Class,
Just reviewed the first 10 presentations. I must impressive, given the time crunch and other constraints you folks were operating under.
The remaining groups which are to present, here's some updates for you.
Am told there are some evening events groups do not want to miss. At the same time, I would like this thing over for sections A and B today. Hence, this is what I propose:
If at least half a group's members can be present, then they are welcome to make their project presentation at any convenient time slot of thier choice between 2 and 9.30 pm today in my office or in the AC2 conference room (where I'm trying to arrange for a projector and computer).
The same shall go for sections C and D tomorrow as well.
Sudhir
Wednesday, November 10, 2010
Tuesday, November 9, 2010
Phase III: The way forward
Class,
The last of the traditional lectures for the last of the sections are done. The only thing remaining is the course project phase III. Some guidelines on what to expect and do:
1. In each section, the order in which groups will present will be randomly drawn. About 5, at most 6 group presentations can be accommodated in lec 10 class time itself. The other groups shall be given time slots to present between 7.30 and 9.30 pm at the AC2 LT/ MLT or NMLT on the same day as their lecture 10.
2. The time between the start of 2 consecutive presentations should be no more than 20 minutes. Ideally, budget 15 minutes for the presentation and about 3-4 minutes for Q&A. Short clarification Questions can be asked anytime during the presentation but the more long-winded questions should come only at the end. Each time will be shown a 2 minute warning sign before their 15 minutes are up.
3. The format in which the submission files are named and the content format is explained briefly in this blog post here. Kindly adhere to the same as far as possible. Use of slide animations, highlighting, multimedia etc to better make a point are welcome.
4. Only the PPTs that have been submitted prior to the blackboard dropbox deadline can be used for the presentations. We will bring the PPTs and load them a priori onto the Classroom computer. No laptop from your side would be required for the presentation.
5. Kindly ensure your entire group is present for the presentation and is reasonably up to date on and on-board with broad aspects of the project.
6. Some basic common-sensical ground rules - (i) Kindly refrain from entering or leaving the classroom when a presentation is in progress. It is needlessly distracting. Kindly use the interval between two presentations to exit/enter. (ii) Kindly feel free to ask questions from a group but no more than 1 question from 1 student/group to a presenting group.
7. Please sign and hand over the peer evaluation form to your section AAs anytime between Wednesday to Friday.
8. The evlauation criteria for Phase III include, broadly -
(A) The quality and clarity of the methodology
(B) Logical flow and consistency of Analysis
(C) Clarity of communication
(D) Handling of Q&A
(E) Quality of recommendations
Pls feel free to use whatever software works for you - MEXL, SAS, SPSS whatever.
Hope that clarifies.
Sudhir
The last of the traditional lectures for the last of the sections are done. The only thing remaining is the course project phase III. Some guidelines on what to expect and do:
1. In each section, the order in which groups will present will be randomly drawn. About 5, at most 6 group presentations can be accommodated in lec 10 class time itself. The other groups shall be given time slots to present between 7.30 and 9.30 pm at the AC2 LT/ MLT or NMLT on the same day as their lecture 10.
2. The time between the start of 2 consecutive presentations should be no more than 20 minutes. Ideally, budget 15 minutes for the presentation and about 3-4 minutes for Q&A. Short clarification Questions can be asked anytime during the presentation but the more long-winded questions should come only at the end. Each time will be shown a 2 minute warning sign before their 15 minutes are up.
3. The format in which the submission files are named and the content format is explained briefly in this blog post here. Kindly adhere to the same as far as possible. Use of slide animations, highlighting, multimedia etc to better make a point are welcome.
4. Only the PPTs that have been submitted prior to the blackboard dropbox deadline can be used for the presentations. We will bring the PPTs and load them a priori onto the Classroom computer. No laptop from your side would be required for the presentation.
5. Kindly ensure your entire group is present for the presentation and is reasonably up to date on and on-board with broad aspects of the project.
6. Some basic common-sensical ground rules - (i) Kindly refrain from entering or leaving the classroom when a presentation is in progress. It is needlessly distracting. Kindly use the interval between two presentations to exit/enter. (ii) Kindly feel free to ask questions from a group but no more than 1 question from 1 student/group to a presenting group.
7. Please sign and hand over the peer evaluation form to your section AAs anytime between Wednesday to Friday.
8. The evlauation criteria for Phase III include, broadly -
(A) The quality and clarity of the methodology
(B) Logical flow and consistency of Analysis
(C) Clarity of communication
(D) Handling of Q&A
(E) Quality of recommendations
Pls feel free to use whatever software works for you - MEXL, SAS, SPSS whatever.
Hope that clarifies.
Sudhir
Monday, November 8, 2010
Project related travails
Hi All,
I've been meeting groups all afternoon and evening today and some things have come up which IMHOmerit wider dissemination:
1. Have some basic roadmap in mind before you start: This is important else you risk getting lost in the data and all the analyses that are now possible. There are literally millions of ways in which a dataset that size can be sliced and diced. Groups that had no broad, big-picture idea of where they want to go with the analysis inevitably run into problems.
Now don't get me wrong, this is not to pre-judge or straitjacket your perspective or anything - the initial plan you have in mind doesn't restrict your options. It can and should be changed and improvised as the analysis proceeds.
Update: OK. Some may ask - can we get a more specific example? Here is what I had in mind when I was thinking broad, basic plan from an example I outlined in the comments section to a post below:
2. Segmentation is the key: The Project essentially, at its core, boils down to an STP or Segmentation-Targeting-Positioning exercise. And it is the Segmentation part which is crucial to getting the TP parts right. What inputs to have for the segmentation part, what clustering bases to use, how many clusters to get out via k-means, how best to characterize those clusters and how to decide which among them is best/most attractive are, IMHO, the real tricky questions in the project.
3. Kindly ask around for JMP gyan: A good number of folk I have met seemed to have basic confusion regarding factor and cluster analyses and how to run these on JMP. This after I thought I'd done a good job going step-by-step over the procedure in class and interpreting the results. Kindly ask around for clarifications etc on the JMP implementation of these procedures. The textbook contains good overviews about the conceptual aspects of these methods.
I'm hopeful that at least a few folk in each group have a handle on these critical procedures - factor and cluster. I shall, for completeness sake, again go through them quickly tomorrow in class.
4. The 80-20 rule applies very much so in data cleaning: Chances are under 20% of the columns in the dataset will yield over 80% of its usable information content. So don't waste time cleaning data (i.e. removing missing values, nonsense answers etc) from all the columns, just the important ones only. Again, you need to have some basic plan in mind before you can ID the important columns.
Also, not all data cleaning need mean dropping rows. In some instances, missing values can perhaps be safely imputed using column means or medians or the mode (depending on data type).
Chalo, enough for now. More as updates occur.
Sudhir
I've been meeting groups all afternoon and evening today and some things have come up which IMHOmerit wider dissemination:
1. Have some basic roadmap in mind before you start: This is important else you risk getting lost in the data and all the analyses that are now possible. There are literally millions of ways in which a dataset that size can be sliced and diced. Groups that had no broad, big-picture idea of where they want to go with the analysis inevitably run into problems.
Now don't get me wrong, this is not to pre-judge or straitjacket your perspective or anything - the initial plan you have in mind doesn't restrict your options. It can and should be changed and improvised as the analysis proceeds.
Update: OK. Some may ask - can we get a more specific example? Here is what I had in mind when I was thinking broad, basic plan from an example I outlined in the comments section to a post below:
E.g. - First we clean data out for missing values in Qs 7,10,27 etc -> then do factor analysis on psychogr and demogr -> then did cluster analysis on the factors -> then we estimate segment sizes thus obtained -> then we look up supply side options -> arrive at recommendations.
Hope that clarifies.
2. Segmentation is the key: The Project essentially, at its core, boils down to an STP or Segmentation-Targeting-Positioning exercise. And it is the Segmentation part which is crucial to getting the TP parts right. What inputs to have for the segmentation part, what clustering bases to use, how many clusters to get out via k-means, how best to characterize those clusters and how to decide which among them is best/most attractive are, IMHO, the real tricky questions in the project.
3. Kindly ask around for JMP gyan: A good number of folk I have met seemed to have basic confusion regarding factor and cluster analyses and how to run these on JMP. This after I thought I'd done a good job going step-by-step over the procedure in class and interpreting the results. Kindly ask around for clarifications etc on the JMP implementation of these procedures. The textbook contains good overviews about the conceptual aspects of these methods.
I'm hopeful that at least a few folk in each group have a handle on these critical procedures - factor and cluster. I shall, for completeness sake, again go through them quickly tomorrow in class.
4. The 80-20 rule applies very much so in data cleaning: Chances are under 20% of the columns in the dataset will yield over 80% of its usable information content. So don't waste time cleaning data (i.e. removing missing values, nonsense answers etc) from all the columns, just the important ones only. Again, you need to have some basic plan in mind before you can ID the important columns.
Also, not all data cleaning need mean dropping rows. In some instances, missing values can perhaps be safely imputed using column means or medians or the mode (depending on data type).
Chalo, enough for now. More as updates occur.
Sudhir
Some project related Qs and clarifications
Folks,
Based on queries and comments received in class today re the project, let me try to quickly putup stuff that IMHO mertits wider dissemination.
1. Let me rush to clarify that no great detail is expected in the supply side Q.
As in, you're not expected to say - "XYZ should offer an FD [fixed deposit] with a two year minimum lock-in offering 8.75% p.a.".
No.
Saying "XYZ should offer FDs in its product portfolio." is sufficient.
2. Make the assumption that the sample adequately represents the target population - of young, urban, upwardly mobile professionals.
3. Yes, data cleaning is a long, messy process. But it is worthwhile since once it's done, the rest of the analyses follow through very easily indeed, in seconds.
4. It helps to start with some idea or set of conjectures about a set of product classes and a set of potential target segments in mind, perhaps. One can then use statistical analyses to either confirm or disprove particular hypotheses about them.
5. There is no 'right or wrong' approach to the problem. There is however a logical, coherent and data-driven approach to making actionable recommendations versus one that is not. I'll be looking for logical errors, coherency issues, unsustainable assumptions and the like in your journey to the recommendations you make in phase III.
Hope that clarifies.
Sudhir
Based on queries and comments received in class today re the project, let me try to quickly putup stuff that IMHO mertits wider dissemination.
1. Let me rush to clarify that no great detail is expected in the supply side Q.
As in, you're not expected to say - "XYZ should offer an FD [fixed deposit] with a two year minimum lock-in offering 8.75% p.a.".
No.
Saying "XYZ should offer FDs in its product portfolio." is sufficient.
2. Make the assumption that the sample adequately represents the target population - of young, urban, upwardly mobile professionals.
3. Yes, data cleaning is a long, messy process. But it is worthwhile since once it's done, the rest of the analyses follow through very easily indeed, in seconds.
4. It helps to start with some idea or set of conjectures about a set of product classes and a set of potential target segments in mind, perhaps. One can then use statistical analyses to either confirm or disprove particular hypotheses about them.
5. There is no 'right or wrong' approach to the problem. There is however a logical, coherent and data-driven approach to making actionable recommendations versus one that is not. I'll be looking for logical errors, coherency issues, unsustainable assumptions and the like in your journey to the recommendations you make in phase III.
Hope that clarifies.
Sudhir
Sunday, November 7, 2010
Phase III deliverables
Class,
Your deliverable consists of a 15 + 3 minute presentation of a 30 slide PPT (excluding appendices).
Deadline for dropbox submission of PPT is morning 0600 hrs on 10-Nov (Wednesday).
You are expected to answer two questions, primarily:
1. What products/Services should XYZ offer?
2. What customer segment should be targetted with (1)?
'Why' is a corollary to both questions above. Be prepared to make and argue your case effectively using evidence from primary and/or secondary data.
More specifically, your PPT should contain:
1. Filename (mandatory) - should be groupname_section.pptx when submitted into the dropbox on blackboard.
2. Title slide (mandatory) - project title, Group Name, member names and UIDs & MKTR section.
3. Presentation Outline (Optional) - a Contents page that outlines your presentation along sections (e.g. methodology, Q1, Q2, recommendations, etc).
4. A Methodology Section (Mandatory) - In preferably a graph or flowchart form, lay down what analysis procedures you used in what order to answer Q2 to reach the recommendations based on Q2.
5. A Data section (Mandatory) - explains that nature and structure of the data that were used. Be very brief but very informative - write (ii) the dimensions of the data matrices used as input to in different procedures, and (ii) the sources of data - cited sources if secondary data are used, and Question numbers in the survey questionnaire if primary data are used.
I strongly suggest using a tabular format here. Packs a lot of info into compact space. Easy to read and compare too.
6. Model Expressions (Mandatory)- Write the conceptual and/or mathematical expressions of any models used. Kindly place in the appendix section a descriptives table of the input data, a brief explanation of the X variables used, and of course, output tables along with interpretation.
7. Appendix Section (Optional): Some of the less important tables can be plugged into a separate appendix section (outside the 30 slide limit) in case you are running out of slide space. Have only the most important results tables in the main portion.
8. Recommendations (Mandatory) - crisp, clear, in simple words directed towards the client. Emphasize the usability and actionability of the recommendations.
Hope that clarifies.
Sudhir
Your deliverable consists of a 15 + 3 minute presentation of a 30 slide PPT (excluding appendices).
Deadline for dropbox submission of PPT is morning 0600 hrs on 10-Nov (Wednesday).
You are expected to answer two questions, primarily:
1. What products/Services should XYZ offer?
2. What customer segment should be targetted with (1)?
'Why' is a corollary to both questions above. Be prepared to make and argue your case effectively using evidence from primary and/or secondary data.
More specifically, your PPT should contain:
1. Filename (mandatory) - should be groupname_section.pptx when submitted into the dropbox on blackboard.
2. Title slide (mandatory) - project title, Group Name, member names and UIDs & MKTR section.
3. Presentation Outline (Optional) - a Contents page that outlines your presentation along sections (e.g. methodology, Q1, Q2, recommendations, etc).
4. A Methodology Section (Mandatory) - In preferably a graph or flowchart form, lay down what analysis procedures you used in what order to answer Q2 to reach the recommendations based on Q2.
5. A Data section (Mandatory) - explains that nature and structure of the data that were used. Be very brief but very informative - write (ii) the dimensions of the data matrices used as input to in different procedures, and (ii) the sources of data - cited sources if secondary data are used, and Question numbers in the survey questionnaire if primary data are used.
I strongly suggest using a tabular format here. Packs a lot of info into compact space. Easy to read and compare too.
6. Model Expressions (Mandatory)- Write the conceptual and/or mathematical expressions of any models used. Kindly place in the appendix section a descriptives table of the input data, a brief explanation of the X variables used, and of course, output tables along with interpretation.
7. Appendix Section (Optional): Some of the less important tables can be plugged into a separate appendix section (outside the 30 slide limit) in case you are running out of slide space. Have only the most important results tables in the main portion.
8. Recommendations (Mandatory) - crisp, clear, in simple words directed towards the client. Emphasize the usability and actionability of the recommendations.
Hope that clarifies.
Sudhir
Tying in Course learnings with phase III
Hi all,
Update: It seems to me that Q33 - media habits w.r.t. TV channels has been lost to us. There's *NO* variation in the responses anywhere in Q33 suggesting that the Q didn't work as planned. The ranking question template in qualtrics was pointlessly complex and involved some drag and drop operation that took me a while to figure. Respondents, exhausted at the fag end of the survey, can hardly be expected to get it right.
Well, now that we are dealing with REAL data, some of our learnings will be from such unfortunate events, I guess. Basically, we have lost all usable info on the media habits question. Will likely have to rely only on psychographics mostly to design a marketing communications message for the target segment now, I guess.
Continuing my last post:
We broke our research problem down into two parts - one relating to the supply side and the other, to the demand side. The supply side essentially asks what set of products the firm should offer and the demand side complements it by asking what customers want in financial/retirement planning products. The demand and supply sides are complementary and simultaneous and must be solved iteratively to arrive at a coherent, consistent answer.
Now that we have in some sense mapped the possible set of supply side options to a manageable set of target customer segments on the demand side, the Q arises, what specific analysis methods and techniques might come in use. Pls understand there's plenty of leeway here and many different ways to peel an apple. What I'll talk about here is merely 1 possible way among many alternatives.
Clearly, Factor and Cluster analyses can easily be brought into play - to reduce the # of variables in the psychographics for instance and to explore 'natural groupings' among customers on the basis of demographics (life-cycle stage.age or employment or family size etc), psychographics (risk-return appetite), asset class preference, something else altogether or some combination of all of these. Just remember Kotler gyan about what segments should be like - measurable, actionable, reachable, distinct and all that.
What about the ANOVA and regression stuff we learned? Specific hypotheses you have - such as e.g. "People in their 20s and early 30s are markedly more risk averse than those in their late 30s and 40s", or "there is systematic association between employment type and preference for fixed income/annuities post retirement" that feed into your storyline can and should be inferred using statistical analysis.
OK, what about discrete choice models (like the Logit) that we will do in lec 9? Well, turns out that just like ANOVA and multivariate regression use the 'Analyze > Fit Model' sequence in JMP, so does Logit as well. Hallelujah. Just input a categorical Y into the Y area and see, the 'Standard least squares' method type at the top-right of the screen changes to 'logistic analysis' automatically. Logistic analyses can be used to infer whether particular categorical Ys relate systematically to some set of Xs in a statistically significant way.
For instance, I could have a hypothesis saying "Public sector bank as primary bank choice relates systematically to age (older people perefer this), employment type (govt employees prefer this) and low risk-low return seekers" or something like that.
Well, applying techniques learned in class would be good and should be done wherever opportunity arises. Using methods learned outside of class is very welcome too, just be prepared for Qs on them.
The primary learning in the project comes from phase III, IMHO. It teaches us how to do phase I better based on the mistakes made in phase II questionnaire as reflected in the actual data collected. The biggest learning IMHO is a resetting of our own pre-conceived notions of what analyzing real data is like, of what can and cannot be reliably inferred from the data we do have and so on.
Well, that's it from me.
P.S.
Am prepping lec 9. Hope is to cover Discrete choice and a bit of MDS (Multidimensional scaling) under perceptual maps. Now am unable to locate good MDS tools in JMP and might have to fallback on R for this. Now, I see little scope for MDS usage in phase III, so a demo on R shouldn't be too troublesome, hopefully, IMHO.
Sudhir
Update: It seems to me that Q33 - media habits w.r.t. TV channels has been lost to us. There's *NO* variation in the responses anywhere in Q33 suggesting that the Q didn't work as planned. The ranking question template in qualtrics was pointlessly complex and involved some drag and drop operation that took me a while to figure. Respondents, exhausted at the fag end of the survey, can hardly be expected to get it right.
Well, now that we are dealing with REAL data, some of our learnings will be from such unfortunate events, I guess. Basically, we have lost all usable info on the media habits question. Will likely have to rely only on psychographics mostly to design a marketing communications message for the target segment now, I guess.
Continuing my last post:
We broke our research problem down into two parts - one relating to the supply side and the other, to the demand side. The supply side essentially asks what set of products the firm should offer and the demand side complements it by asking what customers want in financial/retirement planning products. The demand and supply sides are complementary and simultaneous and must be solved iteratively to arrive at a coherent, consistent answer.
Now that we have in some sense mapped the possible set of supply side options to a manageable set of target customer segments on the demand side, the Q arises, what specific analysis methods and techniques might come in use. Pls understand there's plenty of leeway here and many different ways to peel an apple. What I'll talk about here is merely 1 possible way among many alternatives.
Clearly, Factor and Cluster analyses can easily be brought into play - to reduce the # of variables in the psychographics for instance and to explore 'natural groupings' among customers on the basis of demographics (life-cycle stage.age or employment or family size etc), psychographics (risk-return appetite), asset class preference, something else altogether or some combination of all of these. Just remember Kotler gyan about what segments should be like - measurable, actionable, reachable, distinct and all that.
What about the ANOVA and regression stuff we learned? Specific hypotheses you have - such as e.g. "People in their 20s and early 30s are markedly more risk averse than those in their late 30s and 40s", or "there is systematic association between employment type and preference for fixed income/annuities post retirement" that feed into your storyline can and should be inferred using statistical analysis.
OK, what about discrete choice models (like the Logit) that we will do in lec 9? Well, turns out that just like ANOVA and multivariate regression use the 'Analyze > Fit Model' sequence in JMP, so does Logit as well. Hallelujah. Just input a categorical Y into the Y area and see, the 'Standard least squares' method type at the top-right of the screen changes to 'logistic analysis' automatically. Logistic analyses can be used to infer whether particular categorical Ys relate systematically to some set of Xs in a statistically significant way.
For instance, I could have a hypothesis saying "Public sector bank as primary bank choice relates systematically to age (older people perefer this), employment type (govt employees prefer this) and low risk-low return seekers" or something like that.
Well, applying techniques learned in class would be good and should be done wherever opportunity arises. Using methods learned outside of class is very welcome too, just be prepared for Qs on them.
The primary learning in the project comes from phase III, IMHO. It teaches us how to do phase I better based on the mistakes made in phase II questionnaire as reflected in the actual data collected. The biggest learning IMHO is a resetting of our own pre-conceived notions of what analyzing real data is like, of what can and cannot be reliably inferred from the data we do have and so on.
Well, that's it from me.
P.S.
Am prepping lec 9. Hope is to cover Discrete choice and a bit of MDS (Multidimensional scaling) under perceptual maps. Now am unable to locate good MDS tools in JMP and might have to fallback on R for this. Now, I see little scope for MDS usage in phase III, so a demo on R shouldn't be too troublesome, hopefully, IMHO.
Sudhir
Saturday, November 6, 2010
Some specific phase III gyan
Hi all,
Last year's experience tells me it's better to provide some (optional) guidance and structure than none at all, given the time and other constraints we are under. So here goes:
First, if you haven't already, then kindly first read these posts for context and continuation: General gyan on the project scope and background and Some phase I guidance.
There are broadly three research questions I can see relating (i) to a market entry decision, and subsequently (ii) to an STP or Segmentation-Targeting-Positioning decision for client firm XYZ. The first, or the zeroth question rather, is a preliminary one that must be acknowledged and gotten out of the way, first thing.
0. Is there a market out there at all?
1. If yes, what are the characteristics of this market?
2. How best to reach the attractive customer segments?
The zeroth research question implicitly asks if there exists an addressable, sizeable, profitable, sustainable market in financial services and/or products given the target population (the young, upwardly mobile, urban middle class).
Now, one may say, "well, look, there are so many firms and products in this space. Ergo, there must be a good, profitable, actionable market out there. No?" Well, likely but not necessarily. The last 2 years in particular teach us that in bubble scenarios, the herd is grossly wrong. We'll have to take this call for ourselves by ourselves. In this case though, I doubt you'll have much trouble making a case for entry versus non-entry. Hint: Broadly glance at what kind of numbers we are talking about here, at the macro-level - the equivalent of a full one-third of India's GDP, from the household sector alone, needs to find a home in some combination of asset classes, every year. Would be nice if (but is by no means mandatory that) your phase III analysis incorporate some thought, rationale and recommendation regarding the entry or non-entry for XYZ in the target space.
Now consider research Qs 1 and 2. Those simple looking research questions are actually quite involved. Q1 has two sides to it - the supply side characteristics of the market and those of the demand side. Let me label these sub-questions 1.1. and 1.2. thus:
1.1. (Supply side) What combination of products/services/solutions in what asset classes should XYZ consider offering?
1.2. (Demand side) What is the distribution of target population preferences over asset classes and over the attributes of financial products/services/solutions?
For the supply side, ask if there exist synergies in introducing a set of products rather than just one - in terms of competencies required, talent/skills available, scale, marketing, etc. Imaginative use and application of Secondary and Primary data would be great here. For instance, primary data can at least provide directly the (stated) distribution of current and prospective investments into broadly defined asset classes. Again, this is entirely my idea of how I would think if I were in your seat. It nowhere says I am 100% right in what I am thinking. Hence, you don't have to follow my lead here and can go with some radical, innovative and totally out-of-the-box approach of your own.
For the demand side, primary data in some measure allows us to collect data on target population preferences over financial product attributes (broadly speaking) such as risk/return, time to maturity, tax saving status, benefits sought, flexibility or liquidity at short notice, etc. This rich dataset also provides opportunities for Segmentation of the customer base, along various dimensions of interest (entirely your call what you want to go with). I would rather you try to go with what can be actionable from the client firm's viewpoint.
Once some sort of segmentation base has been decided upon, segmentation done and the segments identified, labeled, sized up and analyzed, then comes Q2 with a targetting and positioning plan for the most attractive segments identified. We've asked some basic Qs about media habits and psychographics etc to help with a rudimentary marketing plan. Essentially, you are able to go one step ahead and tell the client not just what are the most attractive customer segments in the market for so-and-so asset classes, but also how to reach them, communicate with them and sell them the product.
Now the logical Q arises, how did all that we do in class come in handy here? Aha. Good Q. I shall putup a separate post explaining and connecting some of the techniques we covered with some of the research Qs above.
OK, that is it from me for this part. I'd ideally you mull over this with your group. Again, you don't have to go with this plan outline as above but kindly ensure that you bring value to client XYZ.
I'd be happy to get some feedback, comments, queries and clarifications on Phase III thus far. Pls use the comments section below should any such arise.
Some of you may have already started playing the data. Please do. You'll find the practical difficulties associated with collating, filtering, sorting out, analyzing and interpreting real data. That, IMVHO is tremendous learning for the individual, the team and the class as a whole.
Sudhir
Last year's experience tells me it's better to provide some (optional) guidance and structure than none at all, given the time and other constraints we are under. So here goes:
First, if you haven't already, then kindly first read these posts for context and continuation: General gyan on the project scope and background and Some phase I guidance.
There are broadly three research questions I can see relating (i) to a market entry decision, and subsequently (ii) to an STP or Segmentation-Targeting-Positioning decision for client firm XYZ. The first, or the zeroth question rather, is a preliminary one that must be acknowledged and gotten out of the way, first thing.
0. Is there a market out there at all?
1. If yes, what are the characteristics of this market?
2. How best to reach the attractive customer segments?
The zeroth research question implicitly asks if there exists an addressable, sizeable, profitable, sustainable market in financial services and/or products given the target population (the young, upwardly mobile, urban middle class).
Now, one may say, "well, look, there are so many firms and products in this space. Ergo, there must be a good, profitable, actionable market out there. No?" Well, likely but not necessarily. The last 2 years in particular teach us that in bubble scenarios, the herd is grossly wrong. We'll have to take this call for ourselves by ourselves. In this case though, I doubt you'll have much trouble making a case for entry versus non-entry. Hint: Broadly glance at what kind of numbers we are talking about here, at the macro-level - the equivalent of a full one-third of India's GDP, from the household sector alone, needs to find a home in some combination of asset classes, every year. Would be nice if (but is by no means mandatory that) your phase III analysis incorporate some thought, rationale and recommendation regarding the entry or non-entry for XYZ in the target space.
Now consider research Qs 1 and 2. Those simple looking research questions are actually quite involved. Q1 has two sides to it - the supply side characteristics of the market and those of the demand side. Let me label these sub-questions 1.1. and 1.2. thus:
1.1. (Supply side) What combination of products/services/solutions in what asset classes should XYZ consider offering?
1.2. (Demand side) What is the distribution of target population preferences over asset classes and over the attributes of financial products/services/solutions?
For the supply side, ask if there exist synergies in introducing a set of products rather than just one - in terms of competencies required, talent/skills available, scale, marketing, etc. Imaginative use and application of Secondary and Primary data would be great here. For instance, primary data can at least provide directly the (stated) distribution of current and prospective investments into broadly defined asset classes. Again, this is entirely my idea of how I would think if I were in your seat. It nowhere says I am 100% right in what I am thinking. Hence, you don't have to follow my lead here and can go with some radical, innovative and totally out-of-the-box approach of your own.
For the demand side, primary data in some measure allows us to collect data on target population preferences over financial product attributes (broadly speaking) such as risk/return, time to maturity, tax saving status, benefits sought, flexibility or liquidity at short notice, etc. This rich dataset also provides opportunities for Segmentation of the customer base, along various dimensions of interest (entirely your call what you want to go with). I would rather you try to go with what can be actionable from the client firm's viewpoint.
Once some sort of segmentation base has been decided upon, segmentation done and the segments identified, labeled, sized up and analyzed, then comes Q2 with a targetting and positioning plan for the most attractive segments identified. We've asked some basic Qs about media habits and psychographics etc to help with a rudimentary marketing plan. Essentially, you are able to go one step ahead and tell the client not just what are the most attractive customer segments in the market for so-and-so asset classes, but also how to reach them, communicate with them and sell them the product.
Now the logical Q arises, how did all that we do in class come in handy here? Aha. Good Q. I shall putup a separate post explaining and connecting some of the techniques we covered with some of the research Qs above.
OK, that is it from me for this part. I'd ideally you mull over this with your group. Again, you don't have to go with this plan outline as above but kindly ensure that you bring value to client XYZ.
I'd be happy to get some feedback, comments, queries and clarifications on Phase III thus far. Pls use the comments section below should any such arise.
Some of you may have already started playing the data. Please do. You'll find the practical difficulties associated with collating, filtering, sorting out, analyzing and interpreting real data. That, IMVHO is tremendous learning for the individual, the team and the class as a whole.
Sudhir
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