Exam and HW related Queries

Hi all,

MKTR class got done today. Whew.

Lot of new, experimental stuff found its way into the course this year.

Hence, 'twas a tad more stressful than I'd first budgeted for. It showed, I guess.

Got quite a few queries re the exam. Here's my attempt at answers:

Exam announcements:

• You'll have plenty of time for the exam. 2.5 hours officially, you probably won't need that long.
• *Only* those pre-reads which we tested for in the class AND which are from your coursepack are within-bounds for the exam.
• I'm traveling from tomorrow, won't be available on campus. Limited email access.
• No intention of making a sample paper solution. Pls consult your peers to compare answers.

HW Announcements:

Session 8 HW is due tonite. Try to get it done ASAP. Submitting by tomorrow midnight is also OK.

• In part 2, take a screen-shot of your named-file network and paste as image onto your PPT. No code to graph your anonymous file was sent.
• In HW part 2, there was a Q on finding out the network metrics for select subclusters. Ignore it. We forgot to send code to enable that one.
• Seems folks with 1000+ friends are having trouble pulling data because the token expires before the pull is complete.
• My advice is to borrow or use a 3G connection for such a data pull. ISB firewalls student access to file downloads and its causing much delay even to install packages.

Project Announcements:

Pls find the project related detailed document here.

Whereas in Homeworks, you've focussed on one tool at a time, the project requires you to bring to bear multiple tools for the same problem.

Important perspective emerges when stitching together different tools for the same substantive business problem.

Use any two tools - no need for large samples or big data collection. You have enough data collection options in your repertoire already.

Unfortunately, the project deadline cannot be pushed further. Pls finish and submit on time.

JSM interpretation explanation:

Some things to remember when interpreting JSMs.

One, when evaluating dots (brands) against attributes (arrows), always drop perpendiculars from the dots to the arrows.

Two, further away from the mean in the positive direction is a brand's projection on an attribute, higher its value on that attribute.

Three, the preferences (pink lines) are akin to attributes (i.e., are lines). Thus, when evaluating brands (dots) against preferences (arrows), drop perpendiculars as usual.

Four, the angle between two arrows (attributes) reveals how correlated they are.

Four, its tricky to try comparing individual preferences (pink lines) against attributes (blue arrows). If one really had to, taking the angle between them is the way forward.

Consider the Qs below from your practice exam. (click for larger image)

For Q 7.1., I'd look at Table 7.1's "Palm V" column. Highest value there, 8.5, corresponds to Light_weight attribute.

For Q 7.2., I'd look at Table 7.1.'s last row, "Preference". Highest value there, 6.6, corresponds to Compaq_IPac.

For Q 7.3., I'd look at Figure 7.1. The highest brand shown there on both attributes, is Sony_CLIE.

For Q 7.4., I'd look at the correlation table given in Table 7.2. In the row "memory", highest value, 0.93, corresponds to "software". In the figure, the same is shown as ThirdPartySW.

For Q 7.5., I'd look at Figure 7.1. The preference attribute has the smallest angle (hence, concordance) with R50's preference vector. Note: Such inferences are dicey, but we're doing it here anyway.

For Q 7.6., I'd look at Figure 7.1. for the smallest angle between preference vectors and the attributes mentioned. Again, note: Such inferences are dicey, but we're doing it here anyway.

I'd say R22 and R30 respectively. However, in the exam you can expect no Qs linking the pink lines and the blue arrows.

Hope that helps.

Good luck for the exam. And beyond.

Sudhir

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.

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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.

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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.

Session 5 Homework

Update:

Sorry folks, typed in the wrong URL by mistake. Those looking for sessions 8 and 9 pre-reads, pls go here.

The following are pre-reads for session 7:

1. AI meets the C-Suite (McKinsey Quarterly)

and 2. Track Customer Attitudes to predict their behaviors (HBR)

The course-pack reading for session 7 is optional and these two are mandatory.

Sudhir

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Hi all,

You might want to lookup the list of shiny apps available listed in the session 5 updates blog post here, before we start.

The homework has three parts. Only two of the three need be done and submitted.

Homework part 1: (group submission, mandatory)

Choose any non-obscure product or service on Flipkart or Amazon (or any other review aggregation source).

Your R.O.s are: (1) Find the top few things people like about the product.

(2) Find the top few things people dislike about the product.

(3) Suggest a (re-)positioning strategy for the product based on the above.

Pull 100+ reviews of the product.

Note: A Flipkart shinyapp is available already. Just follow instructions on the first page of the shinyapp.

We're working on an amazon shinyapp as well. watch this space for updates.

Update: Turns out Amazon pages are now dynamic. They were regular pages till last year. So no shinyapp happening on it.

Text analyze the corpus for insights.

Not everything we can do is up on shiny. Would help massively if at least one member per group runs the classwork R code successfully on their machines.

Homework part 2: (Individual submission - option 1)

Use tm.plugin.webmining to pull data from any of the following news aggregators. Pick any product/ firm/ brand/ celebrity that has been in the news lately.

Pull the last 100+ news articles wherein this entity was mentioned in the article title.

Recall the classroom example wherein we did this for Zara:

install.packages("tm") # if using for the first time install.packages("tm.plugin.webmining") library(tm) library(tm.plugin.webmining) # Note: Run below on base R, not RStudio zara <- WebCorpus(GoogleNewsSource("Zara")) x1 = zara # save the corpus in a local file x1 = unlist(lapply(x1, content)) # strip relevant content from x1 x1 = gsub("\n", "", x1) # remove newline chars x1[1:5] # view content write.table(x1, file.choose(), row.names=F, col.names=F) # save file as 'zara_news.txt'

Replace 'zara' above with whatever entity you chose.

Alternately, try running this shiny app for googlenews pulls. Its not very stable but will do for now.

Text-analyze the corpus for sentiment.

Note: Do you see how the corpus thus obtained can potentially help you mine, measure and score some notion of "PR buzz" for the entity?

Your task: ID the two most positive and two most negative articles.

In a PPT slide or two, write what you found about the reasons for positive and negative sentiment.

Update: Pls insert the following lines of code after you run the older code for sentiment analysis.

This is to obtain the most positive and negative documents.

############### head(pol$all[(order(pol$all[,3], decreasing=T)),]) #– Top positive polarity document head(pol$all[(order(pol$all[,3], decreasing=F)),]) #- Top negative polarity document ##################

Homework part 2: (Individual submission - option 2)

Alternately, instead of HW part 2 above, you could do the following.

Take any long (as in 10+ pages) soft copy article that you know and have read.

Use the textsplit shiny app to split it into uniform length parts (of say 25-50 words each).

Now, text anaylze the split document for topics using the shinyapp for topic mining.

In a PPT, paste the wordclouds for each topic and write your interpretation for what that topic means (in a few descriptive words, is all).

Deliverables and Deadlines:

The deadline for this session's HWs is a week from now. Next week Friday (26-sept) midnight.

Drop boxes will be up for session 5 HW part 1 and HW part 2 separately.

For both homework parts, pls submit a zipped folder containing (a) the text dataset you used, and (b) the PPT you made.

Pls remember to write your (group) name and PGID on the title slide. Name the PPT as name_HWnumber.pptx

Added later: The PPT should be <10 slides in length. Feel free to add more slides in an annexure, if required.

The HWs are all HCC level 0. Feel free to take any help from anybody as required.

Any queries etc, contact me.

Ciao.

Sudhir

Session 5 Updates

Hi all,

Session 5 text analytics, was R heavy and generally heavy.

'course we barely scratched the surface, but even that was quite a bit for a 110 minute session.

Session Summary:

To recap, the main things covered were:

• Some understanding of the bag of words formulation, of elementary pre-processing and of Document-Term Matrices and so on.
• Basic descriptive text analysis and wordclouds (level '0'), given a clean, well-structured text dataset in excel format
• Grouping structure in top terms - using qgraph() to plot and see which terms co-occur in documents more often than at random. (Level '1')
• Grouping structure in documents which typically map one-to-one to respondents. IOW, respondent segmentation possibilities using simple k-means. (Level '2')
• Basic sentiment analysis using qdap. Finding sentiment laden words, understanding document valence, measuring sentiment polarities. (Level '3')
• Topic mining text data. Use of Latent Dirichlet Allocation or LDA models. For both corpora and single documents split into parts. (Level '4')
• Web extraction of text data from structured pages and using tm.plugin.webmining from select sources.

Now, since MKTR is a hands-on course, its time to apply session 5 learnings by doing-it-yourself. There'll be two avenues for this:

(a) replicating classwork examples at home. The final PPT slide deck and the classwork R code should, by now, be up on LMS.

(b) doing the homeworks diligently either individually or in groups. The homeworks are coming up in the next blog post.

About R and Shiny:

Shiny is a wrapper for R code that can be run off the cloud on any browser from anywhere in the world without needing local machine R installation etc.

We're trying to shiny-fy as much of the analysis we're doing in class. The list of shiny Apps till session 5:

Shinyapp for factor analysis

shinyapp for cluster analysis (all 3 types - Hclust, mclust and kmeans)

shinyapp for basic i.e. descriptive text analysis and wordcloud

shinyapp to split a single long article into multiple parts of uniform length

shinyapp for flipkart data extraction

shinyapp for foundational topic mining using textir and maptpx packages

shinyapp for JSMs

The landing page of the shinyapp will have basic instruction and detail for running the shinyapp.

Should things still be unclear, pls email aashish_pandey@isb.edu with a copy to me and let us know.

For the record, I encourage you to use them as backup for your homeworks etc, should regular R code run into glitches.

About the R code now on LMS:

We double-checked the R code we've now putup on LMS, one reason for the delay in its release.

Sure, during the class itself, there were missteps and unexpected glitches here and there, what with trying to run so much disparate code on an unfamiliar machine (i.e. the classroom laptop).

Chances are you'll run into a few glitches here and there yourself while trying to run the codes, perhaps.

If so, first ask around among your peers if someone has faced and solved such an issue.

If the issue remains unresolved, drop a comment on the relevant session blogpost. Then contact my RA Mr Aashish Pandey aashish_pandey@isb.edu

Finally, if the issue is still unresolved, drop by my office with your machine.

P.S. Sorry about the delay in releasing this blog post. Should've come right after Tuesday, ideally.

Dassit for now. Ciao.

Sudhir

Some course related Announcements

Hi all,

A few quick administrative annoucements.

1. External speaker Guest lecture lecture:

We're having Mr Milind Chitgupaker come in as guest lecturer to deliver a talk on analytics in MKTR and in general.

His is a battlefield perspective. He's among the brightest programming minds I know, is the owner of 6 technology patents, is a 15 year veteran with IBM's Global consulting division and currently an entrepreneur in the analytics startup space.

You can find more about him and his firm, here.

Here's he on Youtube on the work his firm did in the last General Elections.

I'm happy and proud to say I have been associated in building this startup up from its beginnings and count Milind among my close friends.

Milind gave the MKTR guest lecture last year as well and it was well received. (Don't take my word for it, feel free to ask alums and confirm).

Date: 20-september. Time 7-9 pm. Venue: AC4 LT.

Seats are limited to venue capacity (70). Pls sign up early, on a first come first serve basis, at this signup sheet here.

If you've missed or plan to skip any class, attending this lecture will insure you one attendence loss. For such folks, CP in the lecture counts towards general CP, besides.

2. Pre-reads for future classes:

For session 5, the pre-read is chapter 1 in the book Modeling Techniques for Predictive Analytics (in R) by Thomas Miller.

You can find the PDF here.

Go through the 12 page chapter carefully, its a fairly easy read. A pre-read quiz *may* land in session 5.

Other pre-reads from the coursepack - 'Perceptual Mapping: A manager's guide' (serial number 5) for Session 6;

'Market Segmentation, Target Market Selection and Positioning' (serial no. 6) for Session 7;

And 'How to design smart buisness experiments' (serial no. 9) for session 8.

The coursepack readings are classic HBS articles. There's a chance I may overrule them and assign new readings instead.

3. R tutorial and help:

Some folks have asked me for a small tutorial on R. To gauge interest, please email the AAs with a blank email entitled "Interested in an R tutorial".

The sampling piece we did on R is a very nice intro to doing R on a small problem for the first time, IMO.

Pls find here an old, detailed blogpost explaining my code etc.

If you're planning a more comprehensive and structured deep-dive into R, let me quote from an older blog post from Analytics Yogi below:

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2. I've received student queries about additional sources of material for study. Well, there are two ways about it. If you are presently working on a problem on R and encounter roadblocks, then the best thing is to simply google your query. Chances are sites like Stackoverflow will have answers for it. It usually works very well for me.

On the other hand, if you are looking for a structured way to start, then there are any number of books you could consider getting and starting. Below I list some which can help the rank beginner get started:

A beginner's guide to R from Computerworld, a video introduction to R here from Google and here is a full fledged book from the Springer publishers' stable on how to get started in R.

Better still is this list of links for books on R: Link for list of books and downloads for R. More advanced users, especially after you are introduced to supervised machine learning as part of the CBA program, may want to consider the following books (some of which are free downloads):

Machine Learning with R, by Brett Lantz. The link takes you to the table of contents which you can browse and also through a sample chapter.

This short document from MIT's open courseware on Machine learning is a useful reporsitary of the very basic datasets, algorithms and packages a beginner can use to get started on the machine learning part of R analytics.

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Will putup a QCs analysis from the past two sessions in the next couple of days.

That's it from me for now. Ciao.

Sudhir

Session 4 Homework

Update:

Hope all's going well with the FGD prep.

A few links (content related) that maybe useful:

An article speculating on 5 Trends Shaping the Future of Work in the next decade+. Useful insights for the moderator's checklist, perhaps?

The homesite of Airbnb which is an infomediary+ for a C2C business model in the bed-n-breakfast business. A more general-purpose classifieds/ C2C site is craigslist. Is C2C or some blend thereof a key trend that we'll come to expect as a matter of habit in many sectors?

A recent economist article that talks about how small businesses (e.g. microbreweries) are issuing bonds that promise to pay an interest + part of the interest in terms of product. Very interesting. The future of business financing as Euro banks retreat from lending in the face of an ongoing bank capitalization crisis?

And so much more.

Looking fwd to seeing the FGD output, folks.

My friendly advice: Have fun with the FGD homework. It will show in the output quality. Good luck.

Sudhir

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

Hi all,

Session 4 got done Y'day. Longish session. Ran into time trouble in 2 of the 3 sections.

Session was readings heavy. At the intersection of qualitative and predictive analytics, too.

Was told that my star readings of the day - Target and Febreze - had already been discussed in CoBe.

Sigh.

Can only hope the MKTR treatment of these readings was distinctive enough to have added value, new perspective and fresh insight.

Only.

OK. Enough musing. There are two home works for Session 4.

Session 4 Homework 1:

Pls fill up the following two surveys.

1. Survey on Brand preferences and some social network ties.

The first survey above'll come in handy in the analytics sessions including both text and network analytics.

Fair warning: Its kinda longish, but *totally* worthwhile in terms of analysis insights.

For instance, wouldn't it be great to know how information on elective bidding or placement applications flows across social interaction networks?

So, pls do your bit to contribute to this insight. Do take the time and patience to fill it in. In any case, full credit only for *complete* submissions.

2. Survey on perceptual mapping inputs.

The second, available here, for the perceptual mapping session.

Privacy Note: I've assigned a unique ID string sequence to every MKTR student. Will use that only for both Classwork and HW analysis. Your responses are thus protected and anonymized.

Deadline for survey filling: Sunday 14-sept midnight.

Session 4 Homework 2:

The following is group HW, one submission per group only.

Author Jamie Turner compiled a slideshare presentation of 50 Amazing Facts of Mobile Marketing in 2012.

Download and go through the above slideshare slide deck. Its a collection of factoids on what I admittedly cheesily had dubbed 'new age habit patterns' in class.

Clearly, if even half the factoids in there are even half true, the world as we know it is already half in flux.

And firms would dearly like to know more about emerging trends in how the typical high-value consumer of tomorrow (i.e. you and people like you):

(i) perceive 'cues'.

For instance, how might traditional 'cues' or triggers that firms have used such as conventional ads, display ads, banner ads, search ads, coupons, product reviews, etc - change and adapt to tomorrow?

(ii) might develop 'routines' around.

For instance, how might the purchasing process, the choice process, the search process etc be impacted by new age habits?

(iii) And might assess or measure 'reward' or utility at the end of the habit loop.

For instance, ask what need is satisfied by the process? Instant gratification at the end of impulse purchases? Distraction or diversion? Deal prone-ness and coupon arbitrage? Some compensatory mechanism?

Frame an exploratory R.O. around this interest firms have.

However R.O.s, like much else of MKTR, are context specific. So, give the R.O. some context.

Select a sector (Services/ manufacturing/ Infra etc.), an industry / vertical (Edu, healthcare, Banking, Travel, entertainment etc.) or even better, a firm (goog, amazon, HBO, apple, flipkart, McKinsey, IBM, some startup etc.).

Ask yourself what might be a pressing R.O. for the relevant space/ domain that hinges on how new age habits turn out.

To solve the R.O., run a mini focus group discussion (mini-FGD) with around 4-6 participants for about 45 odd minutes among your peers (yup, people from your or other MKTR groups are fine as participants).

Whether the participant profile is that of 'experts', of 'opinion leaders' or of 'regular' consumers in the target segment is a judgment call you'll have to make (based on your R.O., besides).

Explore group dynamics in the focus group, if any. Are there constructs and possibilities seem to agree upon (unification dynamics)?

Are there faultlines wherein folks disagree sharply (Polarization dynamics)? What topics might folks be ambiguous about? Etc.

Submission Format: A short PPT.

• Title slide of your PPT should have your group name, member names and PGIDs.
• Second slide, write your D.P. and R.O.(s) clearly for the problem context.
• Third slide, introduce the FGD participants and a line or so on why you chose them (tabular form is preferable for this)
• Fourth Slide, write a bullet-pointed exec summary of the big-picture take-aways from the FGD.
• Fifth Slide on, describe and summarize what happened in the FGD.
• Note if unification and / or polarization dynamics happened in the FGD.
• Name your slide groupname_FGD.pptx and drop in the appropriate dropbox by Friday (19-Sept) midnight.
• Update: Am told there's some CFIN midterm on 19-Sept. So am postponing the deadline by a bit to 23-sept Tuesday midnite.
• Extra points if you can put up a short edited youtube video on the major highlights of the FGD. Share link on the PPT.

Additional FGD Gyan (based on last year's experience):

• The point of the FGD is *not* necessarily to 'solve' the problem. It may merely be to point a likely direction where a solution can be found.
• Different R.O.s lead to very different FGD outcomes. For example (taking last year's Google Glass example), if you define your R.O. as "Explore which portable devices will be most cannibalized due to Google Glass" versus "Explore potential for new to the world applications using Google Glass", etc.
• Keep your D.P. and R.O. tightly focussed, simple and do-able in a mini-FGD format. Having too broad a focus or too many sub-topics will lead nowhere in the 30-45 odd minutes you have.
• Start broad: Given an R.O., explore how people connect with or relate to portability, Technology and devices in general, their understanding of what constitutes a 'cool device', their understanding of what constitutes 'excitement', memorability', 'social currency' or 'talkability' in a device and so on. You might want to start with devices in general and not narrow down to Google Glass right away (depending on the constructs you seek, of course).
• Prep the moderator well: The moderator in particular has a crucial role. Have a broad list of constructs of interest, Focus on getting them enough time and traction (without being overly pushy). For example, the mod could start by asking the group: "What do you think about wearable devices? Where do you see the trend going in wearable devices like your smartphone, fuel bands and so on?" and get the ball rolling, then steer it to keep it on course.

Some FGDs from last year can be seen here.

Session 3 Homework

Hi all,

Session 3, spanning Questionnaire Design and [Exploratory] factor analysis, got done today.

Ran into time trouble and some teething Rstudio issues in Section A. Sorry about that.

About Shiny:

Will try to makeup in Section A by going over the shiny app for Factor analysis again in the opening minutes of Session 4.

Shiny allows you easy web-based cloud-located analysis capability. And though it has an R back-end, you wouldn't see any R code while using it.

Here is the link to the Factor analysis shiny app.

P.S. Some of you might find this old blog post useful (from 2 years ago) on how to interpret factor analysis output.

There are two homework assignments for session 3 - one group submission and the other individual .

Session 3 Homework 1:

Update: Am getting quite a few Qs asking if a scale other than Likert can be used etc. Sure, it can. Likert is important in the ocntext of behavioral constructs. For regular, descriptive Qs, use other scales by all means. *Not* every Q has to be a likert.

Read the following recent Businessweek article:

Coke's big fat problem.

Imagine you are in the shoes of Sandy Douglas. Now, do the following...

(i) From his 'messy reality', extract a relevant and pressing R.O. (stated clearly in words).

(ii) Map that R.O. onto 'information requirements' (see session 2 slides) that are built around some critical constructs of interest. Give these constructs a descriptive name.

In real life, we'd use exploratory/qualitative work extensively at this stage. Assume you have done so already.

(iii) Now, further break down the construct(s) you identified above into one-dimensional aspects that can be captured using Likerts.

(iv) Define your target audience/ target segment as teenagers. Develop a questionnaire for this target audience that can be taken in under 12 minutes.

Use of SKIP logic and any other Qualtrics features is welcome.

(v) Program your questionnaire into a websurvey into Qualtrics. The survey URL (obtained upon launching) is the deliverable and should be pasted along with your group name in this google form.

(vi) The first page of your survey should be descriptive text only, meant for me and the AAs. Pls write cogently the answers to parts (i) to (iv) above in that space.

Session 3 Homework 2:

Pls find in LMS, in the folder titled 'session 3 materials', code and data for your VALS survey.

Student names have been anonymized using random strings in both the class work and homework data files.

Pls replicate class work results for the Big5 dataset either using Rstudio (line by line) or using shiny.

Complete your HW analysis on the VALS dataset, similarly.

Your HW deliverable will be a 4-slide PPT by the name of your_name_here.pptx

In the first slide, pls write your name, PGID and MKTR section.

In the second slide, answer the following Qs

Q1. What is the size of the data matrix?

Q2. What is the optimal number of factors based on the screeplot?

Q3. How much of cumulative variance in the data matrix is explained by the factor solution?

Q4. Which variables have the highest and lowest uniqueness?

In the third slide, answer the following question (preferably in tabular form).

Q5. Interpret the factors. List the variables loading onto each factor. Give each factor a descriptive name.

In the last slide, answer the following Qs.

Q6. Look at the factor scores output. List the anonymized name strings of the respondents who have the highest and lowest scores on each factor.

Q7. If you are marketing lifestyle products - say adventure tours or rock-climbing gear - who in this sample would you target? List the top 5 names.

Deadline: For both HWs, the deadline is the midnight before session 5 begins.

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

Sudhir

Session 2 Quick-Check Responses

Hi all,

I asked for typed-in responses for two quick-check Qs in session 2.

A big reason we're going with creati.st software is that we get ready soft-copy access to and record keeping of folks' in-class attendance and attentiveness.

Pls remember that since some QCs are context-sensitive, there may no "right/wrong" answers every time.

While a lot of folks have written thoughtful answers, I can't put all of them up for lack of space. I'm putting up a few selected to reflect the range and diversity of thought and perspective (and sometimes, wit) that came across.

I've put up some responses from across all 3 sections in google spreadsheet.

Click this link for the google spreadsheet.

Also, following elementary text summation procedures, I built a word count table of term frequencies - words and terms that people most commonly used - and depict them below using simple wordclouds (see below).

And yes, all on R. Only.

QC1 asked you to think up of a relevant construct structure (construct, possible sub-constructs, aspects etc) in the new age tv-viewing mini-case. The following is a wordcloud depicting the most commonly used terms (more frequent leads to larger font) below.

QC2 asked for a few key take-aways and learnings from the session. Here is its' wordcloud.

HW Notes:

The deadline for session 2 HW passed y'day midnight. Of the 209 students registered for MKTR, 209 took the first survey (BigFive Personality) but only 203 took the second VALS survey.

Wonder why. Easy marks just let go of.

Anyway, see you folks soon tomorrow in class.

Sudhir

Session 3 Introduction and Pre-read

Hi all,

In Session 3 we cover two broad topics.

For the first, we continue the "principles of survey design" part and wade into Questionnaire Design Proper.

For the second broad topic, we do Data Reduction via Factor Analysis. For this we'll need R.

Session 3 pre-read:

Reading with serial no. 4 in the course pack "Questionnaire design and development".

Be sure to read the pre-read on Questionnaire Design as it covers the basics. It'll thus help lighten my load considerably in class.

And who knows if there's a pre-reads quiz lurking somewhere in Session 3 as well...

P.S. Pls bring this article in your coursepack to class. The quiz, if it happens, will be open-book.

Session 3 Homework pre-view:

This will require an understanding of construct measurement, questionnaire design and websurvey programming. As we speak, we are trying to get your qualtrics websurvey accounts activated.

Hence, pls make sure you read the blog post on construct exposition I put up (see below this post) so that you are comfortable with how we are defining 'construct' for MKTR.

BTW, this will be group homework (one submission per group) and the deliverable will be the URL of the websurvey you developed to measure the construct of interest.

Deadline will be one week from Session 3 i.e. just before Session 5 starts. By then your groups etc should have been formed.

P.S. Pls ensure your R and Rstudio is installed and ready for Session 3.

You won't need it inside class itself but will need it to replicate Classwork examples and for homework problems, after class.

Sudhir

Session 2 Exposition - "What are constructs in MKTR?"

Hi all,

I got feedback from many of you saying the concept of "constructs" in MKTR is unclear.

I hope this blog post explaining the same, with some illustrations and explanations clarifies things.

Definition:

"Constructs are mental objects linked to behavior." (for our purposes in MKTR).

The behavior may be "expressed" or may be "latent" (i.e. yet to manifest or exist only as a propensity).

The confusion, I think, stems from the fact that sometimes we label the behaviors themselves as constructs (e.g. Cash on delivery), and sometimes the mental objects driving the behavior as a construct.

For clarity, we shall now on use "construct" only for the mental object that manifests as behavior.

Nevertheless, we maintain that under the 'right' conditions - the intersection of motive, means and opportunity - constructs tend to manifest as expressed behavior.

Some Examples:

Consider some simple examples: "Brand Preference" and "Purchase intention" are both constructs in that they are mental objects.

They may manifest as behavior and when they do they will have measurable business outcomes.

This begs the Q, how does one even start to measure a 'mental object'?

As stated in class y'day, we try to do so in two ways - by measuring the motivations behind the behavior and by measuring what we can of the components or features of the behavior itself (we'll get some greater clarity on this later part in Session 4: Qualitative research).

In class, I gave a simple example wherein I tried deconstructing "Purchase Intention" into constituent one-dimensional aspects (see below).

Of course, I pulled out the 2 aspects out of thin-air as it was merely for demonstration purposes. In real life, given the context-sensitive nature of constructs, they are broken down only after extensive exploratory/qualitative work has been undertaken.

Similarly, complex concepts such as "consumer attitude" (towards, say tobacco/ cosmetics/ iProducts etc) or "brand image" are also constructs in that they are mental objects and can be linked to expressed behavior under the right conditions.

Relationship with "Consumer Needs":

For convenience, one can think of constructs as an intermediary step between consumer need and consumer behavior.

For instance, "Purchase intention" is not really a 'consumer need' but merely the means to satisfying one. And "Purchase intention" is a mental object that is not yet expressed behavior.

Hence, it is an easy and clear case of being a "construct".

Take "brand image". What needs does this construct serve?

It depends on the product category etc but one can, on average, think of needs such as mitigation of (quality) risk, search costs, routine/intertia, need to belong/conformity, aspirational reach etc that might be in play.

Two Broad Measurement Approaches:

One is the conventional way we went about in class - We take a behavior pattern of interest (either expressed or latent) and postulate the existence of constructs that are linked to the behavior.

We then break-down this constructs into Likert-able aspects and collect data on the same.

After data collection, we can confirm whether the aspects indeed add up to the constructs as we had hypothesized (e.g., via confirmatory factor analysis, correspondence analysis or structural equation modeling, etc.)

Some of these analyses I mentioned we'll see later in the course.

The above is the approach taken in this complex example of real research on brand advocacy I presented in class (see slide below):

The second approach, which we will see in session 3 - involves collecting bits and pieces of known behavior patterns and then exploring whether there exists a "common", correlation-based inter-relationship among the behaviors (or tendencies) of interest. If yes, then perhaps some construct(s) underlie(s) them.

Class example with the quick-check:

I'm yet to go over the responses to the first quick-check (see slide below) but let me first pin my thoughts down about it.

Thought it best to tabulate my thoughts. Pls click on the image below for a larger view.

Again, I'm not claiming my musings are "right" as opposed to "wrong" etc. Its just a single, simple case of applying analytic thinking and frameworks we picked up in session 2 to a real world, real-time Business problem.

And for this course to be relevant, we have to keep darting between real-life problems - both offline and web-based. I continue to hope for newer insights and perspectives from you folks, who are closer to the consumer-tech revolution as it is happening than an ancient like moi.

Well, that's it for now. Pls do share your thoughts and comments (and even more welcomingly, your own examples, links and ideas) in the comments section below.

Sudhir

Session 2 Updates

Hi all,

Session 2 got done today. It was a loaded one - particularly the introduction to what we mean by constructs in the MKTR context.

We ventured into psychometric scaling and attempted to measure complex constructs using the Likert scale, among others. We also embarked on a common-sensical approach to survey design.

One issue with a lot of MKTR is that it is context-sensitive, so its hard to proclaim 'right' answers that will hold true in general. "It depends" is usually a better bet. Wherever possible I do try to point out general principles and frameworks but in many cases, the problem context decides whether something is true or not.

Well, I will put up a blog post on the subject of "Constructs in MKTR" in the next couple of days. Watch this space.

There were some issues also with the creat.st software but overall, I thought things went by fairly smoothly.

The plan is to share both representative and outlying answers from all sections up here on the blog for enhanced perspective.

I'm also working on a cloud based version of R's software that can allow folks to load data and run R directly off the browser without worrying about the backend code etc. Let's see how that goes.

The homeworks for session 2 are up (see post below).

One other things: Pls form MKTR project groups of upto *5* people each. Previously, I'd said 'exactly 5' people.

Sudhir

Session 2 Homework

Hi all,

For your Session 2 homework, pls fillup the following two surveys.

Big 5 Personality Factors Survey

Values and Life Styles Survey

The surveys may each take upto 20 minutes of your time. There are no 'right' or 'wrong' answers, its just each person's own subjective perception.

Timely and complete submissions alone carry points.

Deadline: is Sunday (07-sept) midnight.

Any queries or concerns, contact me.

Sudhir

Group Formation Instructions

Hi all,

Looking at the number of registrations we have, pls form MKTR project groups of upto *5* people each. Previously, I'd said 'exactly 5' people.

Across section-groups are fine.

Appoint a 'group rep' to manage all group communication with the AAs and me.

Pls select as group name any good quality brand you are aware of. Less common names would be preferred. In case two groups choose the same name, the 'first come first served' rule applies.

Pls find below a google form. The group rep can fill it up giving details of group name and group members' names and PGIDs.

Google form to fill up group details

Fill the form only *once* per group.

Group formation Deadline: By the end of session 4. After that we'll allocate the remaining folks into groups.

After group formation, we'll release the groups list and you can check and confirm that there are no discrpancies.

Any queries etc, contact the AAs directly.

Sudhir

Session 1 Homework

Hi all,

Homework for session 1:

Pls watch this ~ 20 minute video carefully. It features Scott McDonald of Condé Nast holding fort on where MKTR is headed.

“Social Technological and Economic forces affecting Marketing Research over the next decade”

Now, for your HW, pls answer a few simple Qs (True-False, fill in the blanks variety) about the above talk in the following survey:

Questions for Session 1 Homework.

HW Notes:

(i) This is an individual-only HW. Since it involves no R, consulting peers is not permitted.

(ii) I found that using earphones works great in making out what the speaker is saying much more clearly than ordinary speakers. FYI.

(iii) Deadline: The HW should be completed and submitted latest by midnight 8-September.

Any Qs etc, pls feel free to email me or use the comments section below.

Sudhir Voleti

Welcome Message and Session 1 Updates

Hi Co2015,

The list of final registrants is out. Welcome to MKTR.

The first session got done on Tuesday. We covered some subject preliminaries and the crucial task of problem formulation. I've put up the session slides on LMS in PDF form.

About this blog:

This is an informal blog that concerns itself solely with MKTR affairs. It's informal in the sense that its not part of the LMS system and the language used here is more on the casual side. Otherwise, its a relevant place to visit if you're taking MKTR. Pls expect MKTR course related announcements, R code, Q&A, feedback etc. on here.

Since last year, I've made this blog the single-point contact for all relevant MKTR course related info. LMS will only be used for file transfers and email notifications to the class only in emergencies. Each session's blog-post will be updated with later news coming at the top of the post. Kindly bookmark this blog and visit regularly for updates.

About Session 2:

Session 2 deals with psychographic scaling techniques and delves into the intricacies of defining and measuring "constructs" - complex mental patterns that manifest as patterns of behavior. This session also sets the stage for questionnaire design to make an entry in Session 3.

There will be a series of 2 homework assignments for session 2. These concern merely filling up surveys that I will send you (this data we will use later in the course for perceptual mapping and segmentation). Nothing too troublesome, as you can see.

Pre-reads for Session 2:

Pls read the HBS note "Marketing Research" (serial no. 3 in the coursepack) as pre-read for Session 2.

Installing R and RStudio.

The .exe files for R and Rstudio are already placed in LMS in a separate folder. Download them.

Install R first by clicking on its .exe and following instructions.

Next install RStudio by clicking on it and following instructions.

We'll need RStudio up and running by session 3, when we'll have our first brush with R in the classroom.

Any Qs etc, pls feel free to email me or use the comments section below.

Sudhir Voleti