Data-driven insight. Insight-driven outcomes.
Restaurant Analytics: Proof of Concept Case Study
Client Overview
Industry Challenges
Client Background
Colombo is flooded with a lot of restaurants, be it fine-dining, usual
dining and take-out joints, fast food restaurants or food trucks. This
boom in the industry has created a lot of challenges for the players in
the industry.
Our client has been in operation from the early 1940s, and have been
regarded as a leading restaurant. Coming in from the long-term success
of its operations, it has now opened four new branches.
Financial
When opening a new restaurant or expanding, large sums of capital is
needed to cover leasing a building, hiring a new wait and hostess staff,
stocking the kitchen and bar areas and buying furniture and decorations.
Most restaurants do not turn a good profit for several months or years
after opening.
Planning
To be successful, a restaurant needs a good location, a theme or style
that appeals to a broad range of customers and a solid menu.
Restaurants must stand out from the crowd, especially restaurants in
urban areas with high competition. Even established restaurant owners
must continually review their menu and theme to ensure they are giving
customers what they want.
Managerial
Many restaurants have a high staff turnover rate, which can lead to
scheduling problems and a stressful work environment.
Due to the flurry of new restaurants springing up within Colombo, the
restaurant outlets are having heavy competition. Our client had done
minimal changes in the past to its structure of menu items and this was
an acid test to revamp / rejuvenate and optimize their menu with the
customer in mind.
The restaurants have implemented sophisticated data capturing system
in place and wants to capitalize on collected information to improve
decision making processes which will help the client plan further
expansion, and also to re-engineer its processes with improving
performance as the main objective (main goals: increase revenues,
lean/cost-efficient operation, improve customer engagement).
This project was run for a period of six weeks, with an extensive analysis
run on our client’s data from their four existing branches at that point of
time.
The client is currently engaged with Argyle X on follow-up projects
Competition
A restaurant must know how to market its business, how to bring in new
customers and how to develop a repeat clientele.
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Actions
Need to engage
customer better
Actions
Insight
Few categories
drive revenue
 Consider possibility of having certain outlets as
Delivery/Take-Away only
 Assess network and coverage
Actions
Insight
Delivery is a key
revenue driver
 Need to examine footfall around location at specific times – e.g. if an
outlet is in the central business district, what is the potential footfall at
lunchtime? How can we better attract these customers
 Is parking facility an issue?
 Lunch Time Dine-In Promos – e.g. at certain outlets Take-Away is popular
at lunch time. Encourage more Dine-In with special offers on specific days
(as evidenced in the data).
 Credit Card offers for Dine-In – e.g. Visa accounts for 15% Dine-In
payments.
 Promote slow moving items via offers/consider removing
or modifying these items
 Menu Optimization (for further details see Slide 8)
Actions
Insight
Dine-In has highest
average ticket size but
is lagging at certain
outlets
Insight
Project Summary
 Loyalty scheme (for further details see Slide 5 and Slide 8)
 Social Media
 Customer Satisfaction Assessment – e.g. via surveys
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Project Overview
Increasing customer engagement within better customer profiling
Comprehensive assessment of restaurant using internal data and statistical analysis to improve sales
Determine KPIs of
success
Data Sources
Data Gathering & Baseline
Dataset Creation
Modeling Customer
Purchase Behavior
DL channel main
revenue driver
Data Extraction
Revenue Metrics
•
•
•
•
By Labor Hour
By Seat Hour
By Square Footage
Average Ticket Size
Outlet + Location
Analysis
Channel
Assessment
• Delivery (DL)
• Take Away (TA)
• Dine-In (DI)
Customer
data
What data resides
in what system?
Transaction
Data
Data Audit &
Cleaning
Inventory and
Costing data
Menu Item
data
Call Center
Data
• Does the data
make sense?
• Is the data
clean and
match across
systems
Data
Consolidation
Consolidating data
from all data
sources
Insights &
Recommendations
Model Drivers
of Revenue
Detailed
Descriptive
Statistics
DL/TA only outlets?
DI largest
average ticket
size
Outlet-based
strategies to
promote DI at
specific times
Few categories
drive revenue
Cluster
Analysis
Menu Optimization
Gap Analysis
Increase
Engagement
• Loyalty scheme
• Social Media
• Survey campaign
4
Selected Results – Part I
Gap Analysis: Benchmarking against Industry Standard Practices
Our analysis was limited to data provided for a period of 1 Month (18th May – 17th June). As a result,
weAreas
are unable
a holistic
picture
of the
performance of
However, scale,
we recommend
which to
theprovide
restaurant
is lagging
behind
– benchmarked
onCDC.
a comparable
i.e.
CDC
implement
these
next
steps,number
so that of
weoutlets
could provide
a complete
assessment of the CDC
restaurant
chains
with
similar
and reported
revenues.
provide more comprehensive solutions in the next phase of our engagement.
3
2
1
Social
Social Media
Media Analysis
Analysis Provides
Provides insight
insight into
into
customer
customer preference
preference and
and
gives
gives the
the ability
ability to
to
benchmark
benchmark performance
performance
against
against competitors.
competitors.
Potential
Potential to
to run
run promotions
promotions
Customer Survey – Assess
the
gapthe
between
Assess
gap between
experience and expectation.
Possibility to find reason
behind low dine-ins and
gain insight into customer
preference.
Loyalty CardCard useful in
Useful in
findingcustomer
specific
finding
specific
customer information.
information.
Enables the
Enables the
tailoring of and
tailoring
of promotions
promotions
and an inan
in-depth understanding
depth
understanding
of
of
customer
behaviour.
customer behaviour.
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Selected Results – Part II
Descriptive Statistics
Revenue Share by Outlet
All Revenues
Orders Share by Outlet
Outlet 1
All Orders
Outlet 1
Outlet 2
Outlet 2
Outlet 3
Dine In Revenues
Outlet 4
0%
20%
40%
60%
Outlet 3
Dine In Orders
80% 100%
0%
Revenue Per Square Foot By Outlet
20%
40%
60%
80%
100%
Dine In Ticket Size by Outlet
3000
3000
Outlet 1
Outlet 2
2000
Outlet 3
1000
Outlet 4
Average Ticket
(Rupees)
4000
Revenue Per Sq Ft
(Ruppees)
Outlet 4
Cumulative
0
Outlet
Outlet 1
2000
Outlet 2
Outlet 3
1000
Outlet 4
Cumulative
0
Outlet
A selection of results showing KPIs by outlet. Note that Outlet 1 leads in terms of revenue but
when taking into account the square footage it is no longer the best performing.
For the next step we narrowed our focus to the Dine-In channel
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Selected Results –Part III
Dine-In Channel Study
The findings indicate that ‘Dine-In’ (DI) is a channel that could use some focus as it has the highest average
ticket and could thus increase revenues. Across all outlets DI accounts for about 1/3rd of the revenue
Order by Channel: Revenue & Orders Share
Revenue Share
Dine In
Delivery
Take Away
Order Share
0%
20%
40%
60%
80% 100%
Then, we focused on Outlet 1 and Outlet 2. The DI channel at Outlet 1 is fairly ‘healthy’, while at the latter the
DI is lagging behind the other channels in terms of orders and revenue.
Share of Orders by Channel: Outlets 1 & 2
Outlet 2
Share of Revenue by Channel: Outlets 1 & 2
Outlet 2
Dine In
Dine In
Delivery
Delivery
Take Away
Outlet 1
0%
20%
40%
60%
80%
100%
Take Away
Outlet 1
0%
20%
40%
60%
80%
100%
We look to understand and target customers better by looking at the DI purchase patterns using
cluster analysis.
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Cluster Analysis Background
Data Selection and Methodology
o Random selection of DI orders from Outlet 1 and Outlet 2.
o There are 19 categories of items on offer. After the preliminary analysis we were able to reduce this
number to 10. The categories (in alphabetical order) are
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Beverage
Chicken
Combo Meals
Dessert
Noodles
Prawns
Rice
Soup
Squid
Vegetables
o Customers purchasing items from the same category have higher affinity to one another, similar nonpurchases do not carry as much ‘weight’.
o Three different clustering techniques were attempted:
 spherical k-means
 r-Neighbourhood
 k Nearest Neighbour (kNN)
o The best results were from the kNN-clustering method. The values chosen for k were k = 30 and k = 50. The
k = 30 results are presented next
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Selected Results – Part IV
Cluster Analysis and Statistics
The clusters are characterized by their purchases (listed in descending order). Interestingly the clustering
was able to pick out the non-purchase categories as well. The clusters are:
1.Beverage/Squid/Soup/Rice/Chicken/Noodles
2.Combo
3.Rice/Beverage/Vegetable/Prawn/Chicken
4.Dessert/Rice/Beverage/Chicken/Squid
5.Squid/Rice/Beverage (No Dessert)
6.Rice/Beverage (No Soup/Vegetable/Squid)
7.Soup/Rice/Beverage (No Chicken)
8.Rice/Vegetables/Beverage (No Prawn/Squid)
Cluster Percentage Contribution to Revenue
Outlet 2
Outlet 1
Combined
0%
20%
1
40%
2
3
4
60%
5
6
7
80%
100%
8
Cluster Size by Percentage
Outlet 2
Visiting Times of Top 4 Revenue Clusters
Combined
0%
20%
1
40%
2
3
4
60%
5
6
7
80%
8
100%
Cluster Number
Outlet 1
6
4
3
1
0%
20%
40%
Open - 3pm
3-6pm
60%
6-8pm
80%
100%
8pm - Close
9
Insights and Recommendations
Based on the results of the Cluster Analysis
•
•
•
•
Menu Optimization
Set-Menu Value based options based on high frequency/revenue
clusters (e.g. Cluster 3 Menu - Choose a Rice item, a Vegetable
item, a Prawn item, a Chicken item and a beverage for Rupees XX)
Time-based offers (e.g. Offer the ‘Cluster 3’ Set-Menu between 7
and 8 pm)
Smart Website Recommendations
When an item is selected, use the cluster analysis results to make
recommendations – e.g. ‘Goes well with XXXX’
Loyalty Program (LP) Offers
With a LP in place and customer purchase history, restaurant
would be in a position to make individual/mass offers based on
cluster
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Argyle X Engagement Process
Timeline for Project
Manage & monitor
Run
Set up
Week 1
Data Inventory
+ Q&A
Analytics
Solution for
Business
Problem
Week 2
Week 3
Week 4
Week 5
Week 6
Data
Selection
Model
Concept:
KPI &
Monitoring
Metrics
Data
Integration +
Data
Validation
Modelling
and
Statistical
Analysis
Model
Validation
Weekly deliverable review
Issue capture, prioritization & resolution
Customer Relationship Management
Results &
Findings
Contact Us
Head Office, Singapore:
133 Cecil Street #11-02,
Keck Seng Tower 069535,
Singapore
Tel: +65 (82) 334424
Delivery Center, Sri Lanka:
37 Bagatalle Road,
Colombo 3,
Sri Lanka
Tel: +94 (115) 666 111
Web
www.argylex.com
E-Mail
[email protected]
Linked In
https://gr.linkedin.com/company/argylex
Data driven insight. Insight driven outcomes.
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