759 -0.102 -0.322
CompBuy -0.0847 -0.883 -0.0458
Health -0.0524 -0.101 0.523
Finc 0.0546 -0.165 0.345
Sales 0.216 -0.305 0.409
Advt 0.316 -0.118 0.0545
Edu 0.313 0.0577 0.508
Cons 0.262 -0.397 0.453
Eng 0.14 -0.55 0.412
Tech 0.313 -0.0634 0.38
Retail 0.153 0.0811 0.521
SMB 0.224 -0.528 0.297
FB_Insta -0.297 0.496 -0.261
Twit -0.0406 -0.205 0.232
Snap -0.346 0.083 -0.1
YouTube -0.0496 -0.0436 0.359
Pod_radio 0.164 -0.0732 -0.346
TV 0.396 0.0453 -0.588
NewsP 0.194 -0.206 -0.0382
AmznP -0.209 -0.00523 0.231
Age 0.0938 0.0136 0.0196
Female -0.0475 0.0398 -0.0574
Degree -0.129 -0.0284 -0.318
Income -0.075 -0.209 0.0203


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