The need for intelligent data-driven decisions on site selection has just received a turbo-charge, and the risk profile of making the wrong decision is now increased. Discover the fundamental data, analytical methods and insights needed to locate high-performing sites regardless of the retail industry segment. To optimize doesn’t always mean to grow; it can also mean identifying where to reduce physical investment and drive channel shift strategies. Learn how to make such data-driven retail decisions in the current, complex environment.

1. Retail Site Selection
in a World of Digital
Transformation
Presenter
Paul Thompson
Global Retail Business Development Executive

2. The need for intelligent data-driven decisions
on site selection has just received a turbo-charge,
and the risk profile of making the wrong decision
is now increased. Discover the fundamental data,
analytical methods and insights needed to locate
high-performing sites regardless of the retail industry
segment. To optimize doesn’t always mean to
grow; it can also mean identifying where to reduce
physical investment and drive channel shift strategies.
Learn how to make such data-driven retail decisions
in the current, complex environment.

3. • 25+ years of international
consulting experience. Currently
leading our Precisely professional
services practice in UK, Europe, and
North America, responsible for
strategy, innovation and services
delivery of location intelligence and
analytical solutions.
• Optimizing real estate portfolios
and omni-channel strategies.
• https://www.precisely.com/solution/financial-
services-data-solutions
• https://www.precisely.com/solution/data-
driven-solutions-for-retail
Presenter
Paul Thompson
Global Retail Business
Development Executive

4. Location Analytics and Data
Helping Organizations Make Better Decisions
Our dedicated team of client managers, data scientists and
software developers have 30 years of experience in creating
industry leading data sets and analytical solutions.
Select Financial Services, Restaurant and Retail Experience
Customer
Profiling and
Digital Marketing
Omni-Channel
Analytics
Market
Optimization and
Site Forecasting
Store/Product
Sales Goals and
Performance
Benchmarking

5. We are an established
leader in Location
Intelligence.
Recognized as a leader in 2016, 2018
and 2020 Forrester Wave Report on
Location Intelligence Platforms.
In the 2020 report, our Location
Intelligence platform scored the
highest out of all evaluated vendors in
the current product strategy category.
Precisely

6. We help allocate investments by setting
store strategies
Relocation
Candidate
Flagship / Lost Leader
Operational Issues
Control Expenses
Closure
Candidates
Maintain – Cash Cow
Short Term Lease
Long Term
Lease
Trade Area Quality
SiteQuality
Improved 4-walls profit
Targeted Investments
Healthier Portfolio
Confident Decision and
Deal Making

7. Set The Stage – Where We Were

8. Retail was simple
Aside from catalog or
call centers, you shopped
at a store. Coupons, flyer
inserts and telemarketing.
Methods to capture data
were labor and time intensive.
Scale was not possible.

9. Banking was simple
You opened a bank and people
came…but only during
banker’s hours
Banks held cash

10. Set The Stage – Where Are We Now

11. Months = Years
Recent advances in
the digital journey
81%
Smartphone
adoption 1.3 Trillion in 2020
10.4%
Growth in DX spending in 2020,
accounting for COVID impact!
Digital
IDC
Pew Research

12. 15.6 M June 2019
13.3M June 2020
Employed in Retail
1.6+ Million
Retail Establishments USA
90%
Percent of all retail transactions
taking place in physical stores - 2019
Retail
* https://nrf.com/insights/economy/about-retail-jobs
ICSC
NRF

13. 32%
Respondents fear robot chefs and
baristas will take their jobs post-Covid
1 M+
Restaurants in the USA
12M in USA
1.2M in Canada
Employed in Eating & Drinking
Restaurant
Restaurants Canada
National Restaurant Association
Sykes

14. -13%
Bank branch change since 2009
102,100
Total Locations in 2019
83K Bank and 19K CU
+28%
CU branch change since 2009
Bank and Credit
Unions USA

15. Digital Transformation
“… quite simple. It's adopting what are now becoming
mainstream digitization technologies such as IoT,
mobilizations, customer engagement, artificial intelligence,
data and analytics. How do you use those technologies to
improve the value of your product? Not just the value of
your product, but the value your customers get out of
your product or the value they get out of that service,"
said Rick Veague, CTO of IFS North America.

16. Site Selection
… something that needs to be put somewhere.
Site selection is the process of assessing the suitability
of a site, hopefully leveraging objective data and
unbiased analyses, and forecasting the financial
return of that investment.

17. Case Study
1956 to 2017 / 2019 and Chapter 11X2
• 5,000+ across multiple countries
• New business model - shop for shoes yourself.
• Fast expansion - heavy debt load
• Decline of the mall – loss of foot traffic
• Increased competition from discounters – Walmart, Target
• Increased online competition – Zappos
• “A visit to a Payless store became irrelevant” (Forbes)
• 700 international stores
2020 – Now ‘Payless’ – Brick-and-Click stores
• 300 to 500 stores in the next five years
• New e-commerce platform
• New prototype
• Omni channel approach – online and in-store
• Focus on CEX, in-store touchpoints with onsite digital components
– smart mirrors, touchscreen wall panels, AR shoe size in-store and
online purchasing (The Business Insider)

18. Fast delivery is no longer 2 days
Decreasing the last mile is
driving real estate change

19. Banks are Transforming from the
Traditional Branch Prototype
To Café's and Co-working
Spaces that Offer a Rich
Customer Experience and a
Location to Connect

20. Trends Impacting Site Selection

22. CX and omnichannel are top priorities in 2020
Most businesses have started their digital transformation
22.3%
32.2%
45.3%
49.0%
61.3%
0% 50% 100%
Mobile engagement
Single customer view
Digital transformation
Omni-channel communications
Customer experience
Top 5 Corporate Priorities 2020
Source: Aspire, The State of CCM-to-CXM Transformation, 2019
N = 512 enterprises (worldwide)
49%
of businesses are investing in
omnichannel communications
to increase customer experience (CX)
and realize higher business growth

23. What’s in Common
• ALL generations expect a blend
of both DIGITAL and PHYSICAL
channels as part of their ideal
communications mix
• ALL generations MOST enjoy
having access to a PERSON
who can help when they need it
• NO generation gives brands an
A for omnichannel engagement,
as ALL say brands are doing a
“PRETTY GOOD” job
• ALL have been
FRUSTRATED by the
lack of connection
between channels
• ALL have QUESTIONED
their relationship with
the brand because of
the frustration
Gen Z
<18
Born after 1997
Millinial
19-39
1981 - 1996
Gen X
40 - 54
1965-1980
Boomer
55 - 76
1946 - 1964
Silent
77 plus
1928 - 1945

24. The Omnichannel Expectation
Email Telephone Website Text In Person
86%
65%
53% 52% 48%
Top five expected channels of
connection and communication
The top channel consumers
couldn’t live without
28%
Telephone In Person
17%
13%
Email
13%
Text
12%
Social

25. Rise of the APP and
consumer data creators
Physical stores are still needed
on an Omni Channel world

26. The Industry is Transforming
Impact on the Industry Retail
Financial
Services
Restaurant
Retail Restaurant
Innovations in Mobile Trace Data and
Machine Learning
Widespread Consumer Use of Technology
Online Only Competition
Demographics are Changing
Retail Shaping Customer Expectations
Growth of Delivery Channel

27. How The Industry is Responding
Industry Responses Retail
Financial
Services
Restaurant
Retail Restaurant
Closing Mall Locations
Rationalizing The Number of Locations
Investing in Digital Channels
Remodeling and Altering Formats
Localization
Dark Stores / Ghost Kitchens

28. Other trends are driving site
selection decisions
Hollowing out of the middle class

29. Fundamental Data

30. Dynamic
Demographics
Dynamic Mobile
Trace
Site Attributes
• Attitudes, purchasing power,
political views, health, dining
preferences,… travel with
people as they move.
• Online/Call Center
• Mobile
• Take-out
• Dine-in/In Store/ATM
• BOPUS
• Delivery
• Drive-thru
• Digital activity patterns
• Trends
• Day of week
• Hour of day
• Period of year
• Location type (inline, endcap, free
standing, mall, kiosk,…)
• Accessibility/Signage/Visibility
• Parking
• Parcel size
• Drive-thru, sales and service amenities
• Capital investment history
• Efficiency Rating - Mystery Shoppers
Single View
of Truth
Dynamic POI
• Customers – Total Customer Value
• Assets – 4-walls metrics/profit
• Competitors – growth/contraction
change in amenities
• Centers – tenant mix, change
triggered co-tenancy clause
• Accurate
• Current
• Competitors
• Demand generators (shopping
nodes, traffic generators,…)
• Gentrification or decline triggers
Channel Data

31. Location data prep slows data science
Forbes, Mar 23, 2016
“Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says”
3%
60%
19%
9%
4%
5%
What data
scientists spend
the most time
doing
Building data sets
Cleaning and organizing data
Collecting data sets
Mining data for patterns
Refining algorithms
Other
Data preparation
accounts for about
80% of the work of
data scientists

32. Challenges for Data Science
Massive Scale
• Building Footprints:
Available for 100M+ US
properties
• Property Attributes: Across
3000 US counties
• People Data: Thousands
of Demographic Attributes
Unique Data Types
• Addresses, Lat/Lon,
Shapes, Lines, Formats
• Difficult to join different
Formats and Data
Types with accuracy
• Data types must be
modified to be used in
ML algorithms
Geospatial Calculations
• Computationally Intense
to Join and Enrich Spatial
Data at Scale
• Enriching with routing and
catchment (trade area) is
critical, but highly time
consuming

33. Data Management
• Keep addresses up to date by storing the PreciselyID,
then performing a reverse PreciselyID lookup to obtain
the latest address
Data Exchange
• Pass PreciselyIDs to business partners, customers, and
suppliers rather than addresses
Data Enrichment
• Unlock pre-processed reference data with a simple
lookup, no address matching or spatial calculations
required
Analytics
• Use multiple pre-process reference datasets to pre-score
the entire US, analyze trends, predict, and target.
PreciselyID – Speeds up Data Science

35. Dynamic Data
Demographics and POI + Mobile Trace

36. Intelligent combination of data sources
Household
Number of people, marital status
Ages, genders, ethnicities
Health
Education
Home
Housing type and tenure
Location
Neighbourhood
Pre-defined segmentation
Geodemographic based
Affluence
Lifestyle
Socio-economic
$£€
Economics
Income, disposable income
Purchasing power
Spend by category
Employment
Unemployment
MOBILE TRACE
Place: Where do people go?
Persistency: How frequently do they go there?
Period: How long do they stay there?
Path: Where do they go next?

37. Demographics vs. Geodemographics
Map 1: Single Person Households Map 2: Geodemographic Classification

38. Create a dynamic profile for a location
Inflows of Population: Mobile TraceOrigins of Flows to One Destination
Chart 1: Resident
profile
Chart 2: Resident
profile plus inflow

39. Residential vs. Daytime Profiled Locations
Residential locations: CAMEO Group 3 –
Prosperous Families
Daytime locations: CAMEO Group 3 –
Prosperous Families

40. Weekday Morning March: High Earners

41. Mobile trace data forecast
captured markets
CAMEO – Groups 1 & 2
Urban Affluent – Flourishing Professionals
CAMEO – Groups 9 & 10
On a Budget – Strained Society

43. These examples show the level of pedestrian activity at a micro grid/heat/cluster map level.
The grid shows the ‘busyness’ of the locations, by day and time as well as user type
(residential, worker, visitor) and profile type (purchasing power, age band)
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Digital Activity: Footfall Analysis

44. Essential Analytical Methods

45. Winners possess:
Single View of a Customer or Asset.
Ability to leverage data to pivot and
accentuate one channel over
another when needed.
37% increase website traffic
45% increase for emerging
brands due to presence of
new brick-and-mortar store.
ICSC

46. Ecommerce Sales Lift per Capita
by customer distance to new store
Lift = Performance delta as measured against control group (first 3 years of operation)
No Existing Store within 25 miles (First 12 Months)
One Store within 25 miles
No Existing Stores within 25 miles
EcommerceSalesLiftperHousehold
Multiple Existing Stores within 25 miles
* Precisely – Custom Reseach

47. Residential
Worker
Transient
Visitor
Shopper/Retail
Key Driver
Analysis &
Enrichment
Demand
AnalysisExclusions
Modelling
• Analog
• Regression
• Random Forecast
• Gravity
• Deep Learning Neural Nets
• Black Box vs Explainable
Network Optimisation
Businesses

48. Analytical Models in Use Today
$£€
Deep Learning / Neural Nets
• Good for complex problems with
non-obvious patterns
• Poor for needing “LARGE”
amounts of data to train model
and risk of overfitting
Gravity
Origin-destination based
flow models
• Good for scenario and
cannibalization impact
modelling
• Poor for needing detailed
spatial data not always
available
Random Forest
Flavor of the Day - Many
models not just one
• Good for handling large
messy complex datasets
• Poor on explainability and
forecasting outside existing
observed performance
Analog
Peer store review
• Good for traditionalists
• Poor for chains with low unit
counts or large chains expanding
outside core markets
Regression
Historically the most widely used
technique
• Good for explaining results
• Poor for complex non-obvious pattern
recognition
Scoring
Subjective weights driving the decision
• Good for encoding expert knowledge
• Poor for possibility of reinforcing
anecdotal/myths that have no basis
The best model to use
depends on the question
being asked.

49. What the Future Holds

50. Store Closures 2020
160 to 511,600 to 160 50 or Less

51. Resilience is Key - Ability to turn off
a channel and still thrive –
Convenient and accessible
restaurants will win.
Adapt to changing consumer
movement patterns, changes in lifestyle
preferences, competitors, and greater
numbers working from home

52. Percent of operators stated these
had a large positive impact on their
business:
50% - Location Intelligence
45% - Geofencing
Order food where you are!
52% of operators have remodeled to
add/enhance off-premise services.
Limited capital – invest where you
will get the best return.
New competitor
just opened
• QSR Magazine
• 2019 National Restaurant Association and Technomic,

53. From destruction comes creation!
Major chains will continue to grow
more rapidly, grabbing market share
–
More innovative and resilient brands
will take their place
Capital spending on hold for the
most part but winners are ready
to move on key properties.
Ensure you are able to quickly
evaluate a property when it
becomes available!

54. Stores are
here to stay
Creative destruction
will accelerate.
Expect 5+ years
to return to
Pre-COVID level.
Customers
expectations will
focus most on
convenience
• Physical stores will continue to
be enhanced with fulfillment
centers, pick-up counters,
easing the last mile
• Ship from store
• Increased online sales
changing the nature of
physical stores
• Technology is leveling the
playing field for small retailers
and restaurants
• Competition will intensify
• Differentiation will be key
30% drop in opex
is needed. Further
accelerating
digitalization
The need to make
accurate, data
driven decisions
quickly will intensify

55. Companies now need to be good at not just
buying and selling products, but also at things
like online fulfillment, home delivery, data
analytics, AI, machine learning, and process
automation. Given the current capability
shortages and cash flow challenges, retailers
should now be looking to refocus on the core
retail fundamentals of buying and selling while
partnering to deliver the other required skills.
Many are looking to platform companies to help
deliver some of those important capabilities.
Paul Martin, UK head of retail, KPMG

56. Q&A