9th November 2017

1. MRS ADAN Symposium
Multiple Source Analytics
9th November 2017
#MRSlive

2. 2
Intelligent Benchmarking

3. 3
An approach to evaluating outlet-level performance based on its individual
circumstances and local operating environment / conditions…

4. 4
…facilitating
Performance Drivers Benchmarks Targets

5. 5
Every outlet is
similar…but different!
Different sizes, different
customer base, different staffing
levels, different formats etc…
Unfair to compare
outlets directly
An outlet with lower footfall and
excess staff will outperform an
outlet that is under pressure!
Need to control for
differences
We use statistical modelling to
level the playing field so that a
direct comparison is possible…

6. 6
Applications of Intelligent Benchmarking
Grocery Retailing
Store-level
Customer Satisfaction
Financial Services
Branch-level
Customer Satisfaction
Hospitality Industry
Hotel-level
Food & Beverage
Performance

7. 7
Key Factors
Branch Grade
# Staff
# Tills
Footfall
Opening Hours
Competitors
Location
Demographic Profile
Sales / Revenue
Branch Size
Transaction Volumes
Customer
Satisfaction
Hotel Size
Hotel Type
Occupancy
Business/Leisure Mix
Length of Stay
Competitors
Location
Demographic Profile
Avg Room Rate
Management Tenure
Customer Satisfaction
Food & Beverage
Performance

8. 8
Data
Range of variables to test means data
availability is not consistent – weekly,
monthly, quarterly etc.
Continuous data needs to be rolled up to
the relevant frequency used for modelling
Data on a number of factors will be point
in time and relatively static over time
Some data may shift seasonally
Typically use a single data point per factor
per outlet – latest quarter / 6 months /
year

9. 9
Modelling Approach
Pre-analysis, Data Visualisation &
Correlations help to form initial picture of
variable relationships and shapes
Typically, no data transformation
Linear multiple regression modelling
– Don’t tend to see exponential
relationships
– Ease of interpretation
– Other model forms are available!
Updates determined by client business /
reporting cycle

10. 10
THE MAGIC
LOYALTY
SCHEME
FORMULA
DOES EXIST
MR + BIG DATA
=
A GREAT
FUTURE

11. 11

12. 12
GENEROSITY
EFFORT
UTILITY
HIGHLOW
INTEREST
? = f(utility, generosity, effort)
?

13. 13

14. approach
uncovering
existing
knowledge
by analysing
historical
transactional
data
1
designing
a test plan
to test new
combinations
(in market)
using a CBC
approach
2
executing
the CBC
design in
live test
market
3
analysing
the test
market
data
+developing
simulator tool
4
upscale
results
with real
time data
5
timeline

15. 60%
40%
Uncovering existing knowledge
Identified drivers of
redemption through
regression modelling (type of
customer, type of
supermarket, time of year, …)
& response based sampling
Used as covariates in
CBC analysis in step 4


16. 16
EFFORT LEVELS
€
GENEROSITY
LEVELS
X
X X X
X XX
X
X
X
X
X
X
X
X
X
X X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
AND
ACCORDING
TO YOUR
PREVIOUS
BEHAVIOUR...
XCBC DESIGN
WITH TWO
ATTRIBUTES
+ one control group of consumers
with no points offered

17. 17
CBC DATA =
REAL
BEHAVIOUR
DATA
One CBC choice task per consumer:
a specific effort/generosity combination that can be
redeemed or not
Shopping data showed whether redeemed or not
(=answer to single CBC choice task) and level of
(extra) spend versus control group

18. 18
2,000,000
CBC DESIGN APPLIED ON
N =

19. 19
consumer
LOYALTY
retailer
COST
risks

20. 20
challenge 1
CATEGORIES
needed to be optimised 
complicated sampling plan
50

21. 21
STORE TYPES
challenge 2
3

22. 22
OTHER
challenge 3
PROMOS
in same test period

23. 23
challenge 4
SEASONALITY

24. Results example category
% redemption
(=preference share)
through HB modelling

25. 25

26. 26

27. 27
€€€
Simulator tool

28. 28
The future: from adhoc to automation

29. 29
CARREFOUR AND ITS
CUSTOMER
MR + BIG DATA

30. 30
THANK YOU
QUESTIONS?
Nicole Huyghe
nicole.huyghe@boobook.world
+32 472 96 18 40
+32 9 242 50 40
www.boobook.world
@boobook_world boobook_world

31. 31
Conjoint design details
CBC design with 2 attributes:
 number of points
 required spend on next basket (also called effort)
Not all combinations were
possible – as only realistic
scenarios were tested
Spread of CBC design over
sample: dependent on average
basket value of past 3 Months
= points/effort

32. 32
Conjoint analysis details
 HB estimation
 Full design split in 3 designs for
realistic results (i.e. to avoid
borrowing data from respondents
with too different shopping behaviour
– much lower or much larger
shopping basket values)
 Contraints on both attributes
 Given the little data per respondent (one CBC task)  a lot of data
borrowing required
 Looked at several PV and DF settings
Final choice: PV = 1, DF = half the sample size
3 sub designs
Each estimated
separately
through HB