Analysis Results for the HW posted on LMS

Hi all,

I've received feedback saying that getting the R code to run has become an issue for several reasons.

MKTR's value-add is (or should be) imparting hands-on practice in:

• problem formulation
• opportunity recognition
• a generalist understanding of methods available
• tool selection
• interpretation skills
• and big-picture perspective.

Your getting stuck in R code execution issues doesn't serve that purpose.

So, I'm running the R analysis on my machine and sending you what I think are the results you need (and then some more) for "solving" the HWs.

Mind you, these are not "solutions" in themselves, merely intermediate results.

Your task of thinking through the HW problem, interpreting the results and putting it all together to highlight insights obtained remains critical in solving the HW.

Pls find on LMS a folder titled "R analysis results for the HW" or something like that.

It should contain the results from my R runs for:

• Session 5 HW (individual part)
• Session 6 HW
• Session 8 HW part 1.

Pls let me know if anything else is required.

Sudhir

P.S. When we first wrote and checked the code, we assumed students' web access rights would be much the same as ours. Seems we were mistaken.

Then, new hope was shinyapps would solve the problem. Mistaken again.

Session 8 Homework

Hi all,

Pls find uploaded in LMS in 'session 8 folder' two R code files and one data file.

The code and Homework is self-explanatory, for the most part.

Pls follow instructions and use the blog comments or email aashish_pandey@isb.edu in case of any queries.

You HW has two parts. Both individual.

Part 1: Mapping Brand communities based on Affiliations

Run the code in 'session 8 HW code 1.txt'.

Generate the brand association graph with weighted edges.

Run the community detection algorithm on it.

Answer the following Qs in a few slides.

• Which brands seem most 'central' to the network?
• Which 'space' is this? What might brand locations in this space signifiy?
• How many brand communities arise?
• Pick any one brand cluster/ community. Interpret what might be the most important affiliations or attributes driving clustering in it.
• How many singletons (or single member clusters) are there? Speculate on why these brands seem isolated.
• If you're the AXN channel, which brand community do you think is best aligned or affiliated with you? Why?

Part 2: Mapping your Facebook friends communities

Run the code in 'session 8 HW code 2.txt'.

Get access token for FB API as was demo-ed in class.

Pull and save the data - a named friends' list, an anonymized friends list and a listof pages liked.

In a few slides, answer the following Qs:

• Do you see natural groupings arise? Which ones are they? ID them like I had done for my FB example in class and paste that on a Slide.
• List some common network properties associated with your FB graph (transitivity, Avg path length)
• How many communities arise? Average size?
• How many singletons (or single member communities) are there?
• Pick a community to analyze. Use anaonymized graph for this. What are the network peoperties for this community?
• Put your marketing hat on. Write two Qs that you as a Marketer might be interested in after studying the graph.

Deliverable: Three pieces to submit - (i) A PPT that answers Qs posed above. Break PPT into two sections for each part.

Title slide should contain your name PGID.

(ii) the anonymized FB friends' list and (iii) the liked pages list.

Pls zip these into a folder titled yourName.zip and submit into appropriate dropbox.

Deadline: Midnight Tuesday 30-sept.

Tight deadline, I know. But the code runs smoothly, will be done in minutes.

My suggestion - do the HWs in a group. If any one person in your group has solved their HW by running the R code, pls borrow and share the results with peers.

After all, its the interpretation of the analysis and not running the analysis that this HW hinges on.

Part 1 is common to everyone anyway.

Any queries, contact aashish or me.

Sudhir

MKTR Project

Hi all,

Pls find below details regarding your MKTR group project.

Your Project Task:

1. Pick any substantive business problem which can in turn be mapped into 2-3 R.O.s

2. Ensure that at least one R.O. is exploratory in nature and at least one, confirmatory.

3. Among the repertoire of tools we've covered (think of the survey method, FGDs, perceptual maps, text analytics, STP, experiments etc.)...

4. ... ensure that your R.O.s map onto one tool each.

5. Collect data as required (don't aim for large samples given the paucity of time etc) for your R.O.s

6. Think of the frameworks we've covered (constructs, habit patterns, network-perspective, hypotheses testing)... 7. ... and apply them to solve the R.O.s.

Deliverable Format:

One PPT, no more than 20 slides long (excluding annexures).

First slide should contain group name and membership and project title.

Second slide explains the business problem context and lists the R.O.s

Next few slides constitute the "Methods Section", outline the steps taken to address the R.O.s and the tools employed.

Next few slides are the "Data Section", summarize the type and amount of data collected.

Then follows your "Results and Discussion" section, wherein you lay out your results, interpretation etc.

Finally, end with a "Insights and recommendations" section.

Name the PPT as yourGroup.pptx and submit into teh appropriate dropbox.

Deadline:

The last midnight before Term 5 starts. (12-Oct midnite, I think).

Pls find below a list of Grading criteria likely to apply in project evaluation:

1. Quality of the Problem context chosen - Creativity; alignment with the rest of the project; How well can it be resolved given the data at hand. Etc.

2. Quality of the R.O.s - How well defined and specific the R.O.s are in general; How well the R.O.s cover and address the business problem; How well they map onto specific analysis tools; How well they lead to specific recomendations made to the client in the end. Etc.

3. Clarity, focus and purpose in the Methodology - Flows from the D.P. and the R.O.s. Why you chose this particular series of analysis steps in your methodology and not some alternative. The methodlogy section would be a subset of a full fledged research design, essentially. The emphasis should be on simplicity, brevity and logical flow.

4. Quality of Assumptions made - Assumptions should be reasonable and clearly stated in different steps. Was there opportunity for any validation of assumptions downstream, any reality checks done to see if things are fine?

5. Quality of results obtained - the actual analysis performed and the results obtained. What problems were encountered and how did you circumvent them. How useful are the results? If they're not very useful, how did you transform them post-analysis into something more relevant and useable.

6. Quality of insight obtained, recommendations made - How all that you did so far is finally integrated into a coherent whole to yield data-backed recommendations that are clear, actionable, specific to the problem at hand and likely to significantly impact the decisions downstream. How well the original problem is now 'resolved'.

7. Quality of learnings noted - Post-facto, what generic learnings and take-aways from the project emerged. More specifically, "what would you do differently in questionnaire design, in data collection and in data analysis to get a better outcome?".

9. Creativity, story and flow - Was the submission reader-friendly? Does a 'story' come through in an interconnection between one slide and the next? Were important points highlighted, cluttered slides animated in sequence, callouts and other tools used to emphasize important points in particular slides and so on.

Any queries etc, pls contact me.

Pre-reads for Sessions 8 and 9

Hi all,

A few quick announcements.

There's been a change in session schedule. I'm swapping sessions 8 and 9.

So, next we'll have Network Analytics, which was originally session 9 (and now will be session 8).

--------------------------------------

Pre-reads for session 8 network analytics:

There are two, both from McKinsey Quarterly.

The first is an article called "Demystifying Social Media" from April 2012. Pls free-register with the site to download a PDF.

Optional: The second is a 12-minute video, again from McKinsey on "Making sense of Social Media".

More McKinsey speak on their Social Media Marketing framework.

Worth a quick watch, if you want to further explore the themes that came up in the first pre-read above.

--------------------------------------

Pre-read for Session 9: Hypothesis-testing and Experimentation

Reading No. 9 "How to design smart business experiments" in the coursepack.

Special Reading for Today's regression piece.

I did get the sense that folks weren't entirely comfortable with the basic regression part in today's class.

As a refresher to your stats core, pls consider reading the first few pages of reading no. 12 in your coursepack "Practical regression - regression basics".

I hope that helps.

--------------------------------------

Announcement: R tutorial

Will be held in AC4 7-8 pm on Thursday, 25-Sept. (pending venue confirmation).

Plan is to walk you through basic input-output and help functions in R.

Any and all Qs and doubts and installation and implementation and execution issues are fair game.

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Running the tm.plugin.webmining package for session 5 homework.

Several folks have contacted me with issues in running this.

Pls ensure you have tm and tm.plugin.webmining installed.

Further, run the code on base R and not on Rstudio.

I've updated the code in session 5 HW blog post.

Sudhir

Keeping Track of the various submissions due

Hi all,

One of you, SS, wrote the following to me:

Dear Prof,

We have received quite a few mailers on the MKTR assignments over the last week and we are finding it hard to track these separately and fish out for all of these in the blog.

Request you to kindly create a spreadsheet or a word doc in your blog (with a dedicated section for HWs) and put all the HW details and deadlines on it.

We usually have such a document floated for other subjects so that it becomes a one-stop shop for looking up the deadlines for a course.

Thanks for your help.

My response is as below: (click for a larger view)

Sudhir

Session 9 Network Analytics Homework Survey (Time sensitive)

Hi all,

I just realized that due to oct-2 Gandhi Jayanti holiday, session 9 is pushed forward from 30-sept to 26-sept.

That gives me precious little time. Need to collect two sets of data from you for session 9:

(a) One on self-reported snowball-sampled social interaction data (see survey below), and

(b) one where you pull your data from facebook for analysis (friend-lists and pages Liked)

I'll go over the how to of the facebook data pull in class in the first few minutes of session 7.

Meanwhile, pls take the one last survey filling exercise remaining at the link below:

Social network analysis Co2015 Data collection survey

Deadline: The deadline for the survey is Monday 22-sept Morning 6 a.m.

This is time-sensitive, Pls don't keep it till the last minute.

Shouldn't take more than 15 minutes on average. Qs take you through both objective and hypothetical scenarios.

Pls think through before you answer. Full credit *only* for complete and timely submissions.

This homework will have more credit than the other surveys you filled up because of its slightly higher length and complexity.

Any Qs etc, pls use the blog comments section or contact me.

Sudhir

Session 6 Homework

Hi all,

Session 6 on segmentation and perceptual mapping, got done y'day.

Since we had structured, metric data this time - running R was quite smooth in-class, unlike in session 5.

Pls find uploaded on LMS R code and related datasets for session 6. Pls try to replicate classwork analyses at home with these.

Update: Some folks asked for additional reading material on the clustering methods themselves. Here goes.

The 3 main data clustering methods we covered were: (a) hierarchical clustering via the hclust() function in R

(b) the popular k-means technique ( kmeans() in R )

and (c) the powerful model based clustering technique of Fraley and Raftery (2002) under the Mclust() func in R.

In the second half of session 6, we covered perceptual mapping and joint space maps.

For those seeking greater interpretative clarity on the p-maps, pls scan through this old blog post on precisely this topic from 2012.

For those looking to explore particular methods further, I hope the hyperlinks embedded above help.

Coming to the HW:

Given that session 5 homework has been delayed, am inclined to go easy in this one. HW here shall be light. Only.

There are two small, simple group homeworks for this session. Both easy to run using the code provided. Your interpretation of the analysis is of interest to me.

Homework 1: Segmentation (group submission)

Kirin Brewery Limited, founded in Yokohama, Japan over 100 years ago was the largest beer firm in Japan, and among the 10 largest in the world.

They entered the world's largest beer market, the US, in the 80s and 90s.

You can read up about Kirin and the main characteristics of their offerings here.

Kirin performed some MKTR upon entry and some of the data they collected in available for our analysis.

Read in 'Kirin basis variables.txt' and run the associated code given in 'R code for session 6 HW.txt'.

Hint: You may want to spend a minute carefully reading the attributes they collected data on. What constructs were they after?

Now answer the following Qs:

1. How many segments are there (under mclust)? What is the ratio of the largest to smallest segment?

2. Characterize the segments. Give each an informative name.

3. Read up on the flagship offerings of Kirin Brewing USA here. There are three main Kirin beer variants: Ichiban, Light and Free.

Given each variant's particular features, which segment should each variant target? Why?

Deliverable: Write your answers in a PPT slide deck not exceeding 3 slides. Name it as your_group_name.pptx and drop into appropriate dropbox.

Homework 2: Perceptual Mapping (group submission)

We return to the PDA case we did in class for segmentation. Read in the datasets and run the code as given in 'session 6 HW code.txt'.

Answer these Qs below.

1. Who are PDA ConnecTor's closest competitors? On what attributes do they compete?

2. Present a quick, simple SWOT analysis based on the JSM for the PDA product.

3. Which products or product types seem to have the highest preference?

4. Any white spaces for potential entry that you see?

Deliverable: Another 2 slides in the same PPT as the one above.

Deadline: A week+ from now. Midnight 28-sept Sunday.

Any queries etc. contact me or use the comments section below.

Ciao.

Sudhir

P.S. Was some 20 minutes short of time in Section A. Overcompensated perhaps and was 20 minutes ahead of time in section B. Finally, Section C finished just right.

Sorry about the mismatches. My aim is to present a consistent experience in all sections.