Analytics in Action - How Marketelligent helped a Quick Service Restaurant chain identify customer...
2
Business Situation: Our client, a leading Quick Service Restaurant (QSR) with one of the largest restaurant chains across the globe, wanted to analyze their customer feedback surveys to generate weekly insights for action by Senior Management. The Task: Design and develop an automated system that will categorize text from customer feedback surveys into various broad themes highlighting key areas of concern. Framework: Customer feedback text was parsed into words/phrases and based on their frequency of occurrence, combined using cosine distances method into clusters. A combination of Silhouette Score & Gap Statistic was used to identify the right number of clusters. Themes of the cluster were then understood by looking at word clouds of unigrams, bigrams & trigrams. Based on this clustering exercise, word clouds for various ‘themes’ were generated and analyzed to highlight areas of opportunity and concern Analytics in Action Leveraging Text Analytics to Identify Customer Pain Points Client: A Leading Quick Service Restaurant Chain The Result: • Identified high priority areas of concern on a weekly basis which if not taken proper care of, might result in customer churn • Highlighted features that customers liked and which could be scaled across regions to give Customers a seamless experience • Better targeting of discounts and coupons were possible by clustering price-conscious regions • Top burning issues identified by region / store, and weekly reports generated and distributed to senior management for corrective action Themes Count % Price / Service tax & Ambience 1358 26% Food taste not good 802 15% Order takes long time 747 14% Overall Experience 582 11% Small food piece 574 11% Food is not hot 522 10% Card Machine not working 493 9% Staff not friendly 440 8% Items not available 405 8% Not enough counters open 175 3% Table not clean 174 3%
-
Upload
marketelligent -
Category
Business
-
view
105 -
download
3
description
How Marketelligent helped a Restaurant chain analyze their Customer Feedback Surveys using Text Analytics to identify customer pain points
Transcript of Analytics in Action - How Marketelligent helped a Quick Service Restaurant chain identify customer...
Business Situation:
Our client, a leading Quick Service Restaurant (QSR) with one of the largest restaurant chains across the globe, wanted to analyze their
customer feedback surveys to generate weekly insights for action by Senior Management.
The Task:
Design and develop an automated system that will categorize text from customer feedback surveys into various broad themes highlighting
key areas of concern.
Framework:
Customer feedback text was parsed into words/phrases and based on their frequency of occurrence, combined using cosine distances method
into clusters. A combination of Silhouette Score & Gap Statistic was used to identify the right number of clusters. Themes of the cluster were
then understood by looking at word clouds of unigrams, bigrams & trigrams. Based on this clustering exercise, word clouds for various
‘themes’ were generated and analyzed to highlight areas of opportunity and concern
Analytics in Action Leveraging Text Analytics to Identify Customer Pain Points
Client: A Leading Quick Service Restaurant Chain
The Result:
• Identified high priority areas of concern on a weekly basis which if not taken proper care of, might result in customer churn
• Highlighted features that customers liked and which could be scaled across regions to give Customers a seamless experience
• Better targeting of discounts and coupons were possible by clustering price-conscious regions
• Top burning issues identified by region / store, and weekly reports generated and distributed to senior management for corrective action
Themes Count %
Price / Service tax & Ambience 1358 26%
Food taste not good 802 15%
Order takes long time 747 14%
Overall Experience 582 11%
Small food piece 574 11%
Food is not hot 522 10%
Card Machine not working 493 9%
Staff not friendly 440 8%
Items not available 405 8%
Not enough counters open 175 3%
Table not clean 174 3%
YOUR PARTNER FOR
DATA ANALYTICS SERVICES
MANAGEMENT TEAM GLOBAL EXPERIENCE.
PROVEN RESULTS.
Roy K. Cherian CEO Roy has over 20 years of rich experience in marketing, advertising and media in organizations like Nestle India, United Breweries, FCB and Feedback Ventures. He holds an MBA from IIM Ahmedabad.
Anunay Gupta, PhD COO & Head of Analytics Anunay has over 15 years of experience, with a significant portion focused on Analytics in Consumer Finance. In his last assignment at Citigroup, he was responsible for all Decision Management functions for the US Cards portfolio of Citigroup, covering approx $150B in assets. Anunay holds an MBA in Finance from NYU Stern School of Business.
Greg Ferdinand EVP, Business Development Greg has over 20 years of experience in global marketing, strategic planning, business development and analytics at Dell, Capital One and AT&T. He has successfully developed and embedded analytic-driven programs into a variety of go-to-market, customer and operational functions. Greg holds an MBA from NYU Stern School of Business
Kakul Paul Business Head, CPG & Retail Kakul has over 8 years of experience within the CPG industry. She was previously part of the Analytics practice as WNS, leading analytic initiatives for top Fortune 50 clients globally. She has extensive experience in what drives Consumer purchase behavior, market mix modeling, pricing & promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.
ADVANCED ANALYTICAL SOLUTIONS
MARKETELLIGENT, INC. 80 Broad Street, 5th Floor, New York, NY 10004
1.212.837.7827 (o) 1.208.439.5551 (fax) [email protected]
CONTACT www.marketelligent.com
Industry Business Focus Tools and Techniques
Consumer Finance Investment Optimization SAS, SPSS, R, VBA
Credit Cards Revenue Maximization Cluster analysis
Loans and Mortgages Cost and Process Efficiencies Factor analysis
Retail Banking & Insurance Forecasting Conjoint analysis
Wealth Management Predictive Modeling Perceptual maps
Consumer Goods and Retail Risk Management Neural Networks
CPG & Retail Pricing Optimization Chaid / CART
Consumer Durables Customer Segmentation Genetic Algorithms
Manufacturing and Supply Chain Supply Chain Management Support Vector Machines
High Tech OEM’s Sentiment Analysis
Automotive
Logistics & Distribution
