Project presentations learnings summary.

Class,

As I write this, am done with all group presentations. Would rather pen down learnings early on before memory and other priorities take their toll.

First off, given the time and other constraints (you had less than a week between dataset-access and deliverable presentation), the output was by and large commendable. Some quick general observations:



1. Research Objective (R.O.) matters.
Recall from lectures 1, 2 & 3 my repeated exhortations that "A clear cut R.O. that starts with an action verb defined over a crisp actionable object sets the agenda for all that follows". Well that wasn't all blah-blah blah. Its effects are measurable, as I came to see.

Suppose the entire group was on board with and agreed upon a single, well-defined R.O., then planning, delegation and recombining different modules into a whole would have been much simplified. Coherence matters much in a project this complex and with coordination issues of the kind you must've faced. It was likely to visibly impact the quality of the outcome, and IMHO, it did.

2. Two broad approaches emerged - Asset First and Customer First.
One, where you define your research objective (R.O.) as "Identify the most attractive asset class." and the other, "Identify the most attractive customer segment." The two R.O.s lead to 2 very different downstream paths.

Most groups preferred the first (asset first) route. Here, the game was to ID the most attractive asset classes using size, monetary value as addressable market or some such criterion and then filter in only those respondents who showed some interest in the selected asset classes. Then characterize the indirect respondent segments obtained and build recommendations on that basis.

I was trying to nudge people towards the second, "Customer segmentation first" route partly because it aligns much more closely with the core Marketing STP (Segmentation-Targeting-Positioning) way. In this approach, the entire respondent base is first segmented along psychographic- behavioral - motivational or demographic bases, then different segments are evaluated for attractiveness based on some criterion - monetary value, count or share etc, and then the most attractive segments are profiled/analyzed for asset class preferences and investments.

Am happy to say that in a majority of the groups, once a group implicitly chose a particular R.O., the approach that followed was logically consistent with the R.O.

3. Some novel, surprising things.
Just reeling off a few quick ones that do come to my mind.

One, how do you select the "most attractive" segment or asset class given a set of options? Some groups went with a simple count criterion - count the # of respondents corresponding to that cluster and pick the largest one. Some groups went further and used a value criterion - multiply the count with (%savings times average income times % asset class allocation) to arrive at a rupee figure. This latter approach is more rigorous and objective, IMHO. There were only 2 groups that went even further in their choice of a attractiveness criterion - the customer lifetime value (CLV) criterion. They multiplied the rupee value per annum per respondent with a (cleaned up) "years to retirement" variable to obtain the revenue stream value of a respondent over his/her pre-retirement lifetime. Post-retirement, people become net consumers and not net savers, so post-retirement is a clean break from pre-retirement. I thought this last approach was simply brilliant. Wow. And even within the two groups that did use this idea, one went further and normalized cluster lifetime earnings by cluster size giving a crisp comparison benchmark.

Two, how to select the basis variables for a good clustering solution? Regardless of which approach you took, a good segmenting solution in which clusters are clear, distinct, sizeable and actionable would be required. One clear thing that emerged across multiple groups was that using only the Q27 psychographics and the Demographics wasn't yielding a good clustering solution. The very first few runs (<1 minute each on JMP and I'm told several minutes on MEXL) should have signaled that things were off with this approach. Adding more variables would have been key. Typically, groups adding savings motivation variables, Q7 constant sum etc were able to see a better clustering solution. There is seldom any ideal clustering solution and that's a valuable learning when dealing with real data (unlike the made-up data of classroom examples).

One group that stood out in the second point approach used all 113 variables in the dataset in a factor analysis -> got some 43 odd factors -> labeled and IDed them -> then selectively chose 40 from among the 43 as a segmenting basis and obtained a neat clustering solution. The reason this approach stands out in my mind 'brute force approach' is that there's no place for subjective judgment, no chance that some correlations among disparate variables will have been overlooked etc. It's also risky as such attempts are fraught with multi-collinearity and inference issues. Anyway, it seemed to have worked.

Three, when entry into real estate financial products, a regional break up that plays upon the geographic variation captured in the primary data would have been a good thing from the XYZ point of view. Would help them prioritize and better allocate resources in managerial decision making etc.

Anyway, like I have repeatedly mentioned - effort often correlates positively with learning. So I'm hoping your effort on this project did translate into enduring learning regarding research design, data work, modeling,  project planning, delegation and coordination among other things.

I wish you all the best in your future endeavors.

Goodbye and Goodluck.

Sudhir

P.S.
Would appreciate constructive feedback that may help the MKTR course next year. Many thanks to the kindred souls in Section D for the 'Thank You' card, I thought that was sweet.