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

3040

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 choiceSame brand, switching etc.

In context of price

Category choiceFrequency, consumption,

substitutes etc.

IndustryPast volumes,

substitutes, prices etc.

EconomyIncome, 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 ModelConsumer Research

• Brand shares

• Switching

EconometricMacro 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?

Adapting the Choice Model

The questioning technique uses FORCE principle

to make the consumer response more realistic…

FamiliarOnly 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

Occasion

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

Repertoire

Respondents allowed to select multiple brands, SKUs and units, as they might in real life

ChannelPrimary channel of purchase identified and specified in the questioning

Event Recurrence

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 EffectFor 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 EffectInteraction 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 FromBrand 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 statisticsOptions

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 statisticsAdopted 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 statisticsAdopted 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 outputMarket volumes

125000 tonnes

Current scenario

126250 tonnes

New price

scenario

1%

Bringing the twain together

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 tonnesShare

Market Volumes

49238 tonnesCompany volumes

Share change: 5%

Volume change: 6%

Proof of the pudding

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 choose4)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 5CHOICE:

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



Tuesday 512h February 2012, 18:46

Tuesday 12th February 2012, 13:05

Tuesday 12th February 2012, 08:38

Brand A, Online, Very Positive, Much More Likely to Choose

Brand C, Conversation, Very Negative, Much Less Likely to Choose

Brand E, TV, Fairly Positive, Slightly More Likely to Choose

Other, In store, Fairly Positive, Slightly more likely to Choose

Brand B, Mailing/Leaflet, Slightly Negative, No change

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 you saw? :

What was the purpose of your 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 Wk0Consideration Wk1



Freq. & Pos.Brand AdRetailer AdIn StoreWOM…



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 repeatedresponses, 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 effectDiminishing 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

communications1 =1

WOM =2 =1

Peer observation =2 =1

Retailer advertising =4 =1

Brand advertising =4 =1

Traditional earned 6 =1

Thank You!

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© 2015 Lieberman Research Worldwide.




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

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=QBPo0OmwbgcudM&tbnid=nwQHFB-H43Fl5M:&ved=0CAUQjRw&url=http://phillips.blogs.com/goc/2008/04/dimensions.html&ei=tjgvUvGlE-HxiwL5pIGwAQ&bvm=bv.51773540,d.cGE&psig=AFQjCNEsZZQwJovGUQOChjMVRY-jRVG_mg&ust=1378912819254817

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=QBPo0OmwbgcudM&tbnid=nwQHFB-H43Fl5M:&ved=0CAUQjRw&url=http://phillips.blogs.com/goc/2008/04/dimensions.html&ei=tjgvUvGlE-HxiwL5pIGwAQ&bvm=bv.51773540,d.cGE&psig=AFQjCNEsZZQwJovGUQOChjMVRY-jRVG_mg&ust=1378912819254817



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

BayesNetsVariables or

Factors

Independent

Variables or

Factors



Siloed Regression Models Don’t Capture Indirect or Interaction Effects

Key Satisfaction/Loyalty Metric

ProductsAtmosphereStore

Experience

Minutes in

line

Seconds

Ordering

Speed of

Ordering

Ordering

Process

VolumeCustomer

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 importanceanalysis

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

[email protected]

Lieberman Research

Worldwide

1, Heathcock Court, 415

Strand

London

WC2R ONT

www.lrwonline.com

Direct Line: 0203 551 7075

Contact Information

http://www.lrwonline.com/

Header

Copyright © Nepa All Rights Reserved

Space Optimisation

MRS Advanced Analytics

30 April 2015

Header


60

Space Optimisation is the process of maximising profit by allocating the appropriate amount of store shelf

space to each product category

Header


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

Header


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

Header


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

Header


”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.

Header


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

Header


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

Marg

in (

£)

Space (Shelf sections)


...

...


Gender profile

Age profile


...

...

βs

Header



for each category

67

0

20

40

60

80

100

120

0 50 100 150 200

Ma

rgin

(£

)Space (Shelf sections)


...

...


Gender profile

Age profile


...

...

βs

Header



for each category

68

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Marg

in (

£)

Space (shelf sections)


...

...


Gender profile

Age profile


...

...

βs

Header



for each category

69

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Marg

in (

£)



...

...


Gender profile

Age profile


...

...

βs

Header



for each category

70

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50

Marg

in (

£)



...

...


Gender profile

Age profile


...

...

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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

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0

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0 1 2 3 4 5 6 7 8 9 10

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in (

£)

AB

BB

C

C

D

D

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F

GG

/HI

JK

Store plan, 15 shelves8

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

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The optimal distribution includes many shelves of delicassy cheeses, giving

a large profit at substantial space assignment

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in (

£)

AB

BB

C

C

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D

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GG

/HI

JK

Store plan, 15 shelves96

Header


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.

Header


An online tool is used for

space allocation, bespoke

to each individual store

74

Header


Thank you!

[email protected]

0785-19 49 379

75

mailto:[email protected]

Business

MRS Advanced Analytics Innovation Symposium Presentation