Session 11: Realistic Estimation Cases & Data Cases

Here we teach candidates how to solve demand-driven estimation cases from the supply-side, when the demand approach is too difficult to use, and vice versa, as well as estimations for enclosed spaces, for small spaces, multiple equations etc.

An excellent example of a realistic estimation case is asking a candidate to estimate the dollar value of vodka consumed in Moscow’s hippest nightclub on a Saturday in summer. 99% of candidates would tackle this from the side of the population of people in Moscow, population going to nightclubs and/or the population in the bar. 95% of them will arrive at the wrong answer.

The accurate answer has nothing to do with the population. These are some of the logic rules we teach to simplify math, estimation, market calculation etc. cases.

This is one of the most useful techniques to learn.

Reading and interpreting exhibits is a major problem for candidates. We begin a very intense effort to teach effective data analyses and hypotheses development from session 11. Candidates typically find this to be one of the most enjoyable parts of the sessions.

In the session descriptions which follow, we are using one description for 4 different candidates. Yet candidates do not perform the same, and while the descriptions are mostly accurate, there will be some differences as a few cases are brought forward, others moved back or candidates fail to prepare adequately. While these differences are minor, they sometimes occur.

Cases questions taught in the session:

Felix’s cases recorded in the session; Brainstorm how the US can reduce its oil dependency, Estimate the number of Georgina Chapman gowns worn at the Oscars & BCG Healthcare sector data interpretation.

Rafik’s cases recorded in the session; Brainstorm how Google can reduce server expenditure, Estimate the number of candidates interviewed for a Harvard MBA/year & Bain Telco sector data interpretation.

Samantha’s cases recorded in the session; Brainstorm how the rise of smartphones will impact mobile companies like Verizon, Estimate the number of police officers in NYC & BCG Aviation travel sector data interpretation.

Sanjeev’s cases recorded in the session; Estimate how many power plants will be built next year, Brainstorm how a company in the US can reduce it taxes & BMW wants to enter the Laos market.