Evaluating Phase III - prelim thoughts

OK, just started this vast exercise. Slow going so far. Some quick prelim observations -

1. Some groups haven't met even the minimum thresholds. Have seen at least 1 that did no factor and cluster analysis. Inexcusable when the thing was set in very much a clear cut and structured manner.

2. The minimum thresholds weren't met by some groups who did factor and cluster analysis - in that they factor-analyzed psychographics but left out Q21 - car attrib preferences. Some groups do factor and cluster analyses but in parallel with no connection between the 2 procs.

3. HUGE confusion and is application of fairly well-defined technical terms. Some examples: Referring to factor analysis results as 'segments' (They're not!), some group saying "respondents loaded onto the factors well...", some segmenting prior to factor analysis - makes it hard to understand what is being conveyed, or indeed if there is a point at all being conveyed.

4. Some groups have used basic descriptive analyses of primary data well indeed. Lots to see in basic analysis, basically. But the more involved analyses, such as cross-tabbing demographics, psychogr, reach info, preferences etc should have happened at the chosen target segment level.

Don't get me wrong, the sometimes extensive basic analysis I saw has merit, but contributes less to the recomms than does the same exercise carried out at the chosen recommended customer segment level.

5. Few groups so far have bothered to estimate target popn size --> hence have said nothing about relative segment sizes --> recomms are general stuff w/o focused analysis backing.

6. Some solutions so neat as to be contrived - e.g. a perfect clustering along 6 clusters with zero overlap when an additional demogr variable is introduced as a clustering variable.

7. A project objectives slide was asked for, marked mandatory. More often than not, this has been missed out.

8. Q1 went off OK more often than not. Seems, structured guidance paid off. Demand estimates have come out well. log(sales) against log(price) and other Xs easily trumps the Rsquare tables. Most groups have also bothered to show how the Xs were selected, alternative specs considered etc. Good!

From what I gather, this may well be your last brush in your MBA with model building and demand estimation.This learning should serve most of you well into your work lives.

9. Q1 aslo saw issues though - lots of confusion could also be seen, particularly in the interpretation of Q1 results. Partial regression coeffs were interpreted more as total regression coeffs. Relative importance of the variables (standardized coeffs magnitude) was often ignored.

OK. These are essentially prelim observations based on only the first few ones I saw. More later.

Ciao

Sudhir