Bij Teleperformance helpen we klanten waarde toe te voegen aan het klanttraject. We gebruiken Data Science voor onze Omnichannel-klantinteracties om de behoeften van de klant te voorspellen, zodat we het beste antwoord kunnen geven.
Seamless Journey for A Requisition to Pay Processaccenture
In this joint Accenture and SAP® Ariba® presentation we explore the potential benefits of implementing a procurement to claims service and the simplicity of managing a seamless and integrated procurement process.
Seamless Journey for A Requisition to Pay Processaccenture
In this joint Accenture and SAP® Ariba® presentation we explore the potential benefits of implementing a procurement to claims service and the simplicity of managing a seamless and integrated procurement process.
Accenture Labs details a five-stage journey to data industrialization and unlocking new opportunities. Read more: https://www.accenture.com/us-en/insights/technology/data-industrialization
Join Mindmatrix's Kevin Hospodar and SiriusDecision's Kathy Freeman Contreras, as they discuss-
-The most effective means for delivering lead generating programs that achieve the highest adoption rates and ROI
-How you can drive better engagement and marketing performance with partners
-The 4 phases of the SiriusDecisions Fast-Tracking Channel Demand Model
-How you can prepare for a successful sales enablement project
The report HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED Company Profile is a detailed document covered company’s Overview, History, SWOT Analysis, Products/Services, Facts, Financials, Key Executives, Competitors, Tech Intelligence, IT Outsourcing, IT Management, Recent Developments and Strategy Evaluation.
Avail Sample of the report for more information @
https://www.researchcosmos.com/request/harman-international-industries-incorporated-company-profile-overview-history-swot-analysis-pr/2317093
Legacy HR systems & processes have a significant impact on Diversity Hiring. Headstart & Accenture are bring a new proposition to market focused on significantly improving Diversity Hiring.
Crafting the Modern Manufacturing Enterprise in the Post-COVID-19 WorldCognizant
To get ahead in the industrial space amid the prolonged pandemic, manufacturers must embrace holistic agility and resilience, and democratize access to applications and data. This will eliminate operational silos at last and free data to more effectively inform everything: just-in-time build and logistics decisions, operational execution, customer experience product engineering decisions and everything in between, driving innovative product launches and much-needed cost reductions.
The Work Ahead: How Data and Digital Mastery Will Usher In an Era of Innovati...Cognizant
In this installment of our Work Ahead series, we focus on the impact of digital transformation on the life sciences industry and what it will take to transform an industry value chain in need of drastic modernization.
Competing for the inbox is tough. That's why, we're constantly evolving our process to challenge the perception of best practices. To move beyond what the experts say, you'll have to embrace the creative and the tech.
Is striving for best practices enough? If everyone is following best practices, isn't that average or the benchmark? Bulldog's own Chief Creative Officer, Brian Maschler, provides his suggestion on how to achieve above average results and shares some of Bulldog's key strategies to make your outbound efforts more effective and more relevant to your customer.
Accenture Labs details a five-stage journey to data industrialization and unlocking new opportunities. Read more: https://www.accenture.com/us-en/insights/technology/data-industrialization
Join Mindmatrix's Kevin Hospodar and SiriusDecision's Kathy Freeman Contreras, as they discuss-
-The most effective means for delivering lead generating programs that achieve the highest adoption rates and ROI
-How you can drive better engagement and marketing performance with partners
-The 4 phases of the SiriusDecisions Fast-Tracking Channel Demand Model
-How you can prepare for a successful sales enablement project
The report HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED Company Profile is a detailed document covered company’s Overview, History, SWOT Analysis, Products/Services, Facts, Financials, Key Executives, Competitors, Tech Intelligence, IT Outsourcing, IT Management, Recent Developments and Strategy Evaluation.
Avail Sample of the report for more information @
https://www.researchcosmos.com/request/harman-international-industries-incorporated-company-profile-overview-history-swot-analysis-pr/2317093
Legacy HR systems & processes have a significant impact on Diversity Hiring. Headstart & Accenture are bring a new proposition to market focused on significantly improving Diversity Hiring.
Crafting the Modern Manufacturing Enterprise in the Post-COVID-19 WorldCognizant
To get ahead in the industrial space amid the prolonged pandemic, manufacturers must embrace holistic agility and resilience, and democratize access to applications and data. This will eliminate operational silos at last and free data to more effectively inform everything: just-in-time build and logistics decisions, operational execution, customer experience product engineering decisions and everything in between, driving innovative product launches and much-needed cost reductions.
The Work Ahead: How Data and Digital Mastery Will Usher In an Era of Innovati...Cognizant
In this installment of our Work Ahead series, we focus on the impact of digital transformation on the life sciences industry and what it will take to transform an industry value chain in need of drastic modernization.
Competing for the inbox is tough. That's why, we're constantly evolving our process to challenge the perception of best practices. To move beyond what the experts say, you'll have to embrace the creative and the tech.
Is striving for best practices enough? If everyone is following best practices, isn't that average or the benchmark? Bulldog's own Chief Creative Officer, Brian Maschler, provides his suggestion on how to achieve above average results and shares some of Bulldog's key strategies to make your outbound efforts more effective and more relevant to your customer.
Seen differently, best practices are a race to an average. Maybe it's time to rethink your email strategy and challenge the status quo. Because, innovation happens when you try new things.
The next generation in customer engagement - Coginov Semantics search -Emmanuel Perdikis
interactive semantics search to understand your customers question and grow your understanding of what your customer is asking for and what you may be failing to deliver on your communications, offerings or website.
Cross channel testing insights and recommendations - jump - oct 12 2011 - w...Craig Sullivan
This presentation is about how to enable your website to optimise the *rest* of your business. There are recommendations here for how to instrument your web analytics, so you can see cross channel contact and touch points.
I've researched this thoroughly, and also give you a handy list of all the web analytics/call tracking companies of note. You'll find plenty of experiments to run with your call centre, and advice on how to track all of this crazy omni-channel and screen world.
If you do ONE thing with this information, promise me this please - I will implement tracking for some of my major 'other' channels that web drives, including phone, contact forms or chat.
Increase conversion by Andy CrestodinaAnton Shulke
we examine top mistakes made by web-pros when designing client websites, which factors hurt conversion, and what you can do to get better results.
Dive into visitor psychology, ideas that can significantly impact engagement, and watch Andy review sites submitted in real-time, based on his wealth of experience in content and design.
General Manager at ResellerClub, Shridhar Luthria gives you a closer look at the Internet and where the market is today. He covers the key drivers, and how you can be a growth hacker & benefit.
CustomerCommunity_hidsdzdzdzzzdzdz819.pptxVkrish Peru
Community cloud
Technologies Based on Artificial Intelligence:
Machine Learning: A subfield of AI that uses algorithms to enable systems to learn from data and make predictions or decisions without being explicitly programmed.
Natural Language Processing (NLP): A branch of AI that focuses on enabling computers to understand, interpret, and generate human language.
Computer Vision: A field of AI that deals with the processing and analysis of visual information using computer algorithms.
Robotics: AI-powered robots and automation systems that can perform tasks in manufacturing, healthcare, retail, and other industries.
Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain.
Expert Systems: AI systems that mimic the decision-making ability of a human expert in a specific field.
Chatbots: AI-powered virtual assistants that can interact with users through text-based or voice-based interfaces.Technologies Based on Artificial Intelligence:
Machine Learning: A subfield of AI that uses algorithms to enable systems to learn from data and make predictions or decisions without being explicitly programmed.
Natural Language Processing (NLP): A branch of AI that focuses on enabling computers to understand, interpret, and generate human language.
Computer Vision: A field of AI that deals with the processing and analysis of visual information using computer algorithms.
Robotics: AI-powered robots and automation systems that can perform tasks in manufacturing, healthcare, retail, and other industries.
Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain.
Expert Systems: AI systems that mimic the decision-making ability of a human expert in a specific field.
Chatbots: AI-powered virtual assistants that can interact with users through text-based or voice-based interfaces.Technologies Based on Artificial Intelligence:
Machine Learning: A subfield of AI that uses algorithms to enable systems to learn from data and make predictions or decisions without being explicitly programmed.
Natural Language Processing (NLP): A branch of AI that focuses on enabling computers to understand, interpret, and generate human language.
Computer Vision: A field of AI that deals with the processing and analysis of visual information using computer algorithms.
Robotics: AI-powered robots and automation systems that can perform tasks in manufacturing, healthcare, retail, and other industries.
Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain.
Expert Systems: AI systems that mimic the decision-making ability of a human expert in a specific field.
Chatbots: AI-powered virtual assistants that can interact with users through text-based or voice-based interfaces.Technologies Based on Artificial Intelligence:
Machine Learning: A subfield of AI that uses algorithms to enable systems to learn from data and make predic
Learn how to build ridiculously compelling sales decks based on super tactical examples from industry leaders, so you can put it into practice immediately and start winning deals!
AWS Summit Webinar Edition | Modern Data Architecture | Microsoft Application...Amazon Web Services
- Fast Start with AWS (L100) | Working Backwards from the Customer
-Move it! Migrating to AWS (L200) | Innovating SAP the Easy Way Migrate it to AWS
-Move it! Migrating to AWS (L200) |Secrets of Successful Cloud Migrations
-Thông tin chuyên sâu về khách hàng và Machine Learning (Cấp 200 – 300) | Tìm hiểu khách hàng của bạn Cấu trúc dữ liệu hiện đại
-Hãy thay đổi! Chuyển đổi sang AWS (Cấp 200) | Chuyển đổi & hiện đại hóa các ứng dụng Microsoft truyền thống có bộ chứa
-Chuyển đổi sang AWS (Cấp 200) | Quản lý dự án chuyển đổi DB – Quy tắc thực tiễn tốt nhất
Expertise Hour: The Dos and Don'ts of Web Chat with Johan JacobsMoxie
Web chat is quickly becoming the preferred communication channel for today's online consumer. When implemented correctly, web chat also has one of the highest satisfaction ratings among all online channels. How can you ensure your initiative meets or exceeds your goals?
Similar to Teleperformance - Smart personalized service door het gebruik van Data Science (20)
Centric - Jaap huisprijzen, GTST, The Bold, IKEA en IENS. Zomaar wat toepassi...BigDataExpo
Tijdens deze presentatie wordt duidelijk hoe je machine learning kunt toepassen in het dagelijks leven. Denk aan het kopen van een huis, het kijken van Goede Tijden Slechte Tijden, shoppen bij IKEA en het bezoeken van restaurants.
In this session we'll dive into the journey that Google chooses to take in order focus on AI: what was the mindset, what were the challenges and what is the direction for the future.
Pacmed - Machine Learning in health care: opportunities and challanges in pra...BigDataExpo
The potential of personalized medicine based on machine learning is huge, but big challenges must be overcome to implement this technology in practice. Hidde will discuss both sides of the story, including a case study on the intensive care.
De Toekomst Verkenner is een ‘award winning’ innovatie van PGGM, die in een rap temp doorontwikkeling naar een platform maakt.
In zijn presentatie zal Mladen Sančanin vertellen hoe PGGM real time data en algoritmes heeft ingezet om dit platform te bouwen en hoe PGGM innovaties vanuit haar ‘Big Data Lab’ ondersteunt?
In een half uur worden veel ervaringen gedeeld over het opzetten van innovatieprojecten gebruik makend van data en het inrichten van data lab in een corporate omgeving.
Universiteit Utrecht & gghdc - Wat zijn de gezondheidseffecten van omgeving e...BigDataExpo
Het GGHDC onderzoekt wat de gezondheidseffecten zijn van omgeving en leefstijl in relatie tot het dagelijks leven van mensen. Het onderzoekscentrum is opgebouwd rond een gedeelde data- infrastructuur van de Universiteit Utrecht en het Universitair Medisch Centrum Utrecht (UMCU).
Rob van Kranenburg - Kunnen we ons een sociaal krediet systeem zoals in het o...BigDataExpo
IoT, Big Data, AI creëren een nieuwe situatie met betrekking tot het nemen van beslissingen door beleidsmakers. Toch verschuift er weinig in ons democratisch bestel, terwijl onze data in handen zijn van GAFA, China en andere nieuwe vormen van bestuur die nog ontstaan in de digitale transitie. Wij, in Europa, staan stil.
OrangeNXT - High accuracy mapping from videos for efficient fiber optic cable...BigDataExpo
Construction companies such as BAM Infra Telecom rely on accurate, up-to-date maps. Google Maps isn’t enough, but doing on-site surveys is expensive and time-consuming. However, driving through and recording 360° video from a car is cheap and easy. Using machine learning, we turn videos into highly accurate maps.
Dynniq & GoDataDriven - Shaping the future of traffic with IoT and AIBigDataExpo
Dynniq is a high-tech, innovative company offering smart mobility solutions and services internationally. We will present advanced IoT use cases Dynniq is working on, and share how GoDataDriven helps set up an AI capability. We will share our learnings, and show what makes data science in the mobility domain unique.
FunXtion - Interactive Digital Fitness with Data AnalyticsBigDataExpo
Digital is the new Personal. FunXtion Interactive is een interactieve trainingservaring voor zowel binnen als buiten de sportschool. FunXtion is revolutionair in de fitness branche en volledig data driven, by design. FunXtion laat zien hoe zij real-time data gebruiken voor ondersteuning van beslissingen, proces automatisering, personalisatie en product innovatie.
fashionTrade - Vroeger noemde we dat Big DataBigDataExpo
Big Data was de verzamelnaam voor alles wat je nog niet deed, maar al wel door Google of Amazon was uitgevonden. Inmiddels doen we al die dingen wel dus heet productaanbevelingen weer gewoon productaanbevelingen, fraudebestrijding weer fraudebestrijding, en spraakherkenning nog steeds spraakherkenning; geen Big Data. Geeft niet, want nu is er AI. Deze keynote legt uit of dat anders is, en waarom.
BigData Republic - Industrializing data science: a view from the trenchesBigDataExpo
What does it take to bring machine learning algorithms to production and start delivering business value? How can teams of data scientists and engineers effectively collaborate on a single product, integrate with existing IT systems and keep business stakeholders involved? Using real-life examples, we discuss the challenges and best practices.
Bicos - Hear how a top sportswear company produced cutting-edge data infrastr...BigDataExpo
Industry expert Dave Vanhoudt will set out his vision for the future of data infrastructure. Dave will highlight the key role automation must play in any data infrastructure strategy today, drawing on his current role with Medtronic, and past experiences at AB Inbev, Baxter, BMW and Nike.
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Digitaal is vrijwel alles meetbaar. Maar het is vaak een uitdaging om de impact van samenwerkingen tussen influencers (topsporters) en bedrijven te analyseren. Start-up Endrse gebruikt AI om socialmediacontent te analyseren om content van influencers en bedrijven beter op elkaar te laten aansluiten. Zo maak je impact bij het publiek!
Bovag - Refine-IT - Proces optimalisatie in de automotive sectorBigDataExpo
De ontwikkelingen in de automotive sector gaan snel: elektrisch rijdende auto’s, de snelle groei van private lease, over the air connectiviteit, services on the demand en advanced driver assistance is zo maar een greep uit deze ontwikkelingen. Voorbeelden van (big) data ontwikkelingen die van grote invloed zijn op de automotive retail. De transitie naar een nieuw verdienmodel daagt uit tot samenwerken en datagedreven procesoptimalisatie.
Wilco Schellevis, directeur van Refine-IT en Renate Weggemans, manager strategie en beleid, bij BOVAG Autodealers, nemen u mee in de case Dely-App. Een mooi staaltje samenwerken en datagedreven procesoptimalisatie in de automotive retail; gevangen in één app.
Schiphol - Optimale doorstroom van passagiers op Schiphol dankzij slimme data...BigDataExpo
Schiphol is Europa’s best connected airport en verwerkt op piekdagen tot 235.000 passagiers. Om deze soepel door de processen te leiden is een betrouwbare prognose van de drukte noodzakelijk. Schiphol laat zien hoe zij datatoepassingen ontwikkelt om het aantal reizigers zo accuraat mogelijk te voorspellen en hiermee processen in te richten.
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Veco is marktleider op het gebied van het ontwerpen en vervaardigen van precisie delen middels electroformeren. In deze presentatie zal uitgeleverd worden hoe Veco succesvol Process Mining heeft ingezet in de productie om doorlooptijd te reduceren en new business te creëren. Tevens wordt uitgelegd wat Process Mining is.
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
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In zijn presentatie gaat Frans Feldberg in op het ‘Waarom, Wat, en Hoe’ van big data en datagedreven business model innovation. Hoe is de wereld, als het om data gaat, de laatste jaren veranderd? Waarom zijn big data, business analytics en kunstmatige intelligentie belangrijke digitale innovaties die hoog op menig managementagenda staat en waarom investeren organisaties aanzienlijk in big data en data science? Hoe kunnen organisaties waarde met data creëren door zowel het verbeteren van het bestaande business model als door nieuwe data-gedreven business modellen te ontwikkelen. Dit zijn vragen die in zijn presentatie beantwoord zullen worden.
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At Booking.com we have experienced what a data driven organisation means for creating business impact. And what looks it like, when experimentation is part of your company culture.
During this session we will share our experiences and learnings on how data science and experimentation go hand in go.
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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.
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.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
3. Through our omnichannel
customer experience capabilities,
voice, e-mail, chat, click-
to-call, social media,
video chat, automation,
face-to-face, and other
channels that your
customers use.
we interact every year by
VOICE
EMAIL
WEB FORM
CHAT W/
LIVE
AGENT
SOCIAL
MEDIA
MOBILE
APP
SMS
INSTANT
MESSAGING
AUTOMATION
FACE-
TO-FACE
VIDEO KIOSK/
VIDEO TELLER/
VIDEO CHAT
CLICK-
TO-CALL
4. 4
Over the past 40 years we
have worked
tirelessly to build the
largest Customer
Experience
company on the planet.
We know how to create
great & trusted service
moments for the
customers of our clients.
223Kpeople
We are a team of
350facilities
76countries
Present in
160markets
Serving
265
We provide
service in
languages and
dialects
In 2017
Revenue of
$4.720bn
€4.180bn
5. Tom Mac-Kenzie
Digital Project Manager,
Teleperformance BLX
Tom.Mackenzie@Teleperformance.com
+31 (0)6 514 653 89
• Current: Digital Project Manager, Bus. Development
• Previous: Senior Manager, Google BLX Project
Sales Manager, Google BLX Project
Manager New Teams, Google BLX Project
• Based in Tilburg, NL
• 7+years experience in outsourcing industry
• Created Messaging-Playbook based on experiences with local and global
partners. Created Competitor-Matrix to assess key-players in Messaging
Software. Created and pitched client-specific Messaging Pitches to more than
20 Clients/Prospects. Researched and created main differences narrative
between Chat & Messaging.
• Specializes in providing effortless Customer Experience for enterprises through
Instant Messaging & other innovations within customer contact
• Extensive background within online Ad Sales, SEA, Sales & People Management
• Specialties: Instant Messaging | WhatsApp Business | Facebook Messenger |
Customer Contact | Sales | Effortless Customer Experience | Innovation |
Online Marketing | e-Commerce | WhatsApp | Social Messaging | In-App
Messaging
Helping companies deliver an effortless customer
experience through Instant Messaging Solutions
A passionate online professional with a clear focus on achieving positive ROI
and an effortless Customer Experience across all channels.
Over 7 years proven experience in Customer Contact, Instant Messaging,
Effortless Customer Service, Advertising, e-Commerce, Online Marketing &
Sales Management.
Teleperformance Experience: 7 Years
Relevant Industry Experience
9. 9
THE CHALLENGE
The costs of handling the channel Social Messaging are too
high. The growth of the messages is massive and because of
that, the workforce grows accordingly.
A large number of questions asked by customers have
been answered before, using this data would reduce time
and effort for the agent, but also increase the satisfaction
of both the customer and the employee.
10. 10
Goal
Reduce number of interactions and
time spent to get from question to
answer
Maintain customer satisfaction and
save time.
THE CHALLENGE
Question
Answer
12. The data science life-cycle
Assumptions were made to define question and answer patterns
Question
Answer
Data formatting
§ Define question and
answer
§ Move from “message”
format to “conversation”
format
13. The data science life-cycle
Language complex to model, but we have found solutions
Data cleaning
§ Product tagging
§ Text cleaning
§ Stop words
§ Synonyms
§ Spelling mistakes
Wasmachine Wasapparaat
washingmachineTAG
Kapot Stuk
Text vectorization
§ Bi-Grams
§ Term Frequency – Inverse
Document Frequency
Dimensionality reduction
§ Principal Component Analysis
15. Initial clustering
Once the product is know, we cluster the specific questions
Product tag
clustering
Question
level
clustering
Cluster
checks
Model
answers
Cluster
filtering
All
Smartphone
Screen
display
Broken
screen, fixing
cost
Other
TV Cut-off
28%72%
26%
Product tag
clustering
Question level
clustering
74%
36
1361
16. Initial clustering
The clusters are checked, to validate that there is only one question in them
Product tag
clustering
Question
level
clustering
Cluster
checks
Model
answers
Cluster
filtering
22. Self-learning algorithm
Matching in cluster
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
23. Self-learning algorithm
Select model answer
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
24. Self-learning algorithm
Extend existing cluster
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
25. Self-learning algorithm
Select answer of most similar question
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
26. Self-learning algorithm
Start new cluster
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
27. Self-learning algorithm
Reject and write custom answer
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
28. Self-learning algorithm
(optional) remove from cluster, add observation to history
Database containing historic
conversations
New conversation
Agent options
Give model answer
Use answer of most similar question
Reject and write custom answer
31. Self-learning algorithm
Matching outside cluster
Database containing historic
conversations
New conversation
Agent options
Use answer of most similar question
Reject and write custom answer
32. Self-learning algorithm
Matching outside cluster
Database containing historic
conversations
New conversation
Agent options
Use answer of most similar question
Reject and write custom answer
33. Self-learning algorithm
Start new cluster
Database containing historic
conversations
New conversation
Agent options
Use answer of most similar question
Reject and write custom answer
34. Self-learning algorithm
Matching outside cluster
Database containing historic
conversations
New conversation
Agent options
Use answer of most similar question
Reject and write custom answer
35. Self-learning algorithm
(optional) remove from history, add observation to history
Database containing historic
conversations
New conversation
Agent options
Use answer of most similar question
Reject and write custom answer
36. Self-learning algorithm
Overview
MatchingNew conversation
In cluster
Outside cluster
Use answer of most
similar question
Reject and write custom
answer
Agent options
Give model answer
Use answer of most
similar question
Reject and write custom
answer
Learning
Extend cluster
New cluster
(Optional) remove
match, add to set
New cluster
(Optional) remove
match, add to set
37. Road to production
The developed solution can be used in other customer service situations
Collective
memory
AgentBotFacebook
38. Road to production
The developed solution can be used in other customer service situations
Collective
memory
AgentBotFacebook
Phone
Agent
Website
(self help)
Tweets and
forums
Commercial
actions
39. SUPERVISED LEARNING
RAW DATA DATA SCIENCE ALGORITHM SUGGESTED
SOLUTION
OPTIONS
AGENT SELECTS
BEST SOLUTION
ALGORITHM
IMPROVED
AND IS AN
OPPORTUNITY TO TRAIN
YOUR MODEL!
40. THE CHALLENGE
The costs of handling the channel Social Messaging are too high. The growth
of the messages is massive and because of that, the workforce grows
accordingly. A large number of questions asked by customers have been
answered before, using this data would reduce time and effort for the agent,
but also increase the satisfaction of both the customer and the employee.
THE SOLUTION
Teleperformance created a “Collective Memory Database” containing
Historical Conversations. These conversations have been clustered with
algorithms using Data Science Techniques. Model answers have been
created for these clusters and 3 options are pushed to agents when a
customer query comes in. These options are “Give model answer”, “Use
historic answer of most similar question” and “Reject and write own
answer”.
SUMMARY
Provide Suggested Answers to Agents
IMPLEMENTATION TIME: 3 Months THE BENEFITS
ü Cost Saving: Reduced 12% costs in PoC
ü ROI: 6 Months
ü Improved Response Time with 1,5 minutes
ü Manual effort reduced by 14%
ü Higher standardization of processes
ü Expected increase of Employee Satisfaction
ü Expected reduction of Agent Onboarding
NEXT STEPS
ü Integrate with Self-Service Portal
ü Integrate in Voice Channel