Opening, Analyzing, and Closing Strategies for a Winning Interview (Part 4 of 12) | caseinterview
Summary
TLDR本视频脚本通过一个案例面试的模拟场景,向观众展示了如何进行有效的问题分析和解决方案的提出。首先,强调了在面对问题时,要基于已有信息提出假设,并选择一个框架的分支深入探索。通过标准问题收集初始数据,如果数据支持假设则继续深入,否则重新评估并调整方向。视频中提到,如果遇到死胡同,需要返回并尝试其他分支。此外,还强调了在分析过程中要不断细化和明确假设,并且大声说出自己的思考过程,以便于面试官提供帮助。最后,讲解了如何通过综合分析得出结论,并以清晰的逻辑结构向客户提出建议。整个脚本通过生动的例子和实用的技巧,教导观众如何在咨询或商业分析中有效地解决问题。
Takeaways
- 📈 **提出假设**:在开始分析案例时,先提出一个假设,然后通过收集数据来验证这个假设是否正确。
- 🔍 **深入挖掘**:如果数据支持你的假设,继续深入挖掘;如果假设错误,回到框架的上一层并改变方向。
- 🔄 **不断精炼假设**:在分析过程中,根据收集到的信息不断调整和精炼你的假设。
- 🗣️ **大声思考**:在分析时大声思考,这有助于清晰地表达你的思路,并且如果遇到困难,面试官可能会提供帮助。
- 📊 **数据驱动**:始终基于数据来做决策,确保你的分析和建议都有数据支持。
- 📉 **问题分类**:区分问题是公司特有的还是整个行业普遍存在的,这将影响你的解决方案。
- ⏳ **趋势分析**:寻找数据的趋势线,了解问题随时间的变化情况。
- 📈 **细分数据**:将总数分解为不同的部分或细分市场,以便更准确地识别问题所在。
- ❓ **明确提问**:在请求数据时,先解释为什么需要这些数据,这样可以显示出你对问题的深入理解。
- 🏁 **结论先行**:在提出解决方案时,先给出结论,然后提供支持结论的数据和分析。
- 🔧 **结构化呈现**:使用结构化的方式呈现你的发现和建议,如使用金字塔原理来组织你的沟通。
Q & A
在案例面试中,如何从一张白纸开始构建分析框架?
-在案例面试中,首先需要获取足够的信息。然后,以数据为基础,提出假设,选择框架的一个分支开始深入分析。通过标准问题收集初始数据,如果数据支持你的假设,则继续深入;如果假设错误,则返回并探索框架的其他分支。
如何通过数据来验证你的假设?
-通过询问关键问题来收集数据,这些数据将支持或反驳你的假设。例如,如果你的假设是收入下降导致了盈利问题,那么你需要询问有关收入变化的数据来验证这一点。
如果遇到死胡同,应该如何调整分析方向?
-如果数据不支持你的假设,或者你发现当前的分析路径无法解决问题,应该返回到框架的上一层,然后转向另一个分支继续分析。这可能需要重新审视问题,或者从不同的角度提出新的假设。
如何确保你的分析过程是系统的?
-通过持续细化和明确你的假设,并大声说出你的思考过程。这样做可以帮助你清晰地表达你的思考路径,并且如果遇到错误,面试官也能够及时提供帮助。
在分析案例时,应该如何利用数学知识?
-利用数学知识来理解各种变量之间的关系,例如利润、成本、收入和单位销售量。通过数学计算,可以清晰地展示这些变量是如何影响最终结果的,从而帮助你更准确地识别问题所在。
在案例分析中,为什么需要区分公司特定问题和行业普遍问题?
-区分公司特定问题和行业普遍问题对于制定解决方案至关重要。如果是公司特定问题,可能需要调整公司策略或内部流程;而如果是行业普遍问题,则可能需要考虑市场趋势、竞争环境等宏观因素。
如何通过趋势分析来理解案例中的数据变化?
-通过比较不同时间点的数据,可以观察到数据的变化趋势。这有助于识别问题是否是近期发生的,还是长期存在的,从而为制定解决方案提供依据。
为什么在分析案例时需要对数据进行分段处理?
-分段处理数据可以帮助你识别哪些特定的业务领域或市场细分出现了问题。通过分析不同区域、渠道或产品线的数据,可以更精确地定位问题,并制定针对性的解决策略。
在案例面试结束时,如何有效地总结你的发现?
-在案例面试结束时,首先要明确你的主要发现或“大洞察”。然后,以结论的形式提出你的主要观点,并提供支持这一结论的关键数据点。最后,给出基于这些分析的明确行动建议。
如何确保你的案例分析在逻辑上是连贯的?
-使用结构化的沟通方法,如金字塔原理,首先提出结论,然后提供支持结论的逻辑相关数据点。确保你的分析和建议是清晰、有序且易于理解的。
在案例分析中,为什么说‘洞察力’是一个重要的品质?
-‘洞察力’意味着能够识别出不明显但准确的信息或模式。在咨询行业,能够提供深刻的洞察力是极为宝贵的,因为它可以帮助客户看到问题的本质,并找到有效的解决方案。
在进行案例分析时,为什么要注意书写和呈现的清晰度?
-在案例面试中,清晰的书写和呈现可以帮助面试官更好地理解你的分析过程和结论。如果使用白板或纸板,需要确保字迹清晰、版面布局合理,避免因字迹不清或空间不足而影响信息的传达。
Outlines
😀 案例面试的策略与技巧
本段落介绍了案例面试的过程,强调了从空白纸张到拥有数据信息的转变,以及如何通过提问标准问题、深入探究假设、根据数据调整方向来解决问题。提出了在数据支持假设时深入挖掘,在假设错误时返回并转向其他分支的策略。通过实例展示了如何通过数据验证假设,如收入下降导致盈利问题,以及如何通过大声说出假设变化来清晰思考。
🔍 分析案例的结构与方法
详细讨论了如何深入分析案例,包括如何通过数学计算来验证假设,例如通过检查收入和成本的变化来确定利润下降的原因。强调了在遇到死胡同时如何返回并尝试其他途径,以及如何通过具体而非开放式的问题来收集信息。还提到了练习写作和在不同表面上书写的重要性,以及如何通过实践来提高解决案例的能力。
📝 案例分析的技巧与建议
提供了分析案例时的技巧,包括大声思考、使用假设、请求更多数据、确定问题是否特定于公司或整个行业、寻找趋势线、分割数字等。强调了在咨询中提问的正确性以及解释为什么需要数据的重要性。
📊 结束案例的结构与方法
描述了结束案例的三步过程:首先是确定重要的洞见,然后是提出结论和建议,最后是用数据支持观点。强调了结论的重要性,并提出了如何通过数据支持结论的方法。还介绍了如何使用合成(synthesis)的方法将分析的信息整合成一个连贯的整体,使客户能够理解。
🏁 案例结束的逻辑与示例
讨论了如何有效地结束一个案例,包括如何清晰地提出结论和行动建议,以及如何使用逻辑上相关联的数据来支持结论。通过比较不同质量的案例结束方式,展示了如何使案例结束更加客户友好和易于理解。
👂 案例沟通的风格与效率
通过个人经历和比喻,说明了在咨询中沟通应该直接和高效,与日常对话中讲故事的方式不同。强调了在咨询中,客户更倾向于直接了当、结构化的沟通方式,而不是冗长和杂乱无章的叙述。
Mindmap
Keywords
💡案例面试
💡假设
💡数据
💡深入挖掘
💡死胡同
💡精炼假设
💡框架
💡趋势分析
💡细分
💡综合
💡结论
Highlights
在案例面试中,从空白纸张到利用数据和潜在图表进行分析的转变
提出标准问题,根据数据深入探究假设的正确性
如果假设错误,需要回到框架的上层并改变方向
通过大声思考来清晰表达你的假设是如何变化的
在框架内确定从哪里开始,选择一个分支并识别关键问题
如果遇到死胡同,需要回到框架上层并尝试其他分支
在实际咨询中,信息收集是昂贵的,需要高效利用每一天
避免提出过于开放式的问题,而应更具体地请求信息
通过数学上完整的利润和损失案例来练习分析技能
如果遇到死胡同,要能够视觉上表示并口头上解释你的行动
在分析案例时,要思考问题是否是公司特定还是行业普遍问题
寻找趋势线,了解公司过去几年的表现
对数字进行细分,找出驱动单位出货量的各个部分
总是要求数据,并解释为什么你需要这些数据
在案例结束时,确定重要的洞察点并形成结论
以结论开始,然后是支持结论的两到三个关键点
使用《金字塔原理》来结构化你的沟通,使其逻辑严谨
练习在不同媒介上书写,如白板、纸垫和翻转图
在咨询中,清晰和有逻辑的沟通比讲述整个故事更重要
Transcripts
you have enough information the the the
case and the case interview starts to
feel more like an HPS case you have
information so now it's like you're the
protagonist what would you do you know
you actually have data so you go from
sort of blank piece of paper to
information potential charts data
okay so you ask the standard
question I think I demonstrated that
earlier next you go deeper down the
branch if the data suggest you could if
your hypothesis is correct you keep
drilling down all right if your
hypothesis is wrong you go back up okay
so an example of that is my hypothesis
is that revenues a decline in revenues
is causing the profitability problem do
we have any data that would suggest that
to be do we have any data on whether
revenues have changed okay the
interviewer might say yes revenues have
in fact gone down H my hypothesis is so
far has been confirmed I will go deeper
in that direction if the interviewer
says actually revenues have gone up
that's interesting how can profits go
down when revenues have gone up oh it
must mean that it's a cost problem I'm
going to go up the framework I'll draw
this out in a second so you can see it
I'm going to go up the framework and
move over to cost okay and the last step
which I demonstrated is you want to
continuously refine your hypothesis and
and it's actually a good habit to um say
out loud how your hypothesis is changing
and so I didn't ever use the word
hypothesis I sort of just did it
implicitly but uh but they could sort of
tell ah you know that's kind of odd that
revenues would sort of go up when
profits are down must be a cost problem
so it's better if you say that out loud
in general it's it's good to think out
loud and literally like oh that's kind
of odd you know I literally I'll
literally say that um because it's
easier if you just say what's in your
mind then you don't have to sort of
think whether you should sort of censor
it or
not
okay so you guys all get that okay ask
for information on where to start within
the framework State a hypothesis pick a
branch of the framework to start
identify key issues within the branch
ask standard questions to gather initial
data which is again very formulaic then
go deeper down the branch if the data
suggests you should if you run into a
dead end which is very common sometimes
they'll do it deliberately they'll
deliberately send you down a dead end to
see if you figure out it's dead end you
got to come back up the framework and
move over somewhere else okay and then
continually refine your hypothesis and
state what information you need to test
whe that hypothesis is correct okay
that's important because that's what you
do in real life you know so our
hypothesis is that sales have gone down
in United States because sales in the
Midwest region have really tanked but
all the other regions are fine okay
interesting idea how do we go figure
that out oh we got to go do a data dump
to like the guy in finance and go figure
that out okay that's worth half a day um
so we're trying to simulate that
here we you you don't want to do this is
very linguistic but it's important what
you don't want to do is just ask really
open-ended questions like do we have any
information on the business
situation right then it's like no no no
no no right you gota be more specific
than that okay so you tell them what
your hypothesis is and then you tell
them what specific piece of information
you need to determine whether hypothesis
is true or not because that's what you
would do in real life because gathering
information in real life and Consulting
is expensive it's counted in days and
there are only so many days you have
available to get problem solved so they
want to see that you can see if you can
do that in a case
situation
oops thank
you okay so let me illustrate what going
deeper
means okay going back to my earlier
example you don't have to copy the top
part you can just it's just more of the
visual diagram you sort of I want to
illustrate um if the example was profits
are down 20% uh we're looking at
revenues and we're looking at costs do
we know if revenues have gone up down or
stay the same okay and the interviewer
says uh revenues in fact have
uh have
decreased oh interesting
okay that mean if I so we had earlier we
had sort of
profit cost right so up until now in the
case I've drawn that right and now that
I have data that says revenues in fact
have gone down interesting that means
it's likely to be a revenue problem I'll
say how much have revenues gone down
by okay they've gone down in fact
they've gone down by 20% oh precisely
the same amount as profits have gone
down seems reasonable that
mathematically it seems like this is
really a revenue problem a revenue
decline problem not really a
profitability problem so to further
understand why revenues have gone down
by 20% we need to look at the component
parts of
Revenue and I'll say there's two things
I want to know next I want to break
apart revenues and look at number of
units sold times the average revenue per
unit which is basically like price so
price times volume sold ass your total
revenues do we have any information on
whether the number of units has sold
have changed or not have they gone up
down or stayed the same
okay
and so the inter ofview might then say
um actually number of units sold have
not changed we sold a million units last
year we sold million units this year
interesting H interesting um so
obviously then I wouldn't say obviously
because that's a little bit of a snobby
word but okay so revenues have gone so
profits have sort of you know declined
by 20% and revenues have declined by
20% okay unit sold have not changed okay
and it must mean that prices have gone
down by 20% do we have any information
on whether that's true or not yes in
fact prices are down by 20. okay great
so the real problem here is not why the
profits down is not the revenues of
decline it's that we're for some reason
prices have declined by 20% in this
particular situation and then you you
just keep drilling down right just so
the reason I like sort of profit and
loss cases at least for practice early
on is there sort of the most
mathematically complete it's either or
it's very very clean so a very good way
to sort of practice this go down a tree
come back up a tree uh the other ones
are a little they're a little more
they're squishier um way it's not quite
mathematically clean a lot more overlap
so I like practicing them in terms of
the actual analytical skills and you'll
just keep drilling down further and
further and further
okay so you can see as you go through
this process you the case starts looking
more like an HBS case you're having data
and you get a sense of what's going on
um and you sort of get closer and closer
to that okay so what happens though if
you run into a dead end so I want to
show you what a dead looks like and what
it sounds like and what you visually
want to
do
okay so let's go back to the original
case um profits are down 20% uh we need
to look at revenues or costs have
revenues or costs change actually have
um uh do we know if um do we have any
information or give any suggestions on
as to where to start the says no let's
look at revenues first my hypothesis is
revenues have declined that's why profit
has dropped by 20% do we have any
information on whether or not profits
have revenues have changed um in fact
revenues have actually increased by 20%
that's interesting revenues have
increase by 20% yet profits have decline
by 20% must not be a revenue problem
okay so let's focus on cost
next this means costs have probably gone
up quite
significantly so to understand and is
that true yes in fact costs have
actually gone up by 30 or 40%
okay so we're looking at a cost problem
so we need to actually understand what's
causing what's driving the cost problem
there are two components to cost okay
number of units
sold and the cost per
unit so mathematically you multiply the
two
together and that gets you the cost
right do we have any information as to
whether or not number of units uh sold
has changed in this particular situation
okay
uh yes we have information on that in
fact number of units sold has stay the
same interesting okay so if costs have
declined by minus 40% unit sold has not
changed then it must mean the cost per
unit has gone gone I'm sorry costs have
gone up by 40% unit sold hasn't changed
that must mean that cost per unit has
gone up 40% is that is that true in fact
it is and then you just keep drilling
down so you see the
brand back up the hierarchy go down the
other hierarchy so I like to think of it
as sort of um roots in a tree right you
sort of go down one you come back up you
go down the other
one so so visually you want to sort of
convey that and actually honestly it's
worth practicing
penmanship you know because you're do in
a case interview and I tend not to write
very cleanly so I actually have to slow
down and practice writing and if you're
doing it on a whiteboard you have to
write bigger letters which is a I mean
it seems really silly but it's a
different mechanical skill you know and
I don't I never really written on a
chalkboard so it's just you know and
then you draw if you draw too big then
you run out of space I so like a lot of
very practical things it's worth
practicing on a flip chart worth
practicing on a paper pad um and and
then sometimes a whiteboard is sort of
easy too thing with a whiteboard is you
want to make sure you you're comfortable
with the pant you don't like stick
yourself so you have like ink on your
shirt it's not
good it's all about details
question
because I have a bad poor mathematical
example yeah so these aren't all meant
to sort of be tied out but then you're
right so mathematically you will be
correct you're going to do
well okay so here's some tips for
analyzing
cases other thing too just to back up a
second you'll find that when um
interviewers give cases they either give
cases that they actually have live
experience with hard data in their head
all they'll make it up and so for this
example I was I'm just making stuff
up more to illustrative
Point uh okay um here are some tips
think out
loud it's
useful um if you're struggling you
really don't know what to do but if you
think out loud and you sort of thinking
out loud too long I know you're sort of
stuck I mean they don't they don't want
to be in a room with an awkward
situation either right so they'll help
you you know they might sort of you know
deduct you like two points for style or
whatever um but if you think out loud
I'm stuck that they'll actually help
you um use hypothesis a lot like I just
demonstrated earlier I think it's this
let me get data to verify and and sort
of move
on and you don't the parts underneath
it's it's about taking educated guest
figuring out what data you need to sort
of figure out whether you're not right
or wrong and then validate and then
refine that hypothesis ask for more data
see that hypothesis is correct and you
just keep on
moving okay uh there there are a couple
of types of analyses you want to do
fairly regularly and and I'll mention
this a couple of times um but a lot of
people sort of if they're not
experienced with it they sort of forget
to do this so this is this happens
literally 90% of cases I do
this the first is um what I call
figuring out if the problem is a company
specific issue or an industrywide issue
so like on the the earlier example if
units sold has declined by 20% the next
question I might ask do we have any
information on how the rest of the
Market's doing so is it a 20% decline
for us because we' screwed something up
or is it 20% across the board it's a
market issue you solve those two
problems very differently you respond to
them very differently so that's a very
common analysis I'll ask for data on
company specific or across the
board
uh second thing I always ask for is uh I
try to find the trend line so where are
we this year where was it last year
sometimes was the year before I'm
looking for the chain I'm sorry looking
for the the
trend and often times you'll find that
um the most common Trend you'll see is
around
growth that's the one that I sort of saw
the most you you'll often times have uh
company will be in sort of four
different businesses one will be going
like Gang Busters and one will be sort
of like dying on average they're doing
fine right but on averages the average
is always sort of and the totals always
sort of lie they're misleading you have
to break things up into its parts
because then you might say okay do you
want to solve the problem with the
company that's that's sort of flailing
or do you want to sort of take the one
that's working make it better that would
be a conversation you might have in a
case other things to do um always
segment your
numbers okay and I have specific
segmentation strategies to I'll show you
um but if if revenues are down for
example you know it's a revenue problem
and units and units sold are have
declined let's say um you want to sort
of break up that total so total units
sold have declined what composes units
sold right um and do we have any
information on the sources of where the
units sold and whether those have
changed in fact we do you know units
sold in in North America has stayed
constant Asia has gone up 20% Europe's
gone down 20 20% I see okay so unit sold
has gone down by 20% in Europe do we
know that's a company specific issue or
an is wi issue how are the competitors
doing on volume in terms of Europe uh in
fact Europe the European competitors are
also down 20% in volume ah I see so we
have an industry problem a market
problem in Europe which is suppressing
European sales which is dragging down
overall sales overall units sold for the
company and I'll sort of synthesize that
um but you don't you don't get to that
Insight right until you sort of break
apart the numbers and there like like
there's an infinite number of ways to
sort of break apart numbers in in real
life um I usually like to ask the
interviewer so you you say we need to
break apart the numbers do we have any
more detail we have any more do we have
any more details on what what comprises
you know unit
sold um because on unit sold it could be
by channel right internet channel is up
20% direct Salesforce is down 15% it
could be by region it could be by
product line the super duper product is
up sales are up 20% but the basic
product is down 20%
um and so you could literally sort of do
that all day long and in real life you
would because you don't know um but they
don't want to waste time doing that so
you sort of you make the case that you
need to break it apart and then you ask
them there's any information and they
usually give it to you because they
don't want to like make up numbers
either because then you get you get
embarrassed by you know people who are
really sharp on their numbers right um
okay segment your numbers and then I
mentioned ear always ask for
data okay always
ask and again the key with asking is
make explain why you want the data first
you get credit for asking the right
question which is very important in
Consulting and then when they give you
the data then you kind of proceed if you
ask for the data without asking
explaining why you want it you don't get
points for asking the right question
okay so you got to use the words like we
need to sort of break apart we need to
look at the segments that Drive Unit
shipments do we have any data on the
individual segments okay so that's a
good word to use because segments can
mean segmentation a lot of different
patterns right by region by Channel by
type of customer but you say the word
segments like okay guy the person knows
what they're talking about they're going
to segment stuff I'll save them some
time we should segment by region because
that's where the inside is great um so
if you just use the word segments or
break it apart or in those words they
will give you data on how to do that
I'll give you the actual segmentation
pattern that's most
productive all
right uh mechanically speaking here's
how to close a case and then we'll get
into some actual
cases it's really a three-step
process as you're sort of gathering all
this information you have these
hypotheses you're sort of driving down
various analyses your case in the
interview starts looking a lot more like
an HBS case Okay and then towards the
end of the case interview it's more like
a cold call what would you do if you're
the protagonist in this situation or
what would you do what would you tell
the client to do um the one the thing
you need to work on here first off is
just figure out what's
important I'll give you a demo of that
in a second what's the important idea
what's the big aha what's the big
Insight um by the way one of the biggest
compliments you can sort of pay to a
colleague in Consulting is really
insightful you know that was really
insightful which is basically not
obvious but
spoton so figure out what's
important and that's sort of an internal
process then you want to actually say it
out allowed and give a point of view a
conclusion profitably a conclusion with
a recommendation on an action the client
should take or likely actions the client
might want to consider I'm hedging again
right if I don't have enough data to
know definitively they should do that
I'll say it would seem to me that you
know doing a pilot program of some sort
in the new Emerging Market would be
useful given it's going 50% per year
when the Market's only growing 2%
okay and then then you want to support
your point of view with
data and so um at least I don't know
what the other firms call it but we call
the synthesis taking all this
information and like building up
something one thing what's the one
answer that we're looking for and I also
sort of Googled the definition of
synthesis uh it means to combine
separate elements to form a coherent
hole which basically means taking all
the Lego blocks on the floor building a
house ah it's a house right I understand
what to do
now so analysis is all about pulling
apart the pieces into its component
parts synthesis is then all this
analysis putting it back together into a
coherent hole so a client can make sense
of
it it's it's it's the opposite of
analysis it's
interesting um let's
see so by the way I'm I'm recording this
event I'm going to refer to like page
numbers and slide numbers just what's on
the recording um you guys will get
access to all this after the fact too so
if you see me sort of referring to page
numbers that don't exist you you'll know
why um the common structure by the way
of a close just visually
speaking it sort of looks like this and
you guys want be already familiar with
it you don't actually sort of draw it
out
necessarily but your um
you always start with the con well not
always but in most cases you start with
a
conclusion ABC companies should consider
exting the European market how come uh
market sales have tanked cost structure
of the business is too high and so you
have your your sort of supporting
elements underneath your conclusion okay
so there's an order to it you start with
the conclusion first a lot of people
start with the data first and I'll show
you what that sounds there's there's a
certain Rhythm if you listen to It's
almost almost like music there's a
certain Rhythm to how a poor clothes
goes versus a good cloth versus a great
cloes always conclusion first and then
the key supporting elements underneath
usually it's like two or three things
there's a useful book um that a lot of
people swear by I actually never read it
but I think I've learned it sort of
through osmosis it's called the P
principle have you guys heard of that
okay uh there's this book called The
pyram principle it's by bar Mento I'll
put up the name in a in a second and um
it's the it's the communication it's
it's I think it's a book about critical
thinking logical thinking and writing um
sort of every McKenzie consultant sort
of uses that whether they realize it or
not I I use it without realizing it
others have done it more deliberately
and a lot of the consulting firms use it
and it has to do with structuring your
Communication in a very rigorously
logical way uh and it's it's a good
skill to have um and it's different it's
a little different um in some cases
depending on how you were sort of taught
to write so I got a lot out of that um
but that's the structure it's sort of
big idea up front and then three main
ideas underneath that sort of support
that uh almost look like an expository
writing essay from high school if you're
familiar with
that okay I want to give you um examples
of
closes and I want you just hear what it
sounds
like and I'm going to I'm going to take
my crack at using sort of a a business
example because I think it sort of helps
convey the
point okay
um the the rhythm of a poor close is
like this data data data data data data
data data data part of a conclusion data
data another part of a conclusion data
data dat dat another part of the
conclusion in its entirety all the
information is there it's hard to follow
not client friendly
okay
um a good close would be conclusion
three relevant pieces of data that
directly support that
conclusion and a great close would be a
conclusion with a definitive action
recommendation right and three pieces of
data that are logically related to that
conclusion so the logic is really very
clear and very
compelling and I'll give you example
that umide this hypothetical
situation I know okay still it um but I
can read it this way because that's it's
too small to print and you won't be able
to see
it um I have this hypothetical situation
where I got two I have two daughters and
I'm sort of envisioning this situation
sort of in the future uh and sort of
trying to make it interesting so to make
the point um my youngest one comes to me
and says daddy daddy daddy like what
honey what honey um I'm sorry it was an
accident but my my sister made me do it
we weren't trying to it was an accident
not my fault her fault
okay I know candles no bad idea matches
I know but she pushed me she did really
right oh I I'm coughing a lot help what
do I what should I do okay that's a poor
close because you have no idea what the
how is going on right a lot of
information no conclusion no action
right
okay
um the next one my my my my older
daughter comes to me and
says dad the house is on fire we were
playing with matches let's get the hell
out of here okay that's like a
conclusion action driven
right and then uh the the babysitter
comes down who's trying to train to be a
Management Consultant and says Mr
Tren you have to read this um the house
is on fire it's in fact burning to the
ground quickly and it cannot be saved
you have no other choice than to get the
heck out of here right now okay good
recommendation action conclusion
there are three reasons why I feel this
way let me show you my PowerPoint
presentation okay slide one please the
fire will consume the house in less than
1 minute this is based on the fact that
it's moving at 10t every 5 seconds and
I've measured the width of the house to
be 120 ft we got less than 60 seconds to
live number two supporting Point putting
out the fire is in fact not possible
okay the fire is too big at this point
to put out plus the fire extinguisher is
at the opposite end of the house and
guess what Mr CH I've been watching you
worked out you're not as quite as fast
as you used to be the treadmill you
won't make it great third supporting
Point third slide please uh your only
remaining option is to save you and your
kids now fact the supporting point is
you promise your wife you take care of
the kids if you leave them in a burning
house and go put out the fire she will
kill you therefore conclusion is you
have no other choice than to get the
heck out of here and that's like the
rhythm of a good clothes and you can
obviously tweak and make it better um
but the idea is sort of main idea with a
clear action why you feel that way and
then restate the the
conclusion but you see the difference
right and and there's interesting I mean
I have relatives who were who who who
think this way on the poor
clothes and actually my
mother-in-law and it's just
like I I I my limit's like seven minutes
I can listen to seven minutes of well
first I went to the parking lot and then
I went in and then this happened oh and
there was this really interesting lady I
talked to oh did you realize that she
has a DA and just like
and what what's the point mom uh oh we
need more toilet paper oh okay I can
take care of that right and but she had
to sort of tell the whole story um and
so not that it's right or wrong it's
different but in Consulting that doesn't
work because it's not client friendly
you can't follow
it
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