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

Did you know? Total traffic to restaurant review sites exceeds 100 million per month in India.

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)

Increase footfalls and repeat visits for you !

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

CASE STUDY

HOW WE HELPED A RESTAURANT SERVE THEIR CUSTOMERS BETTER

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

FOCUS AREAS

TOPICS KEYWORDS SENTIMENT POINT OF SALES

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 – OUR GENERIC MODEL

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

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

TOPICS – AC TEMPERATURE

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

DEMO

LUNCHBOX – AN INTELLIGENT RESTAURANT APP

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

QUESTIONS ?

MANAS RANJAN KAR

manas@jsm.email

+91-9971 420 188

www.unlocktext.com