Types of customer feedback, how easy are they to collect, analyse and how insightful are they?
Why analyzing customer feedback is important?
Why is it hard to analyze free-text customer feedback?
What approaches are there to make sense of customer feedback (manual coding, word clouds, text categorization, topic modeling, themes extraction) -- and what are their limitations?
Which AI methods can help with the challenges in customer feedback analysis.
Speaking to reporters can be daunting, even for CEOs and founders who've done it dozens of times. Each interview takes a certain amount of preparation and practice to make sure your messaging is on point for a given reporter's beat and area of interest.
But, with a little preparation and some practice scenarios, even the most timid among us can be become experts are briefing the press.
To get started, take a look at Media Training 101.
What Is Customer Effort Score and How Do You Measure CES?Kayako
What is customer effort score (CES)?
This metric shows how much effort the customer thinks they had to put in to have their problem resolved. It’s a survey question “How easy was it for you to get your problem solved?” (scale of 1 to 5)
Why should you measure customer effort scores?
Knowing your CES allows you to see what needs to be done to improve the way your support team interacts with your customers.
It is a strong predictor of future customer loyalty – those with high effort scores are less likely to become return customers.
Learn everything you need to know about customer service metrics: https://blog.kayako.com/customer-support-metrics/
Speaking to reporters can be daunting, even for CEOs and founders who've done it dozens of times. Each interview takes a certain amount of preparation and practice to make sure your messaging is on point for a given reporter's beat and area of interest.
But, with a little preparation and some practice scenarios, even the most timid among us can be become experts are briefing the press.
To get started, take a look at Media Training 101.
What Is Customer Effort Score and How Do You Measure CES?Kayako
What is customer effort score (CES)?
This metric shows how much effort the customer thinks they had to put in to have their problem resolved. It’s a survey question “How easy was it for you to get your problem solved?” (scale of 1 to 5)
Why should you measure customer effort scores?
Knowing your CES allows you to see what needs to be done to improve the way your support team interacts with your customers.
It is a strong predictor of future customer loyalty – those with high effort scores are less likely to become return customers.
Learn everything you need to know about customer service metrics: https://blog.kayako.com/customer-support-metrics/
There is clearly a need for quick and accessible customer support online, and we think that live chat support is the answer. If you already do have it, we congratulate you – you’re well on your way to more effective customer engagement and increased sales. We’ve pulled together the top 3 tips (in our humble opinion) that we reckon would help you to make the most of your live chat software, and really aid you in connecting with your customers.
Passengers in an aircraft do get angry at the least provocation. In the Indian context, the Indian passengers have a mentality that when they buy an airline ticket they think that they have purchased the crew along with the ticket and their behavior inside the aircraft turns very ugly and many a times very nasty. This presentation will help the cabin crew in the Indian aviation scene,immensely.
What is NPS? It stands for Net Promoter Score. Learn what it means, what it can do for your business, how to get the data, and how to turn it into your score.
This slide deck explores using FAB statements to help people deal with buying objections based on price. This can be used for a Sales Team or in a Retail setting to help employees build their confidence.
How to create a Lead Engagement Strategy? Sudip Samaddar
Marketing is passing all the lead to you. There after sales leaders have to take these lead ahead till the closure. At every stage there is different engagement strategy. What are the some of the best practices?
Entity-Relationship Extraction from Wikipedia Unstructured Text - OverviewRadityo Eko Prasojo
This is an overview presentation about my PhD research, not a very technical one. This was presented in the open session of WebST'16 Summer School in Web Science, July 2016, Bilbao - Spain.
Natural Language Processing and Graph Databases in LumifyCharlie Greenbacker
Lumify is an open source platform for big data analysis and visualization, designed to help organizations derive actionable insights from the large volumes of diverse data flowing through their enterprise. Utilizing both Hadoop and Storm, it ingests and integrates virtually any kind of data, from unstructured text documents and structured datasets, to images and video. Several open source analytic tools (including Tika, OpenNLP, CLAVIN, OpenCV, and ElasticSearch) are used to enrich the data, increase its discoverability, and automatically uncover hidden connections. All information is stored in a secure graph database implemented on top of Accumulo to support cell-level security of all data and metadata elements. A modern, browser-based user interface enables analysts to explore and manipulate their data, discovering subtle relationships and drawing critical new insights. In addition to full-text search, geospatial mapping, and multimedia processing, Lumify features a powerful graph visualization supporting sophisticated link analysis and complex knowledge representation.
Charlie Greenbacker, Director of Data Science at Altamira, will provide an overview of Lumify and discuss how natural language processing (NLP) tools are used to enrich the text content of ingested data and automatically discover connections with other bits of information. Joe Ferner, Senior Software Engineer at Altamira, will describe the creation of SecureGraph and how it supports authorizations, visibility strings, multivalued properties, and property metadata in a graph database.
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
There is clearly a need for quick and accessible customer support online, and we think that live chat support is the answer. If you already do have it, we congratulate you – you’re well on your way to more effective customer engagement and increased sales. We’ve pulled together the top 3 tips (in our humble opinion) that we reckon would help you to make the most of your live chat software, and really aid you in connecting with your customers.
Passengers in an aircraft do get angry at the least provocation. In the Indian context, the Indian passengers have a mentality that when they buy an airline ticket they think that they have purchased the crew along with the ticket and their behavior inside the aircraft turns very ugly and many a times very nasty. This presentation will help the cabin crew in the Indian aviation scene,immensely.
What is NPS? It stands for Net Promoter Score. Learn what it means, what it can do for your business, how to get the data, and how to turn it into your score.
This slide deck explores using FAB statements to help people deal with buying objections based on price. This can be used for a Sales Team or in a Retail setting to help employees build their confidence.
How to create a Lead Engagement Strategy? Sudip Samaddar
Marketing is passing all the lead to you. There after sales leaders have to take these lead ahead till the closure. At every stage there is different engagement strategy. What are the some of the best practices?
Entity-Relationship Extraction from Wikipedia Unstructured Text - OverviewRadityo Eko Prasojo
This is an overview presentation about my PhD research, not a very technical one. This was presented in the open session of WebST'16 Summer School in Web Science, July 2016, Bilbao - Spain.
Natural Language Processing and Graph Databases in LumifyCharlie Greenbacker
Lumify is an open source platform for big data analysis and visualization, designed to help organizations derive actionable insights from the large volumes of diverse data flowing through their enterprise. Utilizing both Hadoop and Storm, it ingests and integrates virtually any kind of data, from unstructured text documents and structured datasets, to images and video. Several open source analytic tools (including Tika, OpenNLP, CLAVIN, OpenCV, and ElasticSearch) are used to enrich the data, increase its discoverability, and automatically uncover hidden connections. All information is stored in a secure graph database implemented on top of Accumulo to support cell-level security of all data and metadata elements. A modern, browser-based user interface enables analysts to explore and manipulate their data, discovering subtle relationships and drawing critical new insights. In addition to full-text search, geospatial mapping, and multimedia processing, Lumify features a powerful graph visualization supporting sophisticated link analysis and complex knowledge representation.
Charlie Greenbacker, Director of Data Science at Altamira, will provide an overview of Lumify and discuss how natural language processing (NLP) tools are used to enrich the text content of ingested data and automatically discover connections with other bits of information. Joe Ferner, Senior Software Engineer at Altamira, will describe the creation of SecureGraph and how it supports authorizations, visibility strings, multivalued properties, and property metadata in a graph database.
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
“Semantic PDF Processing & Document Representation”diannepatricia
Sridhar Iyengar, IBM Distinguished Engineer at the IBM T. J. Watson Research Center, presention “Semantic PDF Processing & Document Representation” as part of the Cognitive Systems Institute Group Speaker Series.
Natural Language Processing (NLP) practitioners often have to deal with analyzing large corpora of unstructured documents and this is often a tedious process. Python tools like NLTK do not scale to large production data sets and cannot be plugged into a distributed scalable framework like Apache Spark or Apache Flink.
The Apache OpenNLP library is a popular machine learning based toolkit for processing unstructured text. Combining a permissive licence, a easy-to-use API and set of components which are highly customize and trainable to achieve a very high accuracy on a particular dataset. Built-in evaluation allows to measure and tune OpenNLP’s performance for the documents that need to be processed.
From sentence detection and tokenization to parsing and named entity finder, Apache OpenNLP has the tools to address all tasks in a natural language processing workflow. It applies Machine Learning algorithms such as Perceptron and Maxent, combined with tools such as word2vec to achieve state of the art results. In this talk, we’ll be seeing a demo of large scale Name Entity extraction and Text classification using the various Apache OpenNLP components wrapped into Apache Flink stream processing pipeline and as an Apache NiFI processor.
NLP practitioners will come away from this talk with a better understanding of how the various Apache OpenNLP components can help in processing large reams of unstructured data using a highly scalable and distributed framework like Apache Spark/Apache Flink/Apache NiFi.
Pipeline for automated structure-based classification in the ChEBI ontologyJanna Hastings
Presented at the ACS in Dallas: ChEBI is a database and ontology of chemical entities of biological interest, organised into a structure-based and role-based classification hierarchy. Each entry is extensively annotated with a name, definition and synonyms, other metadata such as cross-references, and chemical structure information where appropriate. In addition to the
classification hierarchy, the ontology also contains diverse chemical and ontological relationships. While ChEBI is primarily manually maintained, recent developments have focused on improvements in curation through partial automation of common tasks. We will describe a pipeline we have developed for structure-based classification of chemicals into the ChEBI structural classification. The pipeline connects class-level structural knowledge encoded in Web Ontology Language (OWL) axioms as an extension to the ontology, and structural information specified in standard MOLfiles. We make use of the Chemistry Development Kit, the OWL API and the OWLTools library. Harnessing the pipeline, we are able to suggest the best structural classes for the classification of novel structures within the ChEBI ontology.
Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language
Increases in capital and labor are no longer driving the levels of economic growth the world has become accustomed to and desires. Fortunately, a new factor of production is on the horizon, and it promises to transform the basis of growth for countries across the world.
Accenture analyzed 12 developed economies and found that AI has the potential to double their growth rates by 2035.
The Bright Future of Market Research Smartees WorkshopInSites on Stage
This is the full slidedeck of our Smartees Workshop on 'the Bright Future of Market Research' (11 February, 2014). The main focus is on how both traditional quantitative and qualitative research can be better, fresher and more contemporary by approaching participants and internal stakeholders differently.
To segment effectively, you need to understand what drives the segments, not just how to measure them. That's where qualitative insight comes in.
Please credit the author if you use the material. Some images are subject to copyright.
A mini workshop designed to prepare teams with the knowledge and practice they need to better understand their problems and project gaps, determine appropriate participants, ask the right qualitative questions, and gather information in an unbiased and thoughtful way.
Taking Care of Your Campus Customers - An Inclusive Approach to Customer Servicemichellebaker
These slides were used with the "Taking Care of Your Campus Customers" workshop, delivered at Ball State University in September 2014.
Workshop facilitated by Michelle Baker, phase(two)learning
phasetwolearning.com - phasetwolearning@gmail.com
How to Gather Useful, Usable Customer Satisfaction FeedbackNaomi Karten
Do you know what your customers really think? Many organizations have woefully inadequate processes for gathering customer satisfaction feedback – processes that lead to distortion and misinterpretation rather than useful, usable information. In this presentation, I focus on key issues in planning, designing, gathering and using customer satisfaction feedback, and review some of the most blatant feedback-gathering flaws as well as some of the most subtle ones. I also describe the interesting approaches some organizations have used to gather feedback. This presentation includes numerous examples of what to do – and what not to do – to gather meaningful, actionable feedback.
I gave this presentation to an undergraduate Design Research class at the University of Kansas, taught by Julia Eschman and Tamara Christensen, in March 2011. It focuses on the importance of finding the right people to drive insights for ethnographic/design research, and addresses tactics for doing so.
Recruiting is a key part of the design research process that often does not get the attention it deserves, to the detriment of project outcomes. I invite you to share your experiences and questions, to build a dialogue about this topic!
Emotional engagement: The magic ingredient in any customer experienceMary Brodie
These are the slides from a Webinar about the impact of emotions on decision making and customer engagement. To hear the complete webinar presentation, go to: https://gearmark.lpages.co/sign-up-for-cx-magic-ingredient-emotions/
From Dissastisfied to Evangelist: How to Measure and Manage Your Clients’ Sat...Nex.to
Why client satisfaction is important. What the biggest obstacles are to measuring it. How can using one question can improve your efforts. Where to use your response data to improve your service delivery.
The complete guide to increasing quality and quantity of survey responsesXoxoday
In an attention-hungry world, people ignore surveys for several reasons. But, it doesn’t have to be this way. Today, every stakeholder, be it a researcher, marketer, or a CXO understands the importance of good quality surveys and the role incentives play in boosting response rates.
Good survey design can assuage and resolve several challenges faced by surveyors across the globe.
Xoxoday has created this guide to teach you the best practices for creating surveys, walk you through the various reward systems, and show how a leading market research company automated survey incentives to achieve its business goals.
The guide is all about how surveyors can create quality surveys that respondents would like to fill. It has the following sections:
Reasons why people ignore surveys.
Best practices to conduct surveys.
How to maximize ‘Show Rate’ with rewards.
To differentiate between manual and automated systems for rewards.
How Nielsen used Xoxoday Rewards to automate survey rewards.
Reward ideas for your surveys.
Removing survey response bias.
How LinkedIn fuels their NPS insights using AIAlyona Medelyan
LinkedIn is the world's largest professional network with close to 660 million users in 200 countries. In addition to the core product, it's also known for its solutions for sales, hiring, marketing and learning. One of the ways in which LinkedIn's market research team gains insights into perceived member/customer value is through NPS surveys. Beyond the core NPS metric, the team asks critical open-end questions to identify specific product likes and pain points in order to help drive prioritization.
Because of the large volume of feedback and the speed at which these insights must be delivered, LinkedIn chose to automate coding of open-ended questions in NPS surveys. Learn of the methodology used to get deep insights from NPS data and the results this approach enabled.
KiwiPyCon 2014 talk - Understanding human language with PythonAlyona Medelyan
Introduction into Natural Language Processing:
- Fiction vs Reality
- Complexities of NLP
- NLP with Python: NLTK, Gensim, TextBlob
(stopwords removal, part of speech tagging, tfidf, text categorization, sentiment analysis
- What's next
Introduction to NLP with some practical exercises (tokenization, keyword extraction, topic modelling) using Python libraries like NLTK, Gensim and TextBlob, plus a general overview of the field.
Presentation at the HCIR-2011 workshop by Anna Divoli (University of Chicago) and Alyona Medelyan (Pingar). Title: Search interface feature evaluation in biosciences
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
5. Actual Responses
Two costly & unnecessary referendum followed. Outcome: NZ kept the current flag
Millions could have been saved!
People wanted to ”keep the current flag”
6. 1. Types of customer feedback
2. Why analyzing customer feedback is important
3. Why is it hard
4. Approaches
5. Applying AI to customer feedback analysis
6. Demo
8. Types of Customer Feedback
one-on-one interviews / focus groups
call centre logs / complaints
social media
open-ended survey questions / reviews
quantitate survey questions
UX tests / analytics
unstructured
structured
9. Collection Analysis Insight
one-on-one interviews / focus groups hard hard good
call centre logs / complaints easy hard limited
social media easy hard limited
open-ended survey questions / reviews easy medium good
quantitate survey questions easy easy limited
UX tests / analytics medium easy limited
unstructured
structured
Comparing Types of Customer Feedback
18. Sarcasm is Hard:
Even People Struggle
I’ll keep it in
mind
They’ll do it
I’ve
forgotten
already
19. Sarcasm is Rarer Than You Think
Dataset Sarcasm Example
NPS Survey 1%
I’m so disappointed! What a great
customer service you have!
Social Media
comments
5% Very helpful answer. Troll.
22. How many ways there are to say
‘wet paper’?
Challenge 2: Synonyms and Paraphrases
23. Hundreds of
possible variations
of the same theme
wet
dripping
soaking
soaked
damp
drenched
paper
papers
newspaper
news paper
newspapers
news papers
+
Paraphrasing the Same Theme
24. Challenge 3: Negation
Positive or Negative?
My coffee was great positive
My coffee was awful negative
My coffee was not great negative
My coffee was not that great neutral?
I did not think my coffee was great negative
I did not expect my coffee to be this great positive
I was disappointed with the quality of the coffee negative
I was not disappointed with the quality of the coffee positive
27. Figure out the Code Frame, Apply, Repeat
What is the meaning of life?
1 2 3 4 5
What is the meaning of life?
42
Friends and family
Making a difference in the world
Happiness
Finding happiness
To achieve, to conquer
Family
…
What is the meaning of life?
42
Friends and family
Making a difference in the world
Happiness
Finding happiness
To achieve, to conquer
Family
…
1
2
3
4
4
5
2
28. Sentiment in a Manual Code Frame
Customer Service
Positive Negative
Timely Nice Helpful Didn’t fix issue Rude
29. Word Clouds
2.
“Every time I see a word cloud presented as insight,
I die a little inside.”
– J. Harris, journalist
30. Word Clouds Lack
Interpretation, Context, Meaning
“Overall the language
focuses on sweeping
statements focusing on
the state of the nation.”
Kalev Leetaru (Forbes)
33. It’s Hard to Find a Rule That Works Well
I was impressed by how friendly the person
on the other end of the line was
Staff friendliness ✔
The lady who helped me was friendly Staff friendliness ✔
Friendliness of staff Staff friendliness ✔
Your website is very user friendly Staff friendliness ✘
The young man on the phone was very pleasant Other ✘
friendly OR friendliness –> Staff friendliness
46. Deep Learning
Precision Recall F-Measure Errors
People 84 73 75 <1
Dictionaries 61 57 54 8
Linear Regression 65 56 47 3
Deep Learning 62 57 49 2
Sentiment Analysis is not about maximizing F-Measure,
it’s about reducing true Errors: positive confused with negative