Mas638 – business analytics consulting section 2 – first case

1. MAS638 – Business Analytics Consulting
Section 2 – First case
Document type
Date
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of
McKinsey & Company is strictly prohibited
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1
Effective problem solving is at the heart of successful
consulting
... the problem is usually not well-defined
... the client has already decided they can’t do it alone
... you have very little time

2. ... but you cannot afford to make big mistakes
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Different types of problems
Application of a skill or a technique
2
‘Run a linear optimization on a truck schedule with known
costs and known routes’
MAS 631/632
Data is given, math can be conceptually difficult
Open, ill-defined problem with possibly no or multiple great
answers
‘How and how much can X save through improving the Supply
Chain?’
This course
Data is wrong, non-existent, poor quality, not consistent
Often very difficult to figure out what data is useful and how to
use it
Math usually quite easy

3. Absolute proof rarely exists
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Select, fun, real problems I worked on during my McKinsey
time
3
We are a global direct-selling company with 6 million sales reps
around the world and we are heading for bankruptcy due to
online shopping. Help us!
How can our airline improve our on-time performance?
We accidentally sold lots of GM seed corn to US farmers that is
not approved for human consumption. We lost track of it. If
Japan finds it in a couple of ships of US corn, they will refuse it
and the global corn market will grind to a halt. What should we
do?
What is the fastest airline boarding process that also has
fantastic customer satisfaction?
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The basic problem solving process
4
Highly iterative, sometimes cycling daily
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Define the problem
Structure the problem
Prioritize
Plan & conduct analysis
Synthesize & develop recommendation

5. Dunder Mifflin has hired you!
5
You have been retained by Dunder Mifflin, a distribution
company
They sell paper and many other products to other business
customers (no retail)
They have many sales reps that call on these customers
They want to make more money!
Their question to you:
Do we have a Pricing opportunity?
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Interview with the sales manager
Why do you think you have a pricing opportunity?

6. Well, we know that we don’t price all our customers or products
the same, and sales reps have a lot of freedom to set prices. And
sometimes I hear our customers say that our prices are low
compared to competitors, although frequently they also say they
are too high. We also raised prices on several hundred
customers on April 1st , although I don’t know how that worked
out.
How does pricing work?
We have ‘official’ list prices, but we negotiate a standard
discount off list price with every customer. Small customers
obviously get a smaller discount. But then each sales rep over
time adjusts the prices individually – a customer may say ‘this
item is too expensive’ or the reps may raise the price on some.
So it’s all over the place. Each branch in theory follows this
same approach and we have 30+ branches.
How do sales reps work and how do they get paid?
Each rep is assigned a set of customers. They get paid a small
percentage of the revenue of that customer, so it’s completely
variable and we can’t lose. So they really have a lot of freedom.
What are the basic numbers of the business?
We have >100,000 SKUs, the Scranton branch has 13,000
customers, 50 sales reps and sells nearly $130 million per year.
We’d consider Scranton pretty representative, so let’s look there
first.
6

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What is the problem definition?
7
Basic question or problem:
Criteria for success:
What is in scope, what is not:
Decision makers:
Other stakeholders and involvement:
Constraints:
Perspective & context:

8. As precise as possible without accidentally constraining
A clear definition of what defines success, e.g., ‘>$500m in 3
years’
Primary deciders and who can affect the outcome
Who else needs to be involved and how they are thinking about
this
The overall context in which this problem occurs. E.g., trends in
the market, competitor moves, etc.
Anything that limits the solution space, e.g., ‘people neutral’,
‘don’t upset customers’
E.g., ‘Focused on US business unit only’
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Dunder Mifflin Pricing Problem Definition
8
Basic question or problem:
Can we make a sustainably higher profit by raising prices? On
what products and customers should we raise prices and by how
much?
Criteria for success:
At least $3m in additional profit

9. What is in scope, what is not:
Scranton branch
Decision makers:
Branch manager & VP of sales
Other stakeholders and involvement:
Sales reps
Constraints:
none? Any pricing guidance from corporate overall?
Perspective & context:
Reps have significant pricing freedom, pricing likely all over
the place
Raised prices on some customers in April
Some rumors that we are cheaper than competitors
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Structuring the problem – the issue tree
9
Main issue/
problem

10. Sub-issue 1
Sub-issue 2
Sub-issue 3
Sub-issue 1.1
Sub-issue 2.1
Sub-issue 1.2
Sub-issue 2.2
Sub-issue 2.3
How do we structure the overall question? What are the
different ways to raise price?
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Define
Structure
Prioritize
Analyze
Synthesizem

11. A possible Dunder Mifflin starter issue tree
10
Pricing opportunity
Raise prices universally
Selectively raise prices
Raise prices on certain customers
Make specific sales reps price better
Other ideas
Some decent guesses, but this is stuff the client told us already.
We need some facts and insights
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Define
Structure
Prioritize
Analyze

12. Synthesizem
What data would you ask the client for?
11
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Two fundamentally different approaches to data
12
Approach 1: directly ask for data on specific issues. E.g., if this
was on logistics
Can you give me data on deliveries per customer?
Can you give me your route schedule?
What is the typical wait time per customer delivery?
...
Approach 2: ask for raw data that you can use for those and
many others
The customer master – names, IDs, addresses
The delivery transactions – customer, date & time, truck #

13. If you don’t derive stuff from the master data:
You might get the wrong information and you can’t check
If you have more ideas you have to go back and the results may
be inconsistent
If you find something interesting you cannot directly dig down
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The Dunder Mifflin data
13
Activity data
Sales transactions for Scranton
SKU
Customer
Date
Quantity
Price per unit
List of customers we planned to increase price on April 1st
Customer
Price increase (as a factor – 1.05 is a 5% increase)
Master data

14. Sales rep assignments
Customer
Sales rep assigned
SKU master data
SKU
Vendor ID
Cost per unit
SKU type (ABC)
This is real data from a real distributor. Obviously SKUs,
Customer and Vendor IDs are disguised
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A couple days has passed, and this first stage of the analysis is
complete
We need to communicate to the client. What and how do we say
it?
14

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Synthesize and develop recommendations – Hypothesis trees
15
Main point
Supporting argument 1
Supporting argument 2
Supporting argument 3
Evidence/supporting argument
Supporting argument 2.1
Evidence/supporting argument
Supporting argument 2.2
Supporting argument 2.3
Hypothesis trees are about structuring your logic & telling the
story
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Define

16. Structure
Prioritize
Analyze
Synthesizem
Hypothesis tree - example
16
We should pay our sales force on margin instead of revenue, we
can grow profits substantially
Sales reps sell products badly because we compensate them on
revenue
Selling at higher prices would increase profitability
significantly
Sales reps would accept switching to a new compensation
system
Data analysis shows that many sales happen at near 0 margin,
meaning no profit
Raising prices by 2% on average would be worth $25m
In anonymous interviews, sales reps admit to frequently
lowering price to close deals quickly in order to get higher sales
overall
The close rate of sales reps that charge 2-3% higher prices is
not different from sales at lower prices
Customers tell us that our products are preferred over
competitors
Interviews show that reps are very open to a switch
Good reps would make more money and only a handful of reps

17. would make slightly less
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Synthesizem
Hypothesis tree – DM initial pricing analysis
17
Early indications are that DM has substantial pricing
opportunity, however more work is required
Analyzing profitability by customer shows that many small
customers are low margin, despite our intention to price them
higher

18. Similarly, some reps show much higher margin than others
However, more analysis is required
The raw data contained a lot of non-sensical entries. We
recommend to review these and adjust as needed
Some of the margin differences in customers/reps may be
caused by other external factors that we have to exclude
Most customer buy each item no more than once per year, thus
the odds that a customer even notices increases are low
Analysis of the April 1st price increase was inconclusive, but no
big negative customer reaction was seen
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Executive summary
18
Early indications are that DM has substantial pricing
opportunity, however more work is required
Analyzing profitability by customer shows that many small
customers are low margin, despite our intention to price them
higher
Similarly, some reps show much higher margin than others
Analysis of the April 1st price increase was inconclusive, but no
big negative customer reaction was seen

19. Most customer buy each item no more than once per year, thus
the odds that a customer even notices increases are low
However, more analysis is required
The raw data contained a lot of non-sensical entries. We
recommend to review these and adjust as needed
Some of the margin differences in customers/reps may be
caused by other external factors that we have to exclude
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Many small customers have low margin – analysis indicates a
gap of >$2 million
19
Margin by customer
(Percent)
Customer revenue ($)
There is little correlation between customer margin and size
A large number of small customers do not meet our minimum
target of 50% margin
If every customer under $1m revenue could be raised to at least
50%, we would be making $2.3m in additional profit
Source: Profitability analysis of DM transaction data (last 12

20. months)
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Many small customers have low margin – analysis indicates a
gap of >$2 million
20
Margin by customer
(Percent)
Customer revenue ($)
There is little correlation between customer margin and size
A large number of small customers do not meet our minimum
target of 50% GM
If every customer under $1m revenue could be raised to at least
50%, we would be making $2.3m in additional profit
Source: Profitability analysis of DM transaction data (last 12
months)
The message: what you want the readers to take away, most
important part of the slide
Chart title: what is shown on the chart
Comments & explanations
Source: where the data and analysis comes from

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Similarly, at first sight, several sales reps could raise prices
21
Margin by sales rep
(Percent)
Sales rep ID
Most sales reps sell on average with our target margin, but
several are substantially below 50%
Further work is required to understand whether this is due to
customer mix or competitive intensity in their territory or other
reasons
Source: Profitability analysis of DM transaction data (last 12
months)
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0.38925573923841789 0.53541146916645854
0.53226311780277569 0.47258051601543694

22. 0.48084285705819396 0.49389965102260985
0.41919048923985752 0.49194002104771206
0.52012371505335542 0.52472383988465976
0.49948868803583762 0.34987512286060729
0.53103595166885231 0.5322899287682481
0.51322157192258577 0.51838079858706521
0.54097352928093256 0.52562444940618158
0.51244758137044788 0.45456744449711667
0.45132925828032178 0.54291883773862282
0.54747409213343223 0.53608879330229431
0.54195694297535124 0.21853770092674035
0.51364256127278418 0.51123562428648262
0.52302879580181427 0.53103810257629203
0.53292984886133221 0.52597209399303002
0.52316875702584864 0.24455023322685115
0.35085789550844354 0.53965379115392376
0.51392826184344698 0.2974496824809193
0.49321965988600996 0.41991590971491538
0.41918009929947608 0.48103759135209456
0.54269930192495608 0.50700201552228497
0.47231067265842774 0.50126485918873187
0.4921864902682489 0.52984736909273167
0.45396295322599206 0.52006933804370159
Analysis of the April 1st price increase is inconclusive, but at
least it did not look as if customers had a violent negative
reaction
22
On April 1, DM increased prices on 788 customers by 5% on all
items

23. We manually confirmed that these price increases were indeed
implemented
Margins stayed flat while the control group grew 2 percent
Revenue increased by 52% vs 16% for Control Group
Analysis is inconclusive at this point – clearly customers
selected were non-representative in some way
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Gross margin by customer group
Q1 2019
Increased customers Other customers 0.5 0.44 Q2 2019
Increased customers Other customers 0.5 0.46
Revenue by customer group (Indexed to Q1)
Q1 2019
Increased customers Other customers 1 1 Q2 2019
Increased customers Other customers 1.52
1.1599999999999999

24. Significantly more work is required
23
Issues and potential other factors
Data: The transaction data and cost data has a number of issues
(>10% of entries) – missing cost information, frequent ‘sales at
loss’, items with multiple prices, etc.
Other correlations: customer price differences may be due to
product mix, geographic location, industry etc. Sales rep
variations may be due to customers in different geographies or
with different competitive intensities
Recommended next steps
Need to sit down with IT and Operations group to review
specific instances and agree on approach to clean
Review select ‘interesting’ customers jointly with sales leader
and identify any factors that need to be taken into consideration
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25. Recap, what have we learned?
24
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What have we learned about Consulting with Analytics?
25
Consulting/problem-solving is an end-to-end process with many
parts
The usually hard parts
What exactly is the problem?
How do I break it apart?
What analysis should I and can I do that will actually tell me
something and be defensible against critics?
How do identify and filter out the crud in the data so I am
basing the analysis on the right info
How do I use the insights to tell a convincing story?
The usually easy part
Doing the analysis once the data is clean

26. TRACKER
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Define the problem
Structure the problem
Prioritize
Plan & conduct analysis
Synthesize & develop recommendation
What have we learned about Consulting with Analytics?
26
2. You need to UNDERSTAND the data, INSPECT it and
CHECK it
What exactly does every field mean?
What do ‘weird’ entries mean in the real world? E.g., is this a
return or an error? Is this really a normal SKU? Why would we
sell something at a loss? Is there really one customer that we
lose $millions on?

27. Do sample customers act in a way that is intuitive? E.g., do they
buy a 1-5 times a week?
Do the aggregates in the data set jibe with the overall business
numbers? Is the total revenue close to the financials? Is the
number of customers approximately right? Is the gross margin
roughly right? Are the top earning SKUs the ones we would
expect? …
Macro-statistics (e.g., regressions, overall stats) are not a
substitute for understanding
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What have we learned about Consulting with Analytics?
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3. Only BIG signals matter
Real data is random and will always show trends
Small variations are usually not a signal of opportunity
Small trends usually are random or at least don’t translate to
value
4. Small BIG signals matter
Always look at the ‘units of action’, you might find one or two

28. that drive substantial value
E.g.,
Two big customers may be extremely poorly priced
Two out of the 50 sales reps are really abusing the system
Regression & other macro-analysis usually does not find the
opportunities
Never trust just the summary statistics of an analysis, always
look at plots – you are looking for the ‘That’s weird’ moment
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5. Behold the power of 80/20
Very few businesses have customers or products that are all
similar. The 80/20 rule
20% of customers make 80% of the profit
20% of SKUs are 80% of the volume
20% of transactions are 80% of the revenue
20% of customers have 80% of the wealth

29. When you remove the top 20%, the 80/20 rule still applies –
Distributions follow power laws, not normal statistics
This means you can often radically short-cut analysis by
focusing on the 20%
6. Beware the curse of 80/20
Random variations do not ‘null out’ in aggregate analysis
E.g.,
800 truly random customers out of 14000 are not necessarily
representative
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Title

30. Unit of measure
30
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MAS638 – Business Analytics Consulting
Section 2 – First case
Document type
Date
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of
McKinsey & Company is strictly prohibited

31. TRACKER
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1 Footnote
SOURCE: Source
Title
Unit of measure
1
Effective problem solving is at the heart of successful
consulting
... the problem is usually not well-defined
... the client has already decided they can’t do it alone
... you have very little time
... but you cannot afford to make big mistakes
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure

32. 1
Different types of problems
Application of a skill or a technique
2
‘Run a linear optimization on a truck schedule with known
costs and known routes’
MAS 631/632
Data is given, math can be conceptually difficult
Open, ill-defined problem with possibly no or multiple great
answers
‘How and how much can X save through improving the Supply
Chain?’
This course
Data is wrong, non-existent, poor quality, not consistent
Often very difficult to figure out what data is useful and how to
use it
Math usually quite easy
Absolute proof rarely exists
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Select, fun, real problems I worked on during my McKinsey
time
3

33. We are a global direct-selling company with 6 million sales reps
around the world and we are heading for bankruptcy due to
online shopping. Help us!
How can our airline improve our on-time performance?
We accidentally sold lots of GM seed corn to US farmers that is
not approved for human consumption. We lost track of it. If
Japan finds it in a couple of ships of US corn, they will refuse it
and the global corn market will grind to a halt. What should we
do?
What is the fastest airline boarding process that also has
fantastic customer satisfaction?
TRACKER
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Title
Unit of measure
The basic problem solving process
4
Highly iterative, sometimes cycling daily
TRACKER
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1 Footnote
SOURCE: Source

34. Title
Unit of measure
Define the problem
Structure the problem
Prioritize
Plan & conduct analysis
Synthesize & develop recommendation
Dunder Mifflin has hired you!
5
You have been retained by Dunder Mifflin, a distribution
company
They sell paper and many other products to other business
customers (no retail)
They have many sales reps that call on these customers
They want to make more money!
Their question to you:

35. Do we have a Pricing opportunity?
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Interview with the sales manager
Why do you think you have a pricing opportunity?
Well, we know that we don’t price all our customers or products
the same, and sales reps have a lot of freedom to set prices. And
sometimes I hear our customers say that our prices are low
compared to competitors, although frequently they also say they
are too high. We also raised prices on several hundred
customers on April 1st , although I don’t know how that worked
out.
How does pricing work?
We have ‘official’ list prices, but we negotiate a standard
discount off list price with every customer. Small customers
obviously get a smaller discount. But then each sales rep over
time adjusts the prices individually – a customer may say ‘this
item is too expensive’ or the reps may raise the price on some.
So it’s all over the place. Each branch in theory follows this

36. same approach and we have 30+ branches.
How do sales reps work and how do they get paid?
Each rep is assigned a set of customers. They get paid a small
percentage of the revenue of that customer, so it’s completely
variable and we can’t lose. So they really have a lot of freedom.
What are the basic numbers of the business?
We have >100,000 SKUs, the Scranton branch has 13,000
customers, 50 sales reps and sells nearly $130 million per year.
We’d consider Scranton pretty representative, so let’s look there
first.
6
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What is the problem definition?
7
Basic question or problem:
Criteria for success:

37. What is in scope, what is not:
Decision makers:
Other stakeholders and involvement:
Constraints:
Perspective & context:
As precise as possible without accidentally constraining
A clear definition of what defines success, e.g., ‘>$500m in 3
years’
Primary deciders and who can affect the outcome
Who else needs to be involved and how they are thinking about
this
The overall context in which this problem occurs. E.g., trends in
the market, competitor moves, etc.
Anything that limits the solution space, e.g., ‘people neutral’,
‘don’t upset customers’
E.g., ‘Focused on US business unit only’

38. TRACKER
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1 Footnote
SOURCE: Source
Title
Unit of measure
Dunder Mifflin Pricing Problem Definition
8
Basic question or problem:
Can we make a sustainably higher profit by raising prices? On
what products and customers should we raise prices and by how
much?
Criteria for success:
At least $3m in additional profit
What is in scope, what is not:
Scranton branch
Decision makers:
Branch manager & VP of sales
Other stakeholders and involvement:
Sales reps
Constraints:
none? Any pricing guidance from corporate overall?
Perspective & context:

39. Reps have significant pricing freedom, pricing likely all over
the place
Raised prices on some customers in April
Some rumors that we are cheaper than competitors
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Title
Unit of measure
Structuring the problem – the issue tree
9
Main issue/
problem
Sub-issue 1
Sub-issue 2
Sub-issue 3
Sub-issue 1.1
Sub-issue 2.1
Sub-issue 1.2
Sub-issue 2.2
Sub-issue 2.3
How do we structure the overall question? What are the
different ways to raise price?
TRACKER
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40. 1 Footnote
SOURCE: Source
Title
Unit of measure
Define
Structure
Prioritize
Analyze
Synthesizem
A possible Dunder Mifflin starter issue tree
10
Pricing opportunity
Raise prices universally
Selectively raise prices
Raise prices on certain customers
Make specific sales reps price better
Other ideas
Some decent guesses, but this is stuff the client told us already.
We need some facts and insights

41. TRACKER
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1 Footnote
SOURCE: Source
Title
Unit of measure
Define
Structure
Prioritize
Analyze
Synthesizem
What data would you ask the client for?
11
TRACKER
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1 Footnote
SOURCE: Source
Title
Unit of measure

42. Two fundamentally different approaches to data
12
Approach 1: directly ask for data on specific issues. E.g., if this
was on logistics
Can you give me data on deliveries per customer?
Can you give me your route schedule?
What is the typical wait time per customer delivery?
...
Approach 2: ask for raw data that you can use for those and
many others
The customer master – names, IDs, addresses
The delivery transactions – customer, date & time, truck #
If you don’t derive stuff from the master data:
You might get the wrong information and you can’t check
If you have more ideas you have to go back and the results may
be inconsistent
If you find something interesting you cannot directly dig down
TRACKER
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1 Footnote
SOURCE: Source
Title
Unit of measure

43. The Dunder Mifflin data
13
Activity data
Sales transactions for Scranton
SKU
Customer
Date
Quantity
Price per unit
List of customers we planned to increase price on April 1st
Customer
Price increase (as a factor – 1.05 is a 5% increase)
Master data
Sales rep assignments
Customer
Sales rep assigned
SKU master data
SKU
Vendor ID
Cost per unit
SKU type (ABC)
This is real data from a real distributor. Obviously SKUs,
Customer and Vendor IDs are disguised

44. TRACKER
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SOURCE: Source
Title
Unit of measure
A couple days has passed, and this first stage of the analysis is
complete
We need to communicate to the client. What and how do we say
it?
14
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Synthesize and develop recommendations – Hypothesis trees
15
Main point
Supporting argument 1
Supporting argument 2
Supporting argument 3
Evidence/supporting argument

45. Supporting argument 2.1
Evidence/supporting argument
Supporting argument 2.2
Supporting argument 2.3
Hypothesis trees are about structuring your logic & telling the
story
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Define
Structure
Prioritize
Analyze
Synthesizem
Hypothesis tree - example
16

46. We should pay our sales force on margin instead of revenue, we
can grow profits substantially
Sales reps sell products badly because we compensate them on
revenue
Selling at higher prices would increase profitability
significantly
Sales reps would accept switching to a new compensation
system
Data analysis shows that many sales happen at near 0 margin,
meaning no profit
Raising prices by 2% on average would be worth $25m
In anonymous interviews, sales reps admit to frequently
lowering price to close deals quickly in order to get higher sales
overall
The close rate of sales reps that charge 2-3% higher prices is
not different from sales at lower prices
Customers tell us that our products are preferred over
competitors
Interviews show that reps are very open to a switch
Good reps would make more money and only a handful of reps
would make slightly less
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Define

47. Structure
Prioritize
Analyze
Synthesizem
Hypothesis tree – DM initial pricing analysis
17
Early indications are that DM has substantial pricing
opportunity, however more work is required
Analyzing profitability by customer shows that many small
customers are low margin, despite our intention to price them
higher
Similarly, some reps show much higher margin than others
However, more analysis is required
The raw data contained a lot of non-sensical entries. We
recommend to review these and adjust as needed
Some of the margin differences in customers/reps may be
caused by other external factors that we have to exclude
Most customer buy each item no more than once per year, thus
the odds that a customer even notices increases are low
Analysis of the April 1st price increase was inconclusive, but no
big negative customer reaction was seen
TRACKER
Unit of measure

48. 1 Footnote
SOURCE: Source
Title
Unit of measure
Executive summary
18
Early indications are that DM has substantial pricing
opportunity, however more work is required
Analyzing profitability by customer shows that many small
customers are low margin, despite our intention to price them
higher
Similarly, some reps show much higher margin than others
Analysis of the April 1st price increase was inconclusive, but no
big negative customer reaction was seen
Most customer buy each item no more than once per year, thus
the odds that a customer even notices increases are low
However, more analysis is required
The raw data contained a lot of non-sensical entries. We
recommend to review these and adjust as needed
Some of the margin differences in customers/reps may be
caused by other external factors that we have to exclude
TRACKER
Unit of measure
1 Footnote

49. SOURCE: Source
Title
Unit of measure
Many small customers have low margin – analysis indicates a
gap of >$2 million
19
Margin by customer
(Percent)
Customer revenue ($)
There is little correlation between customer margin and size
A large number of small customers do not meet our minimum
target of 50% margin
If every customer under $1m revenue could be raised to at least
50%, we would be making $2.3m in additional profit
Source: Profitability analysis of DM transaction data (last 12
months)
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Many small customers have low margin – analysis indicates a
gap of >$2 million
20

50. Margin by customer
(Percent)
Customer revenue ($)
There is little correlation between customer margin and size
A large number of small customers do not meet our minimum
target of 50% GM
If every customer under $1m revenue could be raised to at least
50%, we would be making $2.3m in additional profit
Source: Profitability analysis of DM transaction data (last 12
months)
The message: what you want the readers to take away, most
important part of the slide
Chart title: what is shown on the chart
Comments & explanations
Source: where the data and analysis comes from
TRACKER
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1 Footnote
SOURCE: Source
Title
Unit of measure
Similarly, at first sight, several sales reps could raise prices
21
Margin by sales rep
(Percent)
Sales rep ID
Most sales reps sell on average with our target margin, but

51. several are substantially below 50%
Further work is required to understand whether this is due to
customer mix or competitive intensity in their territory or other
reasons
Source: Profitability analysis of DM transaction data (last 12
months)
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
0.38925573923841789 0.53541146916645854
0.53226311780277569 0.47258051601543694
0.48084285705819396 0.49389965102260985
0.41919048923985752 0.49194002104771206
0.52012371505335542 0.52472383988465976
0.49948868803583762 0.34987512286060729
0.53103595166885231 0.5322899287682481
0.51322157192258577 0.51838079858706521
0.54097352928093256 0.52562444940618158
0.51244758137044788 0.45456744449711667
0.45132925828032178 0.54291883773862282
0.54747409213343223 0.53608879330229431
0.54195694297535124 0.21853770092674035
0.51364256127278418 0.51123562428648262
0.52302879580181427 0.53103810257629203
0.53292984886133221 0.52597209399303002
0.52316875702584864 0.24455023322685115
0.35085789550844354 0.53965379115392376

52. 0.51392826184344698 0.2974496824809193
0.49321965988600996 0.41991590971491538
0.41918009929947608 0.48103759135209456
0.54269930192495608 0.50700201552228497
0.47231067265842774 0.50126485918873187
0.4921864902682489 0.52984736909273167
0.45396295322599206 0.52006933804370159
Analysis of the April 1st price increase is inconclusive, but at
least it did not look as if customers had a violent negative
reaction
22
On April 1, DM increased prices on 788 customers by 5% on all
items
We manually confirmed that these price increases were indeed
implemented
Margins stayed flat while the control group grew 2 percent
Revenue increased by 52% vs 16% for Control Group
Analysis is inconclusive at this point – clearly customers
selected were non-representative in some way
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title

53. Unit of measure
Gross margin by customer group
Q1 2019
Increased customers Other customers 0.5 0.44 Q2 2019
Increased customers Other customers 0.5 0.46
Revenue by customer group (Indexed to Q1)
Q1 2019
Increased customers Other customers 1 1 Q2 2019
Increased customers Other customers 1.52
1.1599999999999999
Significantly more work is required
23
Issues and potential other factors
Data: The transaction data and cost data has a number of issues
(>10% of entries) – missing cost information, frequent ‘sales at
loss’, items with multiple prices, etc.
Other correlations: customer price differences may be due to
product mix, geographic location, industry etc. Sales rep
variations may be due to customers in different geographies or

54. with different competitive intensities
Recommended next steps
Need to sit down with IT and Operations group to review
specific instances and agree on approach to clean
Review select ‘interesting’ customers jointly with sales leader
and identify any factors that need to be taken into consideration
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Recap, what have we learned?
24
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure

55. What have we learned about Consulting with Analytics?
25
Consulting/problem-solving is an end-to-end process with many
parts
The usually hard parts
What exactly is the problem?
How do I break it apart?
What analysis should I and can I do that will actually tell me
something and be defensible against critics?
How do identify and filter out the crud in the data so I am
basing the analysis on the right info
How do I use the insights to tell a convincing story?
The usually easy part
Doing the analysis once the data is clean
TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
Define the problem
Structure the problem
Prioritize

56. Plan & conduct analysis
Synthesize & develop recommendation
What have we learned about Consulting with Analytics?
26
2. You need to UNDERSTAND the data, INSPECT it and
CHECK it
What exactly does every field mean?
What do ‘weird’ entries mean in the real world? E.g., is this a
return or an error? Is this really a normal SKU? Why would we
sell something at a loss? Is there really one customer that we
lose $millions on?
Do sample customers act in a way that is intuitive? E.g., do they
buy a 1-5 times a week?
Do the aggregates in the data set jibe with the overall business
numbers? Is the total revenue close to the financials? Is the
number of customers approximately right? Is the gross margin
roughly right? Are the top earning SKUs the ones we would
expect? …
Macro-statistics (e.g., regressions, overall stats) are not a
substitute for understanding
TRACKER

57. Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
What have we learned about Consulting with Analytics?
27
3. Only BIG signals matter
Real data is random and will always show trends
Small variations are usually not a signal of opportunity
Small trends usually are random or at least don’t translate to
value
4. Small BIG signals matter
Always look at the ‘units of action’, you might find one or two
that drive substantial value
E.g.,
Two big customers may be extremely poorly priced
Two out of the 50 sales reps are really abusing the system
Regression & other macro-analysis usually does not find the
opportunities
Never trust just the summary statistics of an analysis, always
look at plots – you are looking for the ‘That’s weird’ moment

58. TRACKER
Unit of measure
1 Footnote
SOURCE: Source
Title
Unit of measure
What have we learned about Consulting with Analytics?
28
5. Behold the power of 80/20
Very few businesses have customers or products that are all
similar. The 80/20 rule
20% of customers make 80% of the profit
20% of SKUs are 80% of the volume
20% of transactions are 80% of the revenue
20% of customers have 80% of the wealth
When you remove the top 20%, the 80/20 rule still applies –
Distributions follow power laws, not normal statistics
This means you can often radically short-cut analysis by
focusing on the 20%
6. Beware the curse of 80/20
Random variations do not ‘null out’ in aggregate analysis
E.g.,
800 truly random customers out of 14000 are not necessarily
representative

59. TRACKER
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SOURCE: Source
Title
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29
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30
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60. RUN_DATE TO FR DATE PRODUCT
VENDOR QTY
10/3/2019 5:33:42 PM 01 51 2019-05-13 40099117
BR-01 2
10/3/2019 5:33:42 PM 02 01 2019-05-07 80117
142905 8
10/3/2019 5:33:42 PM 02 01 2019-05-28 80117
142905 8
10/3/2019 5:33:42 PM 02 01 2019-01-07 WSC117
121560 100
10/3/2019 5:33:42 PM 03 01 2019-01-02 80117
142905 10
10/3/2019 5:33:42 PM 03 01 2019-05-20 80117
142905 10
10/3/2019 5:33:42 PM 03 01 2019-01-21 WSC117
121560 200
10/3/2019 5:33:42 PM 03 01 2019-05-16 WSC117
121560 100
10/3/2019 5:33:42 PM 03 01 2019-06-07 WSC117
121560 100
10/3/2019 5:33:42 PM 03 01 2019-06-20 WSC117
121560 100
10/3/2019 5:33:42 PM 03 01 2019-08-30 WSC117
121560 100
10/3/2019 5:33:42 PM 04 21 2018-12-07 SB117 BR-
20 1
10/3/2019 5:33:42 PM 05 01 2018-12-27 40099117
142905 10
10/3/2019 5:33:42 PM 05 01 2018-10-29 WSC117
121560 100

61. 10/3/2019 5:33:42 PM 05 01 2018-11-09 WSC117
121560 100
10/3/2019 5:33:42 PM 05 01 2019-01-08 WSC117
121560 100
10/3/2019 5:33:42 PM 05 01 2019-07-25 WSC117
121560 100
10/3/2019 5:33:42 PM 05 01 2019-08-01 WSC117
121560 100
10/3/2019 5:33:42 PM 05 20 2018-12-14 XLG117
132327 7
10/3/2019 5:33:42 PM 05 20 2019-01-29 XLG117
132327 9
10/3/2019 5:33:42 PM 06 01 2019-01-05 RSK2117
135440 1
10/3/2019 5:33:42 PM 06 01 2018-10-31 WSC117
121560 200
10/3/2019 5:33:42 PM 06 01 2019-03-21 WSC117
121560 100
10/3/2019 5:33:42 PM 06 01 2019-04-15 WSC117
121560 100
10/3/2019 5:33:42 PM 06 01 2019-04-23 WSC117
121560 100
10/3/2019 5:33:42 PM 06 01 2019-06-19 WSC117
121560 200
10/3/2019 5:33:42 PM 06 01 2019-07-26 WSC117
121560 300
10/3/2019 5:33:42 PM 06 21 2019-04-26 XLG117
BR-20 3
10/3/2019 5:33:42 PM 06 21 2019-07-22 XLG117
BR-20 3
10/3/2019 5:33:42 PM 07 01 2019-02-26 WSC117
121560 100
10/3/2019 5:33:42 PM 07 01 2019-03-26 WSC117
121560 100
10/3/2019 5:33:42 PM 07 01 2019-04-30 WSC117
121560 100

62. 10/3/2019 5:33:42 PM 07 01 2019-05-14 WSC117
121560 100
10/3/2019 5:33:42 PM 07 01 2019-05-19 WSC117
121560 100
10/3/2019 5:33:42 PM 07 01 2019-06-24 WSC117
121560 200
10/3/2019 5:33:42 PM 07 01 2019-09-16 WSC117
121560 100
10/3/2019 5:33:42 PM 07 01 2019-09-27 WSC117
121560 200
10/3/2019 5:33:42 PM 08 01 2019-02-18 40099117
142905 20
10/3/2019 5:33:42 PM 08 01 2018-11-12 WSC117
121560 100
10/3/2019 5:33:42 PM 08 01 2018-11-20 WSC117
121560 100
10/3/2019 5:33:42 PM 08 01 2019-03-08 WSC117
121560 200
10/3/2019 5:33:42 PM 09 01 2018-10-05 80117
142905 10
10/3/2019 5:33:42 PM 09 01 2019-08-22 80117
142905 10
10/3/2019 5:33:42 PM 09 01 2019-08-26 80117
142905 40
10/3/2019 5:33:42 PM 09 01 2019-08-29 80117
142905 8
10/3/2019 5:33:42 PM 09 01 2019-04-18 80117
142905 10
10/3/2019 5:33:42 PM 09 01 2019-02-11 G3117
135510 14
10/3/2019 5:33:42 PM 09 01 2018-10-15 H4117
010101 1
10/3/2019 5:33:42 PM 09 01 2018-10-29 WSC117
121560 200
10/3/2019 5:33:42 PM 09 01 2019-08-05 WSC117
121560 200

63. 10/3/2019 5:33:42 PM 09 01 2019-09-03 WSC117
121560 200
10/3/2019 5:33:42 PM 10 01 2019-01-11 80117
142905 20
10/3/2019 5:33:42 PM 10 01 2019-01-21 80117
142905 20
10/3/2019 5:33:42 PM 10 01 2019-03-25 80117
142905 57
10/3/2019 5:33:42 PM 10 01 2018-12-03 CP998117
108300 1
10/3/2019 5:33:42 PM 10 01 2018-11-19 G3117
135510 1
10/3/2019 5:33:42 PM 10 01 2019-08-19 WSC117
121560 100
10/3/2019 5:33:42 PM 10 01 2019-09-27 WSC117
121560 100
10/3/2019 5:33:42 PM 10 21 2019-07-23 SB117 BR-
20 1
10/3/2019 5:33:42 PM 11 01 2019-01-09 40099117
142905 1
10/3/2019 5:33:42 PM 11 01 2018-10-23 80117
142905 10
10/3/2019 5:33:42 PM 11 01 2018-11-03 80117
142905 7
10/3/2019 5:33:42 PM 11 01 2019-04-09 WSC117
121560 100
10/3/2019 5:33:42 PM 11 01 2019-06-03 WSC117
121560 100
10/3/2019 5:33:42 PM 11 01 2019-07-10 WSC117
121560 100
10/3/2019 5:33:42 PM 11 01 2019-07-30 WSC117
121560 300
10/3/2019 5:33:42 PM 11 01 2019-08-06 WSC117
121560 200
10/3/2019 5:33:42 PM 12 01 2018-10-30 WSC117
121560 100

64. 10/3/2019 5:33:42 PM 12 01 2019-07-01 WSC117
121560 100
10/3/2019 5:33:42 PM 13 01 2019-01-03 WSC117
121560 100
10/3/2019 5:33:42 PM 15 01 2019-06-18 G3117
135510 4
10/3/2019 5:33:42 PM 15 01 2019-01-29 WSC117
121560 100
10/3/2019 5:33:42 PM 15 01 2019-04-11 WSC117
121560 100
10/3/2019 5:33:42 PM 15 20 2019-04-01 XLG117
132327 7
10/3/2019 5:33:42 PM 15 20 2019-04-23 XLG117
132327 10
10/3/2019 5:33:42 PM 16 21 2019-09-03 SB117 BR-
20 1
10/3/2019 5:33:42 PM 16 21 2019-02-12 XLG117
BR-20 1
10/3/2019 5:33:42 PM 16 21 2019-03-12 XLG117
BR-20 5
10/3/2019 5:33:42 PM 16 21 2019-05-03 XLG117
BR-20 1
10/3/2019 5:33:42 PM 16 21 2019-08-17 XLG117
BR-20 2
10/3/2019 5:33:42 PM 16 21 2018-11-05 XLG117
BR-20 3
10/3/2019 5:33:42 PM 20 21 2018-10-16 XLG117
BR-20 1
10/3/2019 5:33:42 PM 20 21 2018-11-19 XLG117
BR-20 1
10/3/2019 5:33:42 PM 21 20 2018-12-17 SB117
999920 2
10/3/2019 5:33:42 PM 21 20 2019-04-01 SB117
999920 2
10/3/2019 5:33:42 PM 21 20 2019-08-05 SB117
999920 2

65. 10/3/2019 5:33:42 PM 21 20 2018-10-29 XLG117
132327 4
10/3/2019 5:33:42 PM 21 20 2019-02-04 XLG117
132327 1
10/3/2019 5:33:42 PM 21 20 2019-03-05 XLG117
132327 5
10/3/2019 5:33:42 PM 21 20 2019-04-22 XLG117
132327 3
10/3/2019 5:33:42 PM 21 20 2019-04-29 XLG117
132327 1
10/3/2019 5:33:42 PM 21 20 2019-07-17 XLG117
132327 3
10/3/2019 5:33:42 PM 21 20 2019-08-12 XLG117
132327 2
10/3/2019 5:33:42 PM 22 01 2019-09-26 WSC117
BR-01 100
10/3/2019 5:33:42 PM 22 20 2018-10-30 SB117
999920 1
10/3/2019 5:33:42 PM 22 20 2018-11-06 SB117
999920 1
10/3/2019 5:33:42 PM 22 20 2018-11-06 SB117
999920 1
10/3/2019 5:33:42 PM 22 20 2019-08-16 SB117
999920 1
10/3/2019 5:33:42 PM 22 20 2019-08-16 SB117
999920 1
10/3/2019 5:33:42 PM 22 20 2019-05-29 XLG117
132327 3
10/3/2019 5:33:42 PM 22 20 2019-06-20 XLG117
132327 3
10/3/2019 5:33:42 PM 31 01 2018-11-09 AG4117
101620 12
10/3/2019 5:33:42 PM 31 01 2019-02-04 AG4117
101620 5
10/3/2019 5:33:42 PM 31 01 2019-04-01 AG4117
101620 10

66. 10/3/2019 5:33:42 PM 31 01 2019-05-13 AG4117
101620 8
10/3/2019 5:33:42 PM 31 01 2019-06-17 AG4117
101620 12
10/3/2019 5:33:42 PM 31 01 2019-04-22 G3117
135510 1
10/3/2019 5:33:42 PM 32 01 2019-09-17 WSC117
121560 100
10/3/2019 5:33:42 PM 33 01 2018-12-10 K5117
135510 1
10/3/2019 5:33:42 PM 34 01 2018-11-05 WSC117
121560 100
10/3/2019 5:33:42 PM 52 51 2018-10-08 JN117
411380 24
10/3/2019 5:33:42 PM 52 51 2018-11-07 JN117
411380 24
10/3/2019 5:33:42 PM 52 51 2019-01-21 JN117
411380 48
10/3/2019 5:33:42 PM 52 51 2019-05-20 JN117
411380 48
10/3/2019 5:33:42 PM 53 51 2019-01-30 DRM9117
BR-45 30
10/3/2019 5:33:42 PM 53 51 2019-06-05 JN117
411380 12
10/3/2019 5:33:42 PM 53 52 2019-05-28 DRM9117
BR-51 13
10/8/2019 9:08:17 AM 02 01 2019-10-07 WSC117
121560 100
10/8/2019 9:08:17 AM 16 01 2019-10-07 K5117
135510 4
10/8/2019 9:08:17 AM 16 21 2019-10-07 SB117 BR-
20 1
10/8/2019 9:08:17 AM 22 01 2019-10-01 WSC117
121560 100

67. You are getting 3 files from Dunder Mifflin (cut down to a very
small subset)
Your mission, should you choose to accept it, is to generate an
output table in the following format (with an example - and
actually correct - entry)
SC PRODUCT SOLD TRANSFERRED_IN
ON_HAND
2 80117 10 16 0
The total result should have 18 entries, use that and the sample
entry as a check.
SC is the ‘Service Center’ (= branch), PRODUCT is the product
ID, SOLD is the number of units of this product sold in this SC
in the month of May! Transferred_in is the number of units of
this SKU that were transferred into this SC also in the month of
May! And the QOH column is the amount of inventory we have
on hand in that SC for that product.
Documentation of the source files and the columns is below.
A couple of hints:
- cleanup will be required (look closely)
- you can convert DateTime objects to months using the
df.DATE.dt.month function
- you will have to generate the individual columns separately
and then join them together afterwards.
- When you use the .merge function, you can merge on multiple
columns by using on=[‘col1’,’col2’]
- To derive the individual columns you will need to use ‘group
by’. You can group by multiple columns using

68. .groupby([‘A’,’B’]).agg({'QTY' : 'sum'})
- Once you use groupby, that grouping columns will become an
index. You can convert it back to a column using
reset_index(inplace=True).
- To pick dates, please use the INVOICE_ORDER_DATE
column, not the ORDER_DATE one
Example for the last part:
s1=sales.groupby(’Customer’).agg({‘QTY’ : ‘sum})
s1.reset_index(inplace=True)- For the final output use LEFT
joins, starting with the sales, then the transfers, finally the
inventory
Deliverable is a notebook file with all your steps and the output
included. Pandas or R, I don’t care. But it needs to work
Data files:
Sales test - a file with the sales transactions. Most fields should
be obvious,
SHIP_VIA is the mode of shipping (e.g., UP for UPS) - not
important.
SHIP_SC is the Service Center from which it ships
SKU master test - a file with the SKU master data. Important
non-obvious fields are
SC - Service Center (=branch)
QOH - quantity on hand - actual current inventory
EOQ - the economic reorder quantity
DATE - the date this data was pulled
you can ignore most of the rest, but try to guess what they
might be
Transfers test - a file with the transfers of products between
branches. The fields are

69. RUN_DATE - date when this report was run
TO - SC from which the item was shipped
FR - SC to which the item was shipped
rest is obvious
BE VERY CAREFUL WHEN YOU LOOK AT THE CSV FILES
WITH EXCEL - DO NOT HIT ‘SAVE’ OR EXCEL WILL
MESS THEM UP AND YOU HAVE TO RE-DOWNLOAD
THEM