decoding ratings for superior service in restaurants
TRANSCRIPT
A talk delivered atUnderstanding Consumers in Digital Era
IIM Lucknow, Noida Campus
DECODING RATINGS FOR SUPERIOR SERVICE IN RESTAURANTSUsing text to understand customers
BROAD AGENDA
• CHALLENGES OF DATA
• HOW TO TACKLE IT
• SOME TERMINOLOGY
• CASE STUDY : RESTAURANT + TEXT ANALYTICS
• WHY RATINGS ARE NOT HOLY
• LUNCHBOX – AN INTELLIGENT RESTAURANT APP
CHALLENGES
You are drowning in too much data – social channels, feedback forms, emails
Whom to trust? Reviews can be often contradictory/biased across channels
Difficult to maintain parity in customer experience across channels seamlessly
Offering personalized service and offers is not always possible
SOLUTION
Adopt a 360 degree approach
Read and understand the reviews – internal or external
Extract actionable insights for operational improvements
Unify your internal feedback with POS transactionsUnderstand what sets your
competition ahead
Personalized offers & services for each guest
Own an intelligent restaurant management system (RMS)
RATINGS – HOW MUCH IS BETTER ?
Increasing scope of differentiating operational improvements
Decreasing scope of customer loyalty
BRIEF TERMINOLOGY
You build an algorithm, machine learns patterns, machine predicts, rinse & repeat.
MACHINE LEARNING
TEXT ANALYTICS
Analyzing unstructured text, assign structure, load into a BI/program to visualize
PROBLEM STATEMENT
The client had thousands of customer reviews which they wanted to analyse - to understand customer feedback and identify improvement opportunities.
The broad questions we focused on;
What did they say about the restaurant?
Keywords & topics of discussion across the comments
What elements of the restaurant would they want improved? – service, staff behaviour, ambience etc.
When did the customer visit the store?
How is client’s traffic distributed over time?
Ticket sizes across multiple customer dimensions – age, gender, ratings, location, time of visit etc.
Overall customer sentiments & views about UCH
PRIMARY FOCUS AREAS SECONDARY FOCUS AREAS
APPROACH
Extract data and validate
Corpus from social media
Tokenise and remove stop
words
Initiate ML models , NER , parsers & topic
algorithms
Initiate detection rules for topics, keywords, gender, sentiment and multi-word
concept detection
Final Output
PRE - PROCESSING PARSING & ANALYSIS OUTPUT
Part of Speech (POS) Tagger
DATA SNAPSHOT
Bill No. Net Amount Membership No. Gender Profession Marital Status Date Rating Comment
SL-0220 678 EXXXXXX FEMALE SALARIED UNMARRIED 02-02-2013 5This is a fantastic, inexpensive
casual place to have delicious……
SL-0221 1202 EXXXXXX MALE SALARIED MARRIED 15-02-2013 4Great shakes and burgers. The
sandwiches…
SL-0222 707 EXXXXXX MALE SALARIED MARRIED 18-02-2013 3Very good food but the service is
slow.
SL-0223 791 EXXXXXX FEMALE SALARIED MARRIED 21-02-2013 4A friend steered me here for the
…..
SL-0224 619 EXXXXXX FEMALE SALARIED UNMARRIED 27-02-2013 3Bah! Below is my outdated review.
…..
TOPICS
TOPIC PERCENTAGE NUMBER OF RECORDS
Overall Visit Experience 47.0% 22,320
Service 24.7% 11,730
Taste/Quality 18.9% 8960
Recommendation 2.7% 1300
Referral/Loyalty 1.9% 920
Temperature (too hot/cold) 1.1% 530
Quantity 0.9% 420
Music 0.8% 400
Pricing (Too low/high) 0.6% 290
Drinks 0.6% 260
Options/Menu Choices 0.5% 250
Ambience 0.3% 150
TOPICS VS SENTIMENT
Negative Neutral Positive
Topic % # % # % #
Overall Visit Experience 10.5% 270 34.0% 2360 50.8% 19,310
Service 11.3% 290 20.6% 1430 27.6% 10,510
Taste/Quality 47.1% 1210 28.8% 2000 14.8% 5630
Recommendation 3.7% 260 2.7% 1040
Referral/Loyalty 1.2% 30 1.0% 70 2.2% 820
Temperature (too hot/cold) 10.5% 270 1.9% 130 0.3% 130
Quantity 3.9% 100 2.0% 140 0.5% 180
Music 8.9% 230 1.6% 110 0.2% 60
Pricing (Too low/high) 1.9% 50 1.7% 120 0.3% 120
Drinks 1.2% 30 2.2% 150 0.2% 80
Options/Menu Choices 2.3% 60 2.0% 140 0.1% 50
Ambience 1.2% 30 0.4% 30 0.2% 90
TOPICS – AC TEMPERATURE
Some of the randomly picked negative reviews on temperature were –
- A Remarks
- The Ac Was Too Cold
- Your Restaurant Is Too Cold
- Too Cold We Were Shivering
- Change The Music Style AC A Bit Too Cold
- Temperature Of The Restaurant Too Cold Air Conditioned
RATINGS ARE NOT HOLY
It’s not recommended to rely on the ratings alone– they tend to paint a different story than is.
A customer might give a rating 5, but deplore you in his review.
A quick look at reviews vs the actual sentiment of the text.
A sample review with rating of 4 ;
“Desserts Very Bad”
Rating (out of 5) Negative Neutral Positive
4 123 412 2,609
3 77 208 972
2 41 55 109
1 8 6 11
FINAL RECOMMENDATIONS
Improve speed of service
Redesign menu for easy read
Decrease portion size
Use ACs at ambient temperature
Hire more female staff
Expand beer selection
HOW WE DO IT ?
Single platform to analyse customer
reviews – from internal or social
channels
Actionable intelligence on
competitors and upcoming threats
Unified feedback management system – real time analysis of internal & social
feedback
Target customers with hyper-personalized
offers – both real-time and app-based
campaigns
OUR PLATFORM
10.7 Mn 92.6 K 62.6 K16reviews restaurants user profilestopics
As on 31st October, 2015