MRS Advanced Analytics Innovation Symposium Presentation

MRS Advanced Analytics
Innovation Symposium
30th April 2015
#MRSlive

Brand Share & Industry Size: Will
the twain ever meet?
Using a portfolio of techniques to improve
accuracy of market volume evolution in
price change scenarios
April 2015
Sreeram Srinivasan, IMRB International
Ranjana Gupta, IMRB International

30
40
Marketer’s pricing dilemma
30 Market
volum
e 100
bn
Marke
t
volum
e
70 bn
With new prices, my share will
grow…
… but will my volume
also grow?
Market share grows….. ….but, market size shrinks

Both the consumer and the macro variables needed to
answer the questions..
C
o
n
s
u
m
e
r
M a c r
o
Brand choice
Same brand, switching etc.
In context of price
Category choice
Frequency, consumption,
substitutes etc.
Industry
Past volumes,
substitutes, prices etc.
Economy
Income, affordability,
Inflation etc.
Relies on past trends
Future may be different
Future oriented.
Past learnings not
fully leveraged

Hence for accurate volume forecasts, no single
methodology can provide complete answer
Consumer research and macro-economic models should
be integrated
Choice Model
Consumer Research
• Brand shares
• Switching
Econometric
Macro Modelling
• Market size

An approach to integration of
choice and econometric model
However, before integration, the individual
tools need to be refined, adapted…

…to account for the nature of the category
How to account for occasion?
Is it an impulse or
considered purchase?
Is it a repertoire or non-
repertoire category?
Do number of units matter?
What about frequency of
purchase?
How can we ensure that the
respondent reacts to only
relevant offers?

The questioning technique uses FORCE principle
to make the consumer response more realistic…
Familiar
Only brands that the respondent interacts with / likely to interact with shown –
customized for each respondent real time
Evoked set created using respondent’s current repertoire, past usage and future
disposition
Occasio
n
Respondent’s answers using an occasion as a context in occasion led categories
like CSDs, Snacks etc. There can be other household purchase categories where
occasion is irrelevant
Repertoir
e
Respondents allowed to select multiple brands, SKUs and units, as they might in
real life
Channel
Primary channel of purchase identified and specified in the questioning
Event
Recurren
ce
Frequency of purchase

Here’s an example…
around 20 such choice tasks shown
Imagine you are doing your monthly grocery shopping from the supermarket and you
have to buy bathing soaps. On the shelf you see the following brands with the given
prices? Which brands are you likely to buy?
You can choose as many brands as you like. Or you can walk out of the shop without
buying any.
Do state the number of packs that you would be buying.
I will not buy any
anything
2
Johnson’s Baby
Soap
75 gms
Rs. 30
Dove
75 gms
Rs. 40
Santoor – pack of 4
100gms X 4
Rs. 50
Now at Rs. 40
Pears
100 gms + 25 gms
extra free
Rs. 25
Rexona
200 gms
Rs. 25
I would buy a
shower gel
Lux
100 gms
Rs. 10
1

Consumer choices are converted into utilities – two levels
of calculations
Main Effect
For every level within each attribute at a
respondent level
Example: Utility or preference for Brands like
Dove, Pears etc., for SKUs like 100 gms,, 75
gms and for various price levels
Cross Effect
Interaction between attributes
Example: Utility for Dove by itself and for Dove
at a particular price may be different
The utilities are transformed into
share of preference and
weighted to give the shares in
various scenarios
For all existing brands & SKUs,
the current levels of distribution
are built into the model – to
ensure current scenario shares
are in line with actual market
shares
Respondent level estimation of preference helps in calculation of
gains and losses from one brand to another

The output
Current
Scenario
New price
scenario
(Client’s
brands cut
prices by
10%)
Company
Brand X 20.0% 21.8%
Brand Y 17.0% 17.2%
Brand A 10.0% 9.9%
Brand B 5.0% 4.4%
Brand P 8.0% 7.6%
Brand Q 15.0% 14.8%
Brand R 10.0% 9.7%
Brand S 15.0% 14.6%
39%37%
Brand X
Gaining From
Brand B, Brand
S
Net Gain/Loss 1.8%

Econometric Model: Inputs and
Outputs

The input
Past volumes Population
Substitute
categories
(Real) Price Purchase frequency
No. of packs(Real) Income Basket size

The statistics
Options
Simple Regression
Volume = fn (Price)
Easy but can lead to situations like
increase price to increase
volumes!
Simple Time
Series
Volume = fn (Past volumes)
Builds in past volume trends but assumes that
history will definitely repeat itself
Price
Volume

The statistics
Adopted method
ARIMA
(Auto Regressive Integrated Moving
Average)
Volume = fn (Real Price, Past Volume, Real Income, etc….)
Moving average included – accounts for any possible prediction error in
previous time periods
Accounts for autocorrelation
Better prediction accuracy

The statistics
Adopted method
ARIMA
(Auto Regressive Integrated Moving
Average)
Deseasonalized data - predict organic change in volumes
Model by major sub-groups to account for different trends – break the
market
Price gap between sub-groups used – inter-movement built in

The output
Market volumes
125000 tonnes
Current scenario
126250 tonnes
New price
scenario
1%

It’s quite simple actually…
Shares X Market Volumes = Company Volumes
37
Current
Scenario
125000 tonnes
New Scenario
Share
Market Volumes
46250 tonnesCompany volumes
39 126250 tonnes
Share
Market Volumes
49238 tonneCompany volumes
Share change: 5%
Volume change: 6%

Results validated across markets
0
20
40
60
80
100
Predicted volume accuracy by brands in various markets
(74 data points in this graph)
Average :
88%
The trick: improve accuracy of the individual models

Identifying the relative impact of
touchpoints: A tailored statistical
technique for real-time data
Shane Baxendale, Cranfield School of Management
Heval Ceylan-Gilchrist, MESH
MRS ADVANCED ANALYTICS NETWORK
30th April 2015

Why are we here today?
 Real-time Experience Tracking Methodology
 Analysis - using linear mixed-effects
regression
24

Our thinking
 Consumers experience brands through multiple channels
(not just advertising!)
 Brand experiences influence a consumer’s attitude toward
brands
 The majority of existing literature focusses on the impact of
one or two types of experience
What impact are different encounters having on
consumer attitudes toward the brand?
25
*Baxendale S., Macdonald E.K., Wilson H.N., (2015), The impact of different
touchpoints on brand consideration, Journal of Retailing, 37(2), 203.

Real-time Experience
Tracking (RET)
ONLINE REAL-TIME ONLINE
Day 9Day 2 - 8Day 1

Text us whenever you
see, hear or experience
anything to do with the
following brands…
Text framework
27
BRAND: A)Brand A B)Brand B C)Brand C
D)Brand D E)Brand E F) Other
OCCASION: A)TV B)Poster/Billboard
C)Radio D)In store E)Cinema F)Newspaper
G)Magazine H)Conversation
I)Online/Mobile J)Mailing/leaflet K)Me
Purchasing L)Me using M)Someone else
using N)Sponsorship O)Other
FEELING: 5)Very positive 4)Fairly positive
3)Neutral 2)Fairly negative 1)Very Negative
CHOICE: 5)Much more likely to choose
4)Slightly more likely to choose 3)No
difference 2)Slightly less likely to choose 1)
Much less likely to choose

Imagine you experienced Brand A Online…
…you would text:
28
a 5i 5
CHOICE:
5) Much more likely to
choose
4) Slightly more likely to
choose
3) No change
2) Slightly less likely to
choose
1) Much less likely to
choose
ENGAGEMENT:
5) Very positive
4) Fairly positive
3) Neutral
2) Fairly negative
1) Very negative
BRAND:
a) Brand A
b) Brand B
c) Brand C
d) Brand D
e) Brand E
f) Other
OCCASION:
a) TV
b) Poster/Billboard
c) Radio
d) In store
e) Cinema
f) Newspaper
g) Magazine
h) Conversation
i) Online/Mobile
j) Mailing/Leaflet
k) Me purchasing
l) Me using
m) Someone else using
n) Sponsorship
o) Other
Which brand was it? Where did you
experience it?
How likely did it make you
to choose the brand next
time?
How did it make you feel?

Now tell us more in an online diary…
29
This is an individual’s experience log By clicking on each entry, the experience can
be expanded upon in detail
Wednesday 13th February 2012,11:54
Wednesday13th February2012,11:54
Wednesday13th February2012,10:22
Tuesday512h February2012,18:46
Tuesday12th February2012,13:05
Tuesday12th February2012,08:38
Brand A,Online, Very Positive,
MuchMoreLikely toChoose
Brand C,Conversation, Very Negative,
MuchLess Likely toChoose
Brand E, TV, Fairly Positive,
Slightly More Likely to Choose
Other, Instore, Fairly Positive,
Slightly morelikely toChoose
Brand B, Mailing/Leaflet, Slightly Negative,
Nochange
Brand A
Brand’s website
Very positive
Much more likely to choose
I was looking on the brand website to find out
more information about the product range. Looks
like there are some good options.
Look for product info
13/02/2012, 11:54
Please tell us exactly what yousaw? :
What was the purpose ofyour online activity? :
Brand’s website
Ad from brand
In the news
Social networking site
Price comparison site
Other
For each level of data captured
in real-time we can tailor extra
questions to get more granular
information in near-time

Data
For one individual
Brand A Brand B … Brand N
Consideration Wk0
Consideration Wk1
Consideration Wk0
Consideration Wk1
Consideration Wk0
Consideration Wk1
Freq. & Pos.
Brand Ad
Retailer Ad
In Store
WOM
…
Freq. & Pos.
Brand Ad
Retailer Ad
In Store
WOM
…
Freq. & Pos.
Brand Ad
Retailer Ad
In Store
WOM
…
30
+ve -ve -ve

Model
© Cranfield University 31
Change in Consideration = ???
Data Rationale Considerations Implications
Demographics / Participant
information
Certain consumer groups
may be more / less likely to
change their opinion
towards brands over time
Multiple responses per
participant means that we
can learn more about
individual tendencies
Need to account for the
homogeneity in repeated
responses, therefore
random effects modelling
Frequency of experience More experiences can be a
positive impact (via
reinforcement of
messaging) or negative
(over-exposure)
There could be many
potential ways of including
this in the model;
Constant effect
Diminishing returns
Check the validity of the
model by testing multiple
approaches
Positivity of experience A consumers’ perception of
an experience can
determine the impact it
has on them
How do we account for
positivity over multiple
encounters?
Average?
Check the validity of the
model by testing multiple
approaches

Parameter operation
Frequency
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
Exposure
Impact = x if Freq.>0
Increasing Impact
Impact = x*Freq.
Diminishing Returns
Impact = x*ln(Freq.+1)
Impact = x*Freq. - y*Freq.^2

Parameter operation
Positivity
1. Average positivity across experiences
2. Average positivity and variance of positivity
3. Positivity of last experience
4. Freq. of positive and Freq. of negative
experiences

Results
Focal Frequency Positivity
In-store
communications
=1 1
Brand advertising =1 =2
Retailer advertising =1 =2
Peer observation =1 =4
Traditional earned =5 =4
WOM =5 6
Competitor Frequency Positivity
In-store
communications
1 =1
WOM =2 =1
Peer observation =2 =1
Retailer advertising =4 =1
Brand advertising =4 =1
Traditional earned 6 =1

The contents of this document are the sole and confidential property of Lieberman Research
Worldwide, and may not be reproduced or distributed without the express written permission
of Lieberman Research Worldwide.
Prepared for CLIENT
TITLE
LRW Europe
BAYESIAN ANALYSIS
FOR MARKETING IMPACT
April
2015
LRW Europe
1, Heathcock
Court, 415, Strand
London
WC2R 0NT
Prepared by:
Adele Gritten &
Graham Williams
for MRS Advanced
Analytics
Conference

‹#›
© 2015 Lieberman Research Worldwide.
All rights reserved. CONFIDENTIAL.
 Why should our
industry care about
BayesNets?
 What is BayesNets?
 What are the
Advantages of
BayesNets?
 Illustrative outputs
 Live UK Case Study
 Summary
TODAY’S AGENDA

‹#›
Why should
our Industry
Care about
BayesNets?

‹#›
Why Should Our Industry Care About BayesNets?
 BayesNets is a unique and more comprehensive driver analysis to
assist Marketers
 Overcomes the shortcomings of traditional [drivers analysis] methods
 Allows the integration of profiling, behavioural and other metrics with
attitudinal/preference ratings to create a more holistic view of what
drives the dependent variable
“Bayesnets has played a major role in several
recent wins. It’s especially helpful with brand
positioning research where the complex
relationships between brand attributes
demands a more nuanced and flexible
approach to analysis”. LRW Account Director

‹#›
What is
BayesNets?

‹#›
What is BayesNets?
Think of BayesNets as “drivers analysis on steroids”
Let’s review the logic and goals of
“key drivers analysis”:
 There is a market attitude or behaviour – the
“target outcome” – which:
 A client needs to favourably influence in the
marketplace, but which…
 They cannot influence directly
 So we need to find the best “levers to pull”
through which to indirectly influence that attitude
or behaviour; e.g. customer attitudes or
perceptions:
 Which we can influence through
product/service design or marketing, and…
 Which have strong “derived importance” in
driving the “target outcome”
 Ultimately, the purpose of “key drivers analysis” is
to empirically identify the best “levers to pull” for
maximum in market impact.
BayesNets:
 Can be thought of
generally as a more
powerful key drivers
analysis methodology
 Offers a number of
significant advantages
compared to hitherto
commonly used key
drivers analysis
approaches

Where Did BayesNets Come From?
| Thomas Bayes
“Bayesian” refers to Reverend Thomas Bayes’
Theorem from the 18th century that paved the way
for data to be used in prediction. Bayes’ Theorem
basically allows us to look at multi-directional
probabilities.
 18th Century English statistician,
philosopher, and minister
 Formulated Bayes’ Theorem: a
mathematical expression of probabilities
from observed data
 Hotly debated and contested by
Frequentists until recent years
42

‹#›
What are BayesNets? | A Model of Relationships
Bayesian networks (BayesNets) are a type of “path analysis” model that simultaneously describes the
relationships between variables in a network system based on joint probabilities between the variables.
BayesNets improves upon models that use advanced correlation and regression techniques (such as most
regression analyses and Structural Equation Modeling). Basically, it’s a better way of understanding
interactions between independent variables as they drive dependent variables.
WET
PAVEMENT
SPRINKLER
RAIN
SLIPPERY
Probabilistic
Relationship
Probabilistic
Relationship
Variables
or Factors
Variables
or Factors
High-Dimensional Probability Hypercube

‹#›
What are the
Advantages
of
BayesNets?

‹#›
What are Advantages
of BayesNets?
BayesNets’ advantages over
more commonly used
techniques are “technical” but
nonetheless significant.
BayesNets’ advantages include:
 Does fully interactive, “multivariate” modeling
 Not “confused” by multicollinearity
 No implicit assumption of “linear” relationships
 Siloed Regression Models don’t capture indirect or interaction effects
BayesNets helps us find the best model:
 We don’t have to “hypothesise” the structure of the multivariate network
 Rather, BayesNets’ “machine learning” algorithms seek out the best network structures
quickly & cost effectively
 From there we bring in the “art” that mixes with the “science” to yield a highly actionable
understanding of what drives the target outcome – the dependent variable – in the
marketplace.
Traditional Approach
Approach with
BayesNets
Variables or
Factors
Independent
Variables or
Factors

‹#›
Siloed Regression Models Don’t Capture Indirect or Interaction Effects
Key Satisfaction/Loyalty Metric
ProductsAtmosphere
Store
Experience
Minutes in
line
Seconds
Ordering
Speed of
Ordering
Ordering
Process
Volume
Customer
Service
Greeted by
Employee
Friendly
Employees
EXPERIENCE
DOMAINS
PERCEPTIONS
OF BEHAVIORS
QUANTIFIABLE
MICRO-
BEHAVIORS
SUPPORTING
IMPRESSIONS
Metrics can impact other domains, not just those up the ladder in
our hierarchy

‹#›
The Former Best Approach: SEM
Structural Equation Modeling
 Tests Complex Structures
 Interactive & Indirect Effects
 Explanatory & Prescriptive
 Multicollinearity may still be a
problem
 Can only test the “fit” of
specifically hypothesized
networks
Siloed Regression Tree
 Forces Simple Structure
 Direct Effects Only
 Diagnostic & Descriptive

‹#›
Illustrative
Outputs

‹#›
Variables or Factors
Colours identify
“nodes” that belong
to different factors
Probabilistic
Relationship
“Arcs” connect the
various “nodes” in
the network
What does it look like? | It Starts With Networks

‹#›
BayesNets Satisfaction Key Drivers Analysis:
A Case Study
 Customer satisfaction surveys with >650,000 retail customers
 Surveys conducted throughout 2013
 Dependent variable: Overall quality of in-store experience rating
 Independent variables:
 Is a place for someone like me
 Clothing was neatly displayed and well organized
 The wait time in the checkout line was acceptable
 Service you received in the fitting room met your needs
 The cashier worked quickly and efficiently to check out all customers in line
 Your experience in the store was more fun and engaging than other stores you typically
shop
 The signs clearly indicated what was on sale
 Employees were easily accessible
 Employees were willing to find style, color, size
 Employees acknowledged and made you feel welcome
 Employees seemed genuinely glad you were there
 Overall clothing quality

‹#›
BayesNets Customer Satisfaction Drivers Analysis - Retail Example
Wait time at checkout
Feel welcomed
For someone like me
Overall clothing quality
Neat displays /organised
Sale signs clear
More fun and engaging
than other stores
Accessible employees
Employees glad
you were there
Cashier
worked
quickly and
efficiently
Employees willing to
find style, colour, size
Service received
in fitting room
met needs
Overall experience

‹#›
BayesNets “total effects” analysis looks & feels like standard drivers analysis
All Brands Retailer 1 Retailer 2 Retailer 3 Retailer 4 Retailer 5
Total Effects on Target Promoter_Rev Indexed Indexed Indexed Indexed Indexed Indexed
Standardized Standardized Standardized Standardized Standardized Standardized
Total Effects Total Effects Total Effects Total Effects Total Effects Total Effects
F1: MERCHANDISE FOR ME 144.3 134.3 159.5 137.4 150.9 133.1
F0: EMOTIONALLY ENGAGED 131.9 114.6 127.8 110.5 115.4 111.7
F17: GREAT VALUE 125.8 121.6 126.8 112.7 129.7 118.6
F4: EXCITING AND STYLISH MERCHANDISE 125.0 111.6 147.3 115.9 102.7 116.7
F5: QUALITY BRANDS 120.1 111.1 132.7 152.7 111.7 115.6
F3: GREAT FIT & SIZES 112.8 114.9 112.0 132.3 119.1 115.3
F7: GREAT PRICES AND SAVINGS 110.0 120.6 141.9 117.6 95.1 114.7
F14: MERCHANDISE FOR MY HOME AND FAMILY 109.8 107.1 146.2 75.6 128.3 105.7
F11: GREAT SALES 105.1 111.6 102.2 95.8 117.5 106.3
F2: ENJOYABLE SHOPPING 103.9 94.0 56.8 101.3 105.5 100.5
I can always count on STORE to have what I want on sale 102.9 103.2 104.2 69.5 109.1 104.4
F12: BETTER DEALS 101.0 108.3 126.9 99.9 97.2 102.0
F13: EASY RETURN POLICY 93.4 93.4 104.9 107.1 91.4 101.5
F9: PRICES I TRUST 80.6 78.5 98.0 61.9 90.8 74.7
F6: LOYALTY PROGRAM 79.0 78.7 56.3 59.3 52.2 75.2
F8: COUPONS 72.2 93.5 39.0 85.7 75.1 89.1
F16: INSPIRING DISPLAYS 70.0 78.7 26.6 120.5 74.3 95.7
F18: SUPPORTS MY COMMUNITY 61.9 61.5 68.8 71.7 72.7 73.0
F10: EASY PROMOTION 50.5 62.7 22.1 72.5 61.4 46.3
Illustrative

‹#›
The output here gives specific advice on which factors to affect first, and when it is optimal to focus on the
next factor.
Bayesian Analysis can also provide clear recommendations on
where businesses should focus
Initial Mean
Rating
Mean Rating
After
Improving
Preceding
Factors
Target
Mean
Overall Opinion
Mean
Initial Value 4.45
HEALTHY 9.07 9.27 4.79
SELECTION/VARIETY 8.64 8.84 9.05 4.88
QUALITY 8.25 8.40 8.59 4.91
EASY/SIMPLE 8.05 8.56 8.86 4.92
First, the goal is to
move the mean on the
Healthy factor from
9.07 to 9.27
This would increase
the Overall Opinion
0.34 points, from 4.45
to 4.79
Moving the Healthy
mean from 9.07 to 9.27
also affects
Selection/Variety,
moving it from 8.64 to
8.84.
Illustrative
Moving the Selection
mean from 8.84 to 9.05
similarly impacts both
the Overall Opinion
mean (up to 4.88) and
the Quality mean
(moving it to 8.40) and
so on for each
successive factor

‹#›
UK Media Owner Client: Live Case Study
Current data sources include:
• Brand Tracking with image metrics
• Industry audience measurement
• Content audit v competitors
• Content appreciation
• Social Media tracking
They tend to do a rough & ready comparison and they are doing some KDA in the tracking
data, but so far no joined up stuff
With so much data they worry about how much stakeholders trust or care about the data
‘I have an overload of data and metrics and I
want to see what combination of factors drive
audience growth (or decline).... At the moment I
can’t see the wood for the trees – I’m hoping
Bayesnet will help’
LRW are working with the client to
initially conduct a Bayesnet
analysis on the monthly tracker
(which goes back over 2 years) to
identify relationship between
behaviour and the metrics and
which ones are the ones that they
really need to look at
Ideal solution would be to
stream line the cumbersome
tracker – strip out metrics that
don’t add value and then look to
widen the Bayesnet analysis to
other data sources and conduct
a wider analysis

‹#›
Summary

‹#›
In Summary | Why BayesNet Modeling?
 BayesNets modeling is often more effective
than more traditional advanced modeling
of derived importance
analysis
 BayesNet measures both direct impact on the dependent
variables and indirect impacts through other independent
variables in the model
 BayesNets overcomes multicollinearity and makes no
assumptions of either normal distributions of data or linear
correlations between variables.
 BayesNets mathematics and software allow for quicker creation
of the model, optimizations and “what if” scenarios.
 BayesNets offers more effective optimization modeling with
target means to guide activation and appropriate levels of effort
and investment.

‹#›
If you’d like more info: We can set up a time for you to talk to one of our
genuine experts!
Mick
McWilliams
PhD, Sr. VP,
Marketing
Science
Marketing scientist specializing in
segmentation, brand engagement, database
scoring, SEM, KDA and BayesNets
25+ years of MR experience with specialties
in neuroscience studies & evolutionary
psychology
PhD, Sociology, Virginia Polytechnic Institute
& State University
Thank you!

‹#›
Graham Williams
Research Director, Europe
gwilliams@LRWonline.com
Lieberman Research
Worldwide
1, Heathcock Court, 415
Strand
London
WC2R ONT
www.lrwonline.com
Direct Line: 0203 551 7075
Contact Information

60
Space Optimisation is the process of maximising profit
by allocating the appropriate amount of store shelf
space to each product category

Typical client – a retail chain with a wealth of sales and loyalty club data
61
...of different locations
and demographic
profiles
Many hundred stores of
different sizes
Nearly 100
product
categories

Many aspects determine the profit that a product category will yield.
First, the most important ones are identified
Significant factors, in selection :
• Affluence in neighbourhood
• Gender profile
• Age profile
• Proximity to low-cost competition
• ...
• ...
• ...
62
Demography
Location / competition
Sales details
Customer
Satisfaction

Linear regression is used to isolate the relationship between space and
profit, per product category
63
Space allocated
...
...
Affluence in neighbourhood
Gender profile
Age profile
Proximity to low-cost
competition
...
...
β1
+ β2
+ β3
+ β4
+ β5
+ βs

”Space elasticity” – not the same for all product types, illustrated by an
intuitive example from a pharmacy
64
1 shelf
Margin: £500 per day
 £500 /shelf
4 shelves
Margin: £1000 per day
 £250 /shelf
+?
Margin
Space
Margin
Space
Due to its higher space elasticity,
it is likely more profitable to add
another beauty shelf than one for
pain killers. This despite the fact
that painkillers presntly give
more profit per shelf unit.

Store-specific linear regression gives accurate space elasticity curves in
steps, for each category
65
Shelf space allocated
...
...
Gender profile
Age profile
Proximity to low-cost competition
...
...
βs

Store-specific linear regression gives accurate space elasticity curves in steps,
for each category
66
0
20
40
60
80
100
120
0 20 40 60
Margin(£)
Space (Shelf sections)
...
...
Gender profile
Age profile
...
...
βs

for each category
67
0
20
40
60
80
100
120
0 50 100 150 200
Margin(£) Space (Shelf sections)
...
...
Gender profile
Age profile
...
...
βs

for each category
68
0
10
20
30
40
50
60
70
80
90
0 50 100 150 200
Margin(£) Space (shelf sections)
...
...
Gender profile
Age profile
...
...
βs

for each category
69
0
10
20
30
40
50
60
70
80
90
0 50 100 150 200
...
...
Gender profile
Age profile
...
...
βs

for each category
70
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50
...
...
Gender profile
Age profile
...
...

We will never start
adding delicassy
cheeses, since the start
of the curve is so flat.
Combining curves for the optimal space allocation – stepwise incremental
assignment doesn’t always find the best solution available
71
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10
Margin(£)
ABBBC
C
D
D
E
F
GG/HIJK
Store plan, 15 shelves
8
7
Stepwise adding products to shelves
using the highest incremental value at
each step will result in assigning 8 to
vegetables and 7 to sauces

The optimal distribution includes many shelves of delicassy cheeses, giving
a large profit at substantial space assignment
72
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10
Margin(£)
ABBBC
C
D
D
E
F
GG/HIJK
Store plan, 15 shelves
96

We search through all possible combinations to find the best one – an
enormous optimisation problem which we use logic to reduce
100 shelf units to allocate
73
30 categories
...
...
...
...
...
...
...
...
6 x 1028 combinations!
...
... Even this rather moderate number of shelf units and categories
presents an enormous number of potential combinations.
We need to use logic to reduce the computational complexity,
and find the best solution available.

An online tool is used for
space allocation, bespoke
to each individual store
74

Thank you!
kalle.backlund@nepa.com
0785-19 49 379
75

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