3. What is a Retail Audit?
• Study of a
selected
sample of
retail outlets
• May be
continuous
• May be
discrete
(limited
usefulness)
4. Goals
• Monitor
products
• Products are
yours or
competitors'
• Compliance
with agreed
procedures and
standards
5. The Survey
• As with any survey, it
can’t be too complicated.
• Quality of the data is
critical.
• Mystery shopping is
not always best...
• New data types:
• Images
• Video
• Sound
6. What Devices Will Be Used?
• Connectivity - how are you
getting the data back?
• WiFi
• Data networks
• Tablets
• Good screen size - may feel
heavy after hours of use....
• Small vs Large tablets
• Cost
• Smartphones
• Small screen size
• Many people have them
already
7. • Make sure the
survey fits the
device!
• Smartphones
are not tablets.
• The more concise
the better - long
preambles and
questions are not
useful.
8. New Data Types
•Images
• Very useful to document any
aspect of a retail environment
• Can replace store based
assessment
• Images can be stored and
“scored” at a later time
• Objective measurement of
improvements/
degradations
• But: scoring and
assessment can be time
consuming.
9. New Data Types
•Video
• Same properties as images.
• Capture richer information
than images.
• Can give snapshots of
customer behavior/traffic.
•Sound
• Acoustic environment -
announcements, noise,
customer comments
10. Bar Codes
• Can scan bar
codes for
resolution in real
time or later
• Gives definitive
information
about products
quickly
11. Possible Data
• Coupons/promotions available
• Merchandising and presentation
• Store Condition inside/outside
• Staff and training
• Product availability and display
• Service assessment - speed, quality
13. Store Sample
• Type
• Territory
• Sales region
• Vertical
• Types of store i.e. liquor
• Competitive
• With you !
• Random
• As the name implies - any store
If audit is done on a continuous basis, the sample should be the same.
14. Data Collection
• Who collects the audit information ?
• Expert - someone who understands the business space and
environment.
• Expensive but data quality may be higher
• May have intrinsic biases
• Hired help - someone hired to carry out the survey
• Cheap - but quality of data may be very variable
• Lack of knowledge may inhibit data quality.
• Consistency
• “Interviewer” effects are real.
• Being inconsistent with collection methods compromises the data.
15. Values
• Questions can have a scores 1 - 5 for instance:
• “Floors are clean” 1 being the lowest value 5
the highest
• When scores for 1 - 5 scale questions are summed
set a percentage score for “pass/fail” audit.
• 80% + usually.
• Yes/No questions
• Set a level for how many are “failed”
16. Reporting and Analytics
• Audiences
• Store/Regional
Managers
• Segmentation/
Aggregation of
stores/scores
• Execs
• Overall reporting,
with segmentation