Top 10 Tips for Retail
Site Selection
Gerry Stanley
Product Management Director
Kyle Bingham
Principal Client Manager

Content
Data is at the centre of site selection activities. The top 10 tips cover
understanding your data inputs through to insightful uses of data to
create high value insights.
Gerry Stanley
Product Management Director
Precisely Enrich
Stacey Grant
Marketing Manager
Precisely
Kyle Bingham
Principal Client Manager
Precisely

The leader in data integrity
Our software, data enrichment products and
strategic services deliver accuracy, consistency, and
context in your data, powering confident decisions.
of the Fortune 100
99
countries
100 2,500
employees
customers
12,000
Brands you trust, trust us
Data leaders partner with us
3

Data
Integration
Data
Observability
Data
Governance
Data
Quality
Geo
Addressing
Spatial
Analytics
Data
Enrichment
Break down
data silos
by quickly
building
modern data
pipelines that
drive
innovation
Proactively
uncover data
anomalies and
take action
before they
become costly
downstream
issues
Manage data
policy and
processes with
greater insight
into your data’s
meaning,
lineage, and
impact
Deliver data
that’s accurate,
consistent, and
fit for purpose
across
operational
and analytical
systems
Verify,
standardize,
cleanse, and
geocode
addresses to
unlock valuable
context for more
informed
decision making
Derive and
visualize spatial
relationships
hidden in your
data to reveal
critical context
for better
decisions
Enrich your
business data
with expertly
curated datasets
containing
thousands of
attributes for
faster, confident
decisions

60%
19%
9%
5%
4%
3%
Cleansing & Organising Data
Collecting Datasets
Modelling/Machine Learning
Other
Refining Algorithms
Building Training Sets
5
What Data Scientists
spend most of their
time on
79% of time spent
on Data Prep Source: www.forbes.com

# 1 - The quality of location information

Geocoding
Turn business address
information into locations
Increased accuracy = increased
alignment with other internal
and external data
#1 The quality of location information

Resolution
#1 The quality of location information
1 SA3
9,100 km2
38 471 population
35,559 in 2016 – 8.2%
increase between Census
periods
2 SA2s
3 Postcodes
103 SA1s

Resolution
#1 The quality of location information
1 SA3
10.66 km2
56,398 population
56,066 in 2016 – 0.6% increase
between Census periods
4 SA2s 4 Postcodes 103 SA1s

# 2 - Data vintages and alignment

#2 Data vintages and alignment
2016 Versus 2021
Usually Resident Population
203 (9 August 2016 – 1 SA1)
5,728 (10 August 2021 – 16 SA1s)

#2 Data vintages and alignment
Alignment with more
frequent products

#2 Data vintages and alignment
Alignment and mis-
alignment
https://www.abs.gov.au/statistics/standards/australian-statistical-geography-
standard-asgs-edition-3/jul2021-jun2026/main-structure-and-greater-capital-city-
statistical-areas/changes-previous-edition-asgs

#2 Data vintages and alignment
Understanding the
alignment between data
and location

# 3 - Classify your location by a density
measure

Government Classifications
#3 Classify your location by a density measure
ABS remoteness index Urban Centres and Localities Destination Zones

Static density
#3 Classify your location by a density measure
Population Density Building Development in a Catchment Commercial Building Density

Selecting the best Density
Metrics to be used is
critical
What is the target – where people live
versus where people spend time?
SA1 Population Density and SA1
Population Centroid
Or
Demographic composition of visitors to
a region at different times of day/week
#3 Classify your location by a density measure

# 4 - Data Harmony, leverage datasets
across the business

Leveraging internal data
• Existing locations
• Performance
• Loyalty programs
• Transaction history
Leveraging external data
• The right data
• The right resolution
• The right currency
• Alignment to existing data
#4 Data Harmony

Broad Access
(singing from the same hymn sheet)
• Transparency where it makes sense
• Location assessment teams
• Decision makers
• Stock/product selection teams
Ease of Use
• Visualisation of complex data
• Dashboards with layers and filters
• Interactive maps
#4 Data Harmony

# 5 - Generate ongoing store survey data

Collection Methods
In-store survey kiosk
QR code surveys
Web/in app surveys
Email/text surveys
Survey Content Type
Net Promoter Score focused
Customer Experience/Satisfaction focused
Product/pricing focused
#5 Generate ongoing
store survey data
77%
of customers have a more favourable view of
brands that ask for and accept feedback.
Microsoft State of Customer Service Report
74%
Of Millennials receive too many emails
70% are bothered by irrelevant ones
Retail TouchPoints

# 6 - Understand your store maturity
before building any types of model

27
Older Locations
Newer Locations

Maturity Analysis
28
Maturity is a measurement of new-store comp growth attributed to its ‘newness/attractiveness’ and an
increase in consumer awareness of the store

Maturity Analysis – Example
29
72.6%
90.0%
100.0%
60.0%
65.0%
70.0%
75.0%
80.0%
85.0%
90.0%
95.0%
100.0%
Year 1 Year 2 Year 3
Maturity Ramp (% Mature)

Maturity Analysis
30
Year Maturity Ramp
1 72.6%
2 90.0%
3 100.0%

Maturity Analysis
31
Year Maturity Ramp
1 72.6%
2 90.0%
3 100.0%
24.0%
11.1%
Average new store growth above and
beyond mature store comp growth

# 7 - Understand where mobile trace data
fits in the customer data pyramid

Customer Data Pyramid
33
Mobile
Trace Data
Credit Card
Data
Other (e.g. in-store Post
Code capture)
Customer
Address Level
Transaction
Data
Loyalty Card
Data

Customer Data Pyramid
34
Customer
Transaction
Data
Mobile
Trace Data
Benefits
• Level of accuracy
• Can tie back to an evening
or daytime location

Customer Data Pyramid
35
Customer
Transaction
Data
Mobile
Trace Data
Benefits
• Level of accuracy
• Can tie back to an evening
or daytime location
Caution
• Greenfield sites
• Still is only a sample
• No sales amount are tied to
the visit

# 8 - Set realistic expectations on cannibilisation

Cannibalisation
• Highly situational; can be challenging to model
• We look for patterns by store type, by density, by
market type, etc.; review against our historical rules
• On-going research
• Two main approaches (Macro & Micro)

Macro Approach

39
-25.00%
-20.00%
-15.00%
-10.00%
-5.00%
0.00%
0 5 10 15 20 25 30 35 40 45
Sales
Impact
(%)
Distance (KMs)

40
-25.00%
-20.00%
-15.00%
-10.00%
-5.00%
0.00%
0 5 10 15 20 25 30 35 40 45
Sales
Impact
(%)
Distance (KMs)

41
-25.00%
-20.00%
-15.00%
-10.00%
-5.00%
0.00%
0 5 10 15 20 25 30 35 40 45
New store opened
up 10.3 KMs away
and impacted this
store 9.1%
Sales
Impact
(%)
Distance (KMs)

Micro Approach

43
Existing Store

44
Existing Store
New Store

45
Existing Store
New Store

46
Existing Store
New Store
55%
75%
65%
45%
50%
35%
35%
30%
10%
5%
2.5%
2.5%
5%
2%

Cannibalisation Accuracy
Goal:
+/- 2%
47

# 9 - Understand your modelling options and their
strengths and weaknesses

Presentation name
49
Analogue
AI/Deep Learning/Neural Networks

Artificial Intelligence - Example
50

4 Hours Later
51

Artificial Intelligence - Example
52

Model Overfitting
53
Overfitting is a concept in Data Science, which occurs
when a statistical model fits exactly against its training
data. When this happens, the algorithm unfortunately
cannot perform accurately against unseen data,
defeating its purpose.
An overfitted model is a mathematical model that
contains more parameters than can be justified by the
data.
Common issue with Machine Learning

Model Overfitting
54
Overfitting is a concept in Data Science, which occurs
when a statistical model fits exactly against its training
data. When this happens, the algorithm unfortunately
cannot perform accurately against unseen data,
defeating its purpose.
An overfitted model is a mathematical model that
contains more parameters than can be justified by the
data.
Common issue with Machine Learning

Model Overfitting
55
Characteristics
• Outliers exist
• Explainable
• Useful on for
new sites
Characteristics
• Few outliers; happy Clients
• Extremely difficult to explain
• Not sustainable on “new”
data; observations of 1

Retail Modelling - Tips
56
• Choose the right model for the right situation

Retail Modelling - Tips
57
• Choose the right model for the right situation
• Choose the right outcome ($’s vs. Score) for the
right situation
• Consider current store count
• Consider future format

Retail Modelling - Tips
58
• Choose the right model for the right situation
• Choose the right outcome ($’s vs. Score) for the
right situation
• Consider current store count
• Consider future format
• Avoid an over-reliance on AI…..for now

Retail Modelling - Tips
59
• Choose the right model for the right situation
• Choose the right outcome ($’s vs. Score) for the
right situation
• Consider current store count
• Consider future format
• Avoid an over-reliance on AI…..for now
• Complex networks may require more complex
models/data

Retail Modelling - Tips
60
• Choose the right model for the right situation
• Choose the right outcome ($’s vs. Score) for the
right situation
• Consider current store count
• Consider future format
• Avoid an over-reliance on AI…..for now
• Complex networks may require more complex
models/data
• It’s OK to have outliers, especially, if you can explain
them

# 10 - Use multiple methods to verify new site
forecasts

62

Combine Methodologies
Internal Review Committee
Regression-based Sales
Forecast Model with AI
Analyst Adjusted Forecast Field Team
63

Combine Methodologies
Internal Review Committee
Regression-based Sales
Forecast Model with AI
Analyst Adjusted Forecast Field Team
64
SITE APPROVED

www.precisely.com