SlideShare a Scribd company logo
WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
Creating an Omnichannel Banking
Experience with Machine Learning
on Azure Databricks
Petr Pluhacek (Ceska Sporitelna)
Jakub Stech (DataSentics)
Who is presenting
today
Petr Pluháček, Česká spořitelna
Product owner:
• Digital engagement and acquisition squad
Responsibilities:
• Digital sales
• Web platforms and chats
• Digital marketing data and analytics
• Online customer experience
Contacts:
• ppluhacek@csas.cz
• www.linkedin.com/in/pluhacek
Česká Spořitelna
About us:
• Almost 200 years history
• 4,6 millions customers
• 10 000 of employees
• Part of ERSTE group
• Driver of innovation in the group
• Undergoing agile transformation
ČS Mission:
“We are your lifelong guide on the path to prosperity, and in
this way we contribute together to the prosperity of the whole
country. When someone believes in you, you achieve more."
Who is presenting
today Jakub Stech, DataSentics
Data Science architect in:
• DataSentics and Digi data team in CSAS
Responsibilities:
• Translate business problems for data science team
• Personalizing user experience using data and machine
learning approaches
• Building and employing the analytical platform in cloud
Contacts:
• jakub.stech@datasentics.com
• www.linkedin.com/in/jakubstech
DataSentics – European Data Science Center
of Excellence based in Prague
• Machine learning and cloud data
engineering boutique
• 50 data specialists (data science,
data/software engineering)
• Helping customers build end-to-end data
solutions in cloud
• Incubator of ML-based products
• Partner of Databricks & Microsoft
• Make data science and machine learning
have a real impact on organizations across
the world
• Bring to life transparent production-level
data science.
Digital marketing spend optimization
Case study #1
ADVERTISMENT WWW.CSAS.CZ INTERNET BANKING OTHER
SOURCES
BRAND AWARENESS
SALES (ONLINE AND OFFLINE)
CARE
Digital marketing interactions
How to improve digital marketing spend
effectiveness
Business challenge:
10
ADVERTISMENT WWW.CSAS.CZ INTERNET BANKING OTHER
SOURCES
BRAND AWARENESS
SALES (ONLINE AND OFFLINE)
CARE
Digital marketing interactions
Adform is one of the world's largest private and independent
advertising technology companies and is best known for its
seamlessly integrated DSP, DMP, and Ad Server.
ADVERTISMENT
Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks
Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks
Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks
A viewable impression is a standard measure of ad
viewability defined by the International Advertising
Bureau (IAB) to be an ad which appears at least
50% on screen for more than one second.
Decrease costs for visible seconds
Better specification of business challenge
WHEN
WHO
WHAT
WHERE
DEVICE
GEO
INTERACTIONS
DURATION
INT. COUNT
HOW MUCH
Adform Master Data…
Robot score (Adform definition)
Soft fraud score (own definition)
Multiple impressions
CTR (too low/high)
Visibility %
Visibility
time
SEZNAM.CZ; BID 0.2 ;
NOVINKY.CZ; BID 0.6 ;
IDNES.CZ ; BID 0.1 ;
BLESK.CZ ; BID 0.4 ;
…
Metrics of domain model
• API
• BigQuery
• SFTP
• CSV
• Packages
• JSON
• Database dump!
• Web Pages
• …
Daily download, transformation and scoring jobs
4+ BLNs rows in 12months!
Data sources
Automated
download
MS Azure
CSAS Storing the data in
Data Lake
Results stored
into Data Lake
Scoring new
domains
Whitelists, bid
multipliers,
cookies lists,
blaclicks, …
Automated
update
SEZNAM.CZ; BID 0.2 ;
NOVINKY.CZ; BID 0.6 ;
IDNES.CZ ; BID 0.1 ;
BLESK.CZ ; BID 0.4 ;
…
Fully automated pipeline
23% Desktop
28% Mobile
Increased effectivity of 1 EUR:
Omnichannel Banking Experience
Case study #2
23
Leading every client to prosperity
=
Data-driven advisory based on
clients needs and real-time situations
24
… is not easy
in bank
Customer-
centricity….
Low frequency of
interactions between a
client in offline channels
100 Things,
We touch our
phones 2,617
times a day,
says study
Around 100
sessions every
day…
Offline vs. Online
Typical CRM data
• Age/sex/address, policy history, policy
configuration, claim history, sales channel,
…
• Static, mostly long-term behaviour
• Facts and transactions
• Well structured, easy to process with
traditional tech
Digital „footprints“
• Ad interactions (wider internet behaviour),
web interaction (own sites), mobile apps,
external/partner data, …
• Dynamically changing, reflecting short and
long-term needs
• Uncertainty, fragments about interests,
behaviour, lifestyle
• Enormous data (B+ ads, M+ visits of
website…), messy, unstructured, changing
interfaces
1
Ad Interactions
(what the person is
interested in across
the internet)
Own website
interactions
Emailing /
SMS / Push
Siloed customer behaviour data
Classic client profiles
4) Limited customer
experience
2) Missing
environment for
data analytics
and machine
learning
Classic
CRM / data
processes Branches
& sales networks
Transactional
data / product
data
Callcentrum
data / call
logs
Digital campaign
management tools
Classic campaign
management tools
1) Missing connection
between digital and
CRM
3) On-premise
environment is lacking
customer data from
digital
Mobile app
interactions
Client portal /
Internetbanking
interactions
3rd party data,
voice, text,
image, geo
data, etc.
Digital
engagement
(3rd party)
Offline vs. Online
1
Ad Interactions
(what the person is
interested in across
the internet)
Own website
interactions
Emailing /
SMS / Push
Non-client & client behavior
Classic client profiles
Automatic
optimization,
personalization of
customer journeys
Machine
learning
Your Customer
Engagement
360° Platform
(CSAS)
Classic
CRM / data
processes
Automatic signals
for classic
channels
Branches
& sales networks
Transactional
data / product
data
Callcentrum
data / call
logs
Connecting the data on
individual customer level
Digital campaign
management tools
Classic campaign
management tools
New opportunities
Higher
efficiency
Mobile app
interactions
Client portal /
Internetbanking
interactions
3rd party data,
voice, text,
image, geo
data, etc.
Digital
engagement
(3rd party)
AI-augmented Customer Engagement 360°
CASH LOAN MORTGAGESAVING ACCOUNT
Non-client & client behavior
Classic client profiles
Connecting the data on
individual customer level
Digital data based sales signals
Mortgage / Loan / Saving account / Investments …
What product?
OK, Loan… but what message?
Digital data based sales signals
Digital data based sales signals
MACHINE LEARNING
Digital data based sales signals
Call script: “Client is interested in taking loan. He
is planning wedding according to his activity on
internet.”
Digital data based sales signals
Standard classification models
(GLMs as benchmark, GBTs in production)
ML(lib) part
TEXT REPRESENTATIONS
CUSTOMER PATHS
NUMERIC REPRESENTATIONS
Loans predicted by Adform data
4x Higher probability
Words with predictive power: loan, car, moto, wedding, …
ADFORM
WEB
DMP
CRM
CALL CENTRES
…
ADFORM
GA
CRM
Automated
downloadMS Azure
ČS
Storing the data in
Data Lake
Results stored
into Data Lake
AI monitoring
dashboard
Model (AI)
re-training
Scoring new
data using
existing model
(AI) Whitelists, bid
multipliers,
cookies lists,
blaclicks, …
Automated
update
Transforma
tion
Results
(bids,
audiences,
…)
Fully automated pipeline
Current architecture
50% Improvement
Benchmark:
Offline sales signals for mortages sales: call centre 10% sucess rate
Current:
Digi data enhanced signals for mortages sales: call centre 15% sucess rate
Take aways
• No extra tech, just extend platforms with AI
models
• Connecting the data on individual customer
level is crucial
• Clear business specifications and convincing
results are essential
Thank you
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

More Related Content

What's hot

Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason PohlBuilding a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Spark Summit
 
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
Databricks
 
Using neo4j for enterprise metadata requirements
Using neo4j for enterprise metadata requirementsUsing neo4j for enterprise metadata requirements
Using neo4j for enterprise metadata requirements
Neo4j
 
Mastering Your Customer Data on Apache Spark by Elliott Cordo
Mastering Your Customer Data on Apache Spark by Elliott CordoMastering Your Customer Data on Apache Spark by Elliott Cordo
Mastering Your Customer Data on Apache Spark by Elliott Cordo
Spark Summit
 
Customer Event Hub - the modern Customer 360° view
Customer Event Hub - the modern Customer 360° viewCustomer Event Hub - the modern Customer 360° view
Customer Event Hub - the modern Customer 360° view
Guido Schmutz
 
Graphs in Action
Graphs in ActionGraphs in Action
Graphs in Action
Neo4j
 
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlowBuilding an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Databricks
 
Disrupting Risk Management through Emerging Technologies
Disrupting Risk Management through Emerging TechnologiesDisrupting Risk Management through Emerging Technologies
Disrupting Risk Management through Emerging Technologies
Databricks
 
Meaningful User Experience
Meaningful User ExperienceMeaningful User Experience
Meaningful User Experience
Neo4j
 
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE) Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
Guido Schmutz
 
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile AdvertisingTapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
SingleStore
 
MicroStrategy on Amazon Web Services (AWS) Cloud
MicroStrategy on Amazon Web Services (AWS) CloudMicroStrategy on Amazon Web Services (AWS) Cloud
MicroStrategy on Amazon Web Services (AWS) Cloud
CCG
 
Analytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual WorkshopAnalytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual Workshop
CCG
 
Embedding Insight through Prediction Driven Logistics
Embedding Insight through Prediction Driven LogisticsEmbedding Insight through Prediction Driven Logistics
Embedding Insight through Prediction Driven Logistics
Databricks
 
Snowplow: where we came from and where we are going - March 2016
Snowplow: where we came from and where we are going - March 2016Snowplow: where we came from and where we are going - March 2016
Snowplow: where we came from and where we are going - March 2016
yalisassoon
 
Snowplow the evolving data pipeline
Snowplow   the evolving data pipelineSnowplow   the evolving data pipeline
Snowplow the evolving data pipeline
yalisassoon
 
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...
Databricks
 
Scalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and HowScalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and How
Cambridge Semantics
 
Supply Chain and Logistics Management with Graph & AI
Supply Chain and Logistics Management with Graph & AISupply Chain and Logistics Management with Graph & AI
Supply Chain and Logistics Management with Graph & AI
TigerGraph
 
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...
Databricks
 

What's hot (20)

Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason PohlBuilding a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
 
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
 
Using neo4j for enterprise metadata requirements
Using neo4j for enterprise metadata requirementsUsing neo4j for enterprise metadata requirements
Using neo4j for enterprise metadata requirements
 
Mastering Your Customer Data on Apache Spark by Elliott Cordo
Mastering Your Customer Data on Apache Spark by Elliott CordoMastering Your Customer Data on Apache Spark by Elliott Cordo
Mastering Your Customer Data on Apache Spark by Elliott Cordo
 
Customer Event Hub - the modern Customer 360° view
Customer Event Hub - the modern Customer 360° viewCustomer Event Hub - the modern Customer 360° view
Customer Event Hub - the modern Customer 360° view
 
Graphs in Action
Graphs in ActionGraphs in Action
Graphs in Action
 
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlowBuilding an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
 
Disrupting Risk Management through Emerging Technologies
Disrupting Risk Management through Emerging TechnologiesDisrupting Risk Management through Emerging Technologies
Disrupting Risk Management through Emerging Technologies
 
Meaningful User Experience
Meaningful User ExperienceMeaningful User Experience
Meaningful User Experience
 
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE) Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
 
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile AdvertisingTapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
 
MicroStrategy on Amazon Web Services (AWS) Cloud
MicroStrategy on Amazon Web Services (AWS) CloudMicroStrategy on Amazon Web Services (AWS) Cloud
MicroStrategy on Amazon Web Services (AWS) Cloud
 
Analytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual WorkshopAnalytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual Workshop
 
Embedding Insight through Prediction Driven Logistics
Embedding Insight through Prediction Driven LogisticsEmbedding Insight through Prediction Driven Logistics
Embedding Insight through Prediction Driven Logistics
 
Snowplow: where we came from and where we are going - March 2016
Snowplow: where we came from and where we are going - March 2016Snowplow: where we came from and where we are going - March 2016
Snowplow: where we came from and where we are going - March 2016
 
Snowplow the evolving data pipeline
Snowplow   the evolving data pipelineSnowplow   the evolving data pipeline
Snowplow the evolving data pipeline
 
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...
 
Scalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and HowScalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and How
 
Supply Chain and Logistics Management with Graph & AI
Supply Chain and Logistics Management with Graph & AISupply Chain and Logistics Management with Graph & AI
Supply Chain and Logistics Management with Graph & AI
 
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...
 

Similar to Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks

Kde jsou limity zákaznické 360°?
 Kde jsou limity zákaznické 360°? Kde jsou limity zákaznické 360°?
Kde jsou limity zákaznické 360°?
Taste Medio
 
Publishers' Life After Cookies Webinar
Publishers' Life After Cookies WebinarPublishers' Life After Cookies Webinar
Publishers' Life After Cookies Webinar
Matěj Novák
 
Innov day big data enabler & business opportunities(1)
Innov day   big data enabler & business opportunities(1)Innov day   big data enabler & business opportunities(1)
Innov day big data enabler & business opportunities(1)
TelkomDDSKM
 
Digital marketing pharma - google event
Digital marketing   pharma - google eventDigital marketing   pharma - google event
Digital marketing pharma - google event
Daniel Viveiros
 
The Next Digital Marketing- Digital Pharma presentation by Ci&T and Google
The Next Digital Marketing- Digital Pharma presentation by Ci&T and GoogleThe Next Digital Marketing- Digital Pharma presentation by Ci&T and Google
The Next Digital Marketing- Digital Pharma presentation by Ci&T and Google
CI&T
 
1.1 Webtrekk transform your data sales
1.1 Webtrekk transform your data sales1.1 Webtrekk transform your data sales
1.1 Webtrekk transform your data sales
Elena Vigni
 
2017 05 Hadoop User Group Meetup Dublin
2017 05 Hadoop User Group Meetup Dublin2017 05 Hadoop User Group Meetup Dublin
2017 05 Hadoop User Group Meetup Dublin
mdabrowski
 
Workshop: Make the Most of Customer Data Platforms - David Raab
Workshop: Make the Most of Customer Data Platforms - David RaabWorkshop: Make the Most of Customer Data Platforms - David Raab
Workshop: Make the Most of Customer Data Platforms - David Raab
Digital Customer Experience (DX) Summit
 
The Connected Consumer – Real-time Customer 360
The Connected Consumer – Real-time Customer 360The Connected Consumer – Real-time Customer 360
The Connected Consumer – Real-time Customer 360
Capgemini
 
Digital Servicing Using Artificial Intelligence
Digital Servicing Using Artificial IntelligenceDigital Servicing Using Artificial Intelligence
Digital Servicing Using Artificial Intelligence
René Werner
 
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For EcommerceDeep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.BI
 
Where does mobile data fit in your infrastructure?
Where does mobile data fit in your infrastructure?Where does mobile data fit in your infrastructure?
Where does mobile data fit in your infrastructure?
mParticle
 
ANTS - 360 view of your customer - bigdata innovation summit 2016
ANTS - 360 view of your customer - bigdata innovation summit 2016ANTS - 360 view of your customer - bigdata innovation summit 2016
ANTS - 360 view of your customer - bigdata innovation summit 2016
Dinh Le Dat (Kevin D.)
 
From Architecture to Analytics: A look at Simply Business’s data strategy
From Architecture to Analytics: A look at Simply Business’s data strategy From Architecture to Analytics: A look at Simply Business’s data strategy
From Architecture to Analytics: A look at Simply Business’s data strategy
Looker
 
BSFI Technology Offerings by Value Innovation Labs
BSFI Technology Offerings by Value Innovation LabsBSFI Technology Offerings by Value Innovation Labs
BSFI Technology Offerings by Value Innovation Labs
Mount Talent Consulting
 
Compassites Software - Company Profile | Digital Transformation & Custom Soft...
Compassites Software - Company Profile | Digital Transformation & Custom Soft...Compassites Software - Company Profile | Digital Transformation & Custom Soft...
Compassites Software - Company Profile | Digital Transformation & Custom Soft...
Compassites Software Solutions Pvt Ltd
 
Solving churn challenge in Big Data environment - Jelena Pekez
Solving churn challenge in Big Data environment  - Jelena PekezSolving churn challenge in Big Data environment  - Jelena Pekez
Solving churn challenge in Big Data environment - Jelena Pekez
Institute of Contemporary Sciences
 
Mid Term Exam
Mid Term ExamMid Term Exam
Mid Term Exam
webhostingguy
 
Site Intelligence Presentation Sep 2010
Site Intelligence Presentation Sep 2010Site Intelligence Presentation Sep 2010
Site Intelligence Presentation Sep 2010
Paul Galbraith
 
06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...
06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...
06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...
mateja repovž
 

Similar to Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks (20)

Kde jsou limity zákaznické 360°?
 Kde jsou limity zákaznické 360°? Kde jsou limity zákaznické 360°?
Kde jsou limity zákaznické 360°?
 
Publishers' Life After Cookies Webinar
Publishers' Life After Cookies WebinarPublishers' Life After Cookies Webinar
Publishers' Life After Cookies Webinar
 
Innov day big data enabler & business opportunities(1)
Innov day   big data enabler & business opportunities(1)Innov day   big data enabler & business opportunities(1)
Innov day big data enabler & business opportunities(1)
 
Digital marketing pharma - google event
Digital marketing   pharma - google eventDigital marketing   pharma - google event
Digital marketing pharma - google event
 
The Next Digital Marketing- Digital Pharma presentation by Ci&T and Google
The Next Digital Marketing- Digital Pharma presentation by Ci&T and GoogleThe Next Digital Marketing- Digital Pharma presentation by Ci&T and Google
The Next Digital Marketing- Digital Pharma presentation by Ci&T and Google
 
1.1 Webtrekk transform your data sales
1.1 Webtrekk transform your data sales1.1 Webtrekk transform your data sales
1.1 Webtrekk transform your data sales
 
2017 05 Hadoop User Group Meetup Dublin
2017 05 Hadoop User Group Meetup Dublin2017 05 Hadoop User Group Meetup Dublin
2017 05 Hadoop User Group Meetup Dublin
 
Workshop: Make the Most of Customer Data Platforms - David Raab
Workshop: Make the Most of Customer Data Platforms - David RaabWorkshop: Make the Most of Customer Data Platforms - David Raab
Workshop: Make the Most of Customer Data Platforms - David Raab
 
The Connected Consumer – Real-time Customer 360
The Connected Consumer – Real-time Customer 360The Connected Consumer – Real-time Customer 360
The Connected Consumer – Real-time Customer 360
 
Digital Servicing Using Artificial Intelligence
Digital Servicing Using Artificial IntelligenceDigital Servicing Using Artificial Intelligence
Digital Servicing Using Artificial Intelligence
 
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For EcommerceDeep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
 
Where does mobile data fit in your infrastructure?
Where does mobile data fit in your infrastructure?Where does mobile data fit in your infrastructure?
Where does mobile data fit in your infrastructure?
 
ANTS - 360 view of your customer - bigdata innovation summit 2016
ANTS - 360 view of your customer - bigdata innovation summit 2016ANTS - 360 view of your customer - bigdata innovation summit 2016
ANTS - 360 view of your customer - bigdata innovation summit 2016
 
From Architecture to Analytics: A look at Simply Business’s data strategy
From Architecture to Analytics: A look at Simply Business’s data strategy From Architecture to Analytics: A look at Simply Business’s data strategy
From Architecture to Analytics: A look at Simply Business’s data strategy
 
BSFI Technology Offerings by Value Innovation Labs
BSFI Technology Offerings by Value Innovation LabsBSFI Technology Offerings by Value Innovation Labs
BSFI Technology Offerings by Value Innovation Labs
 
Compassites Software - Company Profile | Digital Transformation & Custom Soft...
Compassites Software - Company Profile | Digital Transformation & Custom Soft...Compassites Software - Company Profile | Digital Transformation & Custom Soft...
Compassites Software - Company Profile | Digital Transformation & Custom Soft...
 
Solving churn challenge in Big Data environment - Jelena Pekez
Solving churn challenge in Big Data environment  - Jelena PekezSolving churn challenge in Big Data environment  - Jelena Pekez
Solving churn challenge in Big Data environment - Jelena Pekez
 
Mid Term Exam
Mid Term ExamMid Term Exam
Mid Term Exam
 
Site Intelligence Presentation Sep 2010
Site Intelligence Presentation Sep 2010Site Intelligence Presentation Sep 2010
Site Intelligence Presentation Sep 2010
 
06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...
06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...
06. DIGGIT MARIUS IVANOVAS (Httpool Baltics): Personalizirane marketinške akt...
 

More from Databricks

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
Databricks
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1
Databricks
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
Databricks
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
Databricks
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4
Databricks
 
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Databricks
 
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
Databricks
 
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceLearn to Use Databricks for Data Science
Learn to Use Databricks for Data Science
Databricks
 
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringWhy APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML Monitoring
Databricks
 
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixThe Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Databricks
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Databricks
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Databricks
 
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesScaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Databricks
 
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesScaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Databricks
 
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsSawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature Aggregations
Databricks
 
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkRedis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Databricks
 
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkRe-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and Spark
Databricks
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Databricks
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
Databricks
 
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
Databricks
 

More from Databricks (20)

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4
 
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
 
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
 
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceLearn to Use Databricks for Data Science
Learn to Use Databricks for Data Science
 
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringWhy APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML Monitoring
 
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixThe Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
 
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesScaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
 
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesScaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
 
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsSawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature Aggregations
 
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkRedis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
 
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkRe-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and Spark
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
 
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
 

Recently uploaded

Girls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in City
Girls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in CityGirls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in City
Girls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in City
gargnatasha985
 
Celonis Busniess Analyst Virtual Internship.pptx
Celonis Busniess Analyst Virtual Internship.pptxCelonis Busniess Analyst Virtual Internship.pptx
Celonis Busniess Analyst Virtual Internship.pptx
AnujaGaikwad28
 
Experience, Excellence & Commitment are the characteristics that describe Fla...
Experience, Excellence & Commitment are the characteristics that describe Fla...Experience, Excellence & Commitment are the characteristics that describe Fla...
Experience, Excellence & Commitment are the characteristics that describe Fla...
kittycrispy617
 
BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...
BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...
BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...
fatima shekh$A17
 
DataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptxDataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptx
Kanchana Weerasinghe
 
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...
rightmanforbloodline
 
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
revolutionary575
 
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
sheetal singh$A17
 
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
saadkhan1485265
 
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
satpalsheravatmumbai
 
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
dizzycaye
 
Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...
Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...
Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...
45unexpected
 
Why_are_we_hypnotizing_ourselves-_ATeggin-1.pdf
Why_are_we_hypnotizing_ourselves-_ATeggin-1.pdfWhy_are_we_hypnotizing_ourselves-_ATeggin-1.pdf
Why_are_we_hypnotizing_ourselves-_ATeggin-1.pdf
Alexander Teggin
 
New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...
New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...
New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...
kinni singh$A17
 
VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...
VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...
VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...
sukaniyasunnu
 
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
norina2645
 
Biometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdfBiometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdf
Joel Ngushwai
 
the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...
huseindihon
 
🚂🚘 Premium Girls Call Nashik 🛵🚡000XX00000 💃 Choose Best And Top Girl Service...
🚂🚘 Premium Girls Call Nashik  🛵🚡000XX00000 💃 Choose Best And Top Girl Service...🚂🚘 Premium Girls Call Nashik  🛵🚡000XX00000 💃 Choose Best And Top Girl Service...
🚂🚘 Premium Girls Call Nashik 🛵🚡000XX00000 💃 Choose Best And Top Girl Service...
kuldeepsharmaks8120
 
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
sharonblush
 

Recently uploaded (20)

Girls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in City
Girls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in CityGirls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in City
Girls Call Vadodara 000XX00000 Provide Best And Top Girl Service And No1 in City
 
Celonis Busniess Analyst Virtual Internship.pptx
Celonis Busniess Analyst Virtual Internship.pptxCelonis Busniess Analyst Virtual Internship.pptx
Celonis Busniess Analyst Virtual Internship.pptx
 
Experience, Excellence & Commitment are the characteristics that describe Fla...
Experience, Excellence & Commitment are the characteristics that describe Fla...Experience, Excellence & Commitment are the characteristics that describe Fla...
Experience, Excellence & Commitment are the characteristics that describe Fla...
 
BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...
BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...
BDSM Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And ...
 
DataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptxDataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptx
 
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...
 
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
 
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
 
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
 
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
 
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
 
Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...
Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...
Female Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service A...
 
Why_are_we_hypnotizing_ourselves-_ATeggin-1.pdf
Why_are_we_hypnotizing_ourselves-_ATeggin-1.pdfWhy_are_we_hypnotizing_ourselves-_ATeggin-1.pdf
Why_are_we_hypnotizing_ourselves-_ATeggin-1.pdf
 
New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...
New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...
New Girls Call Noida 9873940964 Unlimited Short Providing Girls Service Avail...
 
VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...
VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...
VIP Kolkata Girls Call Kolkata 0X0000000X Doorstep High-Profile Girl Service ...
 
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
 
Biometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdfBiometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdf
 
the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...
 
🚂🚘 Premium Girls Call Nashik 🛵🚡000XX00000 💃 Choose Best And Top Girl Service...
🚂🚘 Premium Girls Call Nashik  🛵🚡000XX00000 💃 Choose Best And Top Girl Service...🚂🚘 Premium Girls Call Nashik  🛵🚡000XX00000 💃 Choose Best And Top Girl Service...
🚂🚘 Premium Girls Call Nashik 🛵🚡000XX00000 💃 Choose Best And Top Girl Service...
 
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
 

Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks

  • 1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
  • 2. Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks Petr Pluhacek (Ceska Sporitelna) Jakub Stech (DataSentics)
  • 3. Who is presenting today Petr Pluháček, Česká spořitelna Product owner: • Digital engagement and acquisition squad Responsibilities: • Digital sales • Web platforms and chats • Digital marketing data and analytics • Online customer experience Contacts: • ppluhacek@csas.cz • www.linkedin.com/in/pluhacek
  • 4. Česká Spořitelna About us: • Almost 200 years history • 4,6 millions customers • 10 000 of employees • Part of ERSTE group • Driver of innovation in the group • Undergoing agile transformation ČS Mission: “We are your lifelong guide on the path to prosperity, and in this way we contribute together to the prosperity of the whole country. When someone believes in you, you achieve more."
  • 5. Who is presenting today Jakub Stech, DataSentics Data Science architect in: • DataSentics and Digi data team in CSAS Responsibilities: • Translate business problems for data science team • Personalizing user experience using data and machine learning approaches • Building and employing the analytical platform in cloud Contacts: • jakub.stech@datasentics.com • www.linkedin.com/in/jakubstech
  • 6. DataSentics – European Data Science Center of Excellence based in Prague • Machine learning and cloud data engineering boutique • 50 data specialists (data science, data/software engineering) • Helping customers build end-to-end data solutions in cloud • Incubator of ML-based products • Partner of Databricks & Microsoft • Make data science and machine learning have a real impact on organizations across the world • Bring to life transparent production-level data science.
  • 7. Digital marketing spend optimization Case study #1
  • 8. ADVERTISMENT WWW.CSAS.CZ INTERNET BANKING OTHER SOURCES BRAND AWARENESS SALES (ONLINE AND OFFLINE) CARE Digital marketing interactions
  • 9. How to improve digital marketing spend effectiveness Business challenge:
  • 10. 10
  • 11. ADVERTISMENT WWW.CSAS.CZ INTERNET BANKING OTHER SOURCES BRAND AWARENESS SALES (ONLINE AND OFFLINE) CARE Digital marketing interactions
  • 12. Adform is one of the world's largest private and independent advertising technology companies and is best known for its seamlessly integrated DSP, DMP, and Ad Server. ADVERTISMENT
  • 16. A viewable impression is a standard measure of ad viewability defined by the International Advertising Bureau (IAB) to be an ad which appears at least 50% on screen for more than one second.
  • 17. Decrease costs for visible seconds Better specification of business challenge
  • 19. Robot score (Adform definition) Soft fraud score (own definition) Multiple impressions CTR (too low/high) Visibility % Visibility time SEZNAM.CZ; BID 0.2 ; NOVINKY.CZ; BID 0.6 ; IDNES.CZ ; BID 0.1 ; BLESK.CZ ; BID 0.4 ; … Metrics of domain model
  • 20. • API • BigQuery • SFTP • CSV • Packages • JSON • Database dump! • Web Pages • … Daily download, transformation and scoring jobs 4+ BLNs rows in 12months! Data sources
  • 21. Automated download MS Azure CSAS Storing the data in Data Lake Results stored into Data Lake Scoring new domains Whitelists, bid multipliers, cookies lists, blaclicks, … Automated update SEZNAM.CZ; BID 0.2 ; NOVINKY.CZ; BID 0.6 ; IDNES.CZ ; BID 0.1 ; BLESK.CZ ; BID 0.4 ; … Fully automated pipeline
  • 22. 23% Desktop 28% Mobile Increased effectivity of 1 EUR:
  • 24. Leading every client to prosperity = Data-driven advisory based on clients needs and real-time situations 24
  • 25. … is not easy in bank Customer- centricity…. Low frequency of interactions between a client in offline channels
  • 26. 100 Things, We touch our phones 2,617 times a day, says study Around 100 sessions every day…
  • 27. Offline vs. Online Typical CRM data • Age/sex/address, policy history, policy configuration, claim history, sales channel, … • Static, mostly long-term behaviour • Facts and transactions • Well structured, easy to process with traditional tech Digital „footprints“ • Ad interactions (wider internet behaviour), web interaction (own sites), mobile apps, external/partner data, … • Dynamically changing, reflecting short and long-term needs • Uncertainty, fragments about interests, behaviour, lifestyle • Enormous data (B+ ads, M+ visits of website…), messy, unstructured, changing interfaces
  • 28. 1 Ad Interactions (what the person is interested in across the internet) Own website interactions Emailing / SMS / Push Siloed customer behaviour data Classic client profiles 4) Limited customer experience 2) Missing environment for data analytics and machine learning Classic CRM / data processes Branches & sales networks Transactional data / product data Callcentrum data / call logs Digital campaign management tools Classic campaign management tools 1) Missing connection between digital and CRM 3) On-premise environment is lacking customer data from digital Mobile app interactions Client portal / Internetbanking interactions 3rd party data, voice, text, image, geo data, etc. Digital engagement (3rd party) Offline vs. Online
  • 29. 1 Ad Interactions (what the person is interested in across the internet) Own website interactions Emailing / SMS / Push Non-client & client behavior Classic client profiles Automatic optimization, personalization of customer journeys Machine learning Your Customer Engagement 360° Platform (CSAS) Classic CRM / data processes Automatic signals for classic channels Branches & sales networks Transactional data / product data Callcentrum data / call logs Connecting the data on individual customer level Digital campaign management tools Classic campaign management tools New opportunities Higher efficiency Mobile app interactions Client portal / Internetbanking interactions 3rd party data, voice, text, image, geo data, etc. Digital engagement (3rd party) AI-augmented Customer Engagement 360°
  • 30. CASH LOAN MORTGAGESAVING ACCOUNT Non-client & client behavior Classic client profiles Connecting the data on individual customer level
  • 31. Digital data based sales signals Mortgage / Loan / Saving account / Investments … What product?
  • 32. OK, Loan… but what message? Digital data based sales signals
  • 33. Digital data based sales signals
  • 34. MACHINE LEARNING Digital data based sales signals
  • 35. Call script: “Client is interested in taking loan. He is planning wedding according to his activity on internet.” Digital data based sales signals
  • 36. Standard classification models (GLMs as benchmark, GBTs in production) ML(lib) part TEXT REPRESENTATIONS CUSTOMER PATHS NUMERIC REPRESENTATIONS
  • 37. Loans predicted by Adform data 4x Higher probability Words with predictive power: loan, car, moto, wedding, …
  • 38. ADFORM WEB DMP CRM CALL CENTRES … ADFORM GA CRM Automated downloadMS Azure ČS Storing the data in Data Lake Results stored into Data Lake AI monitoring dashboard Model (AI) re-training Scoring new data using existing model (AI) Whitelists, bid multipliers, cookies lists, blaclicks, … Automated update Transforma tion Results (bids, audiences, …) Fully automated pipeline
  • 40. 50% Improvement Benchmark: Offline sales signals for mortages sales: call centre 10% sucess rate Current: Digi data enhanced signals for mortages sales: call centre 15% sucess rate
  • 41. Take aways • No extra tech, just extend platforms with AI models • Connecting the data on individual customer level is crucial • Clear business specifications and convincing results are essential
  • 43. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT