How AWS comes to life at innogy: driving retention and ...... · • Real-estate purchases •...
Transcript of How AWS comes to life at innogy: driving retention and ...... · • Real-estate purchases •...
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How AWS comes to life at innogy: driving retention and revenue with machine learning and advanced analytics
P U T 2 0 1
Kuldip Singh, Director Digital & Data @ InnogyWannes Rosiers, Data Officer @ Essent.be
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Agenda
The digital and data journey at innogy (Kuldip)
Use cases and realized impact (Kuldip)
A deepdive into the data stack and cost optimization (Wannes)
Q&A (all)
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Ice production1924
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Ice productiontoday
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Data storage1980
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Data storagetoday
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Energy conversion with power to gas:The natural gas network has particularly great potential for storage, which is precisely why innogy tests this in a pilot project in Ibbenbüren: a promising option for long-term storage and even more efficient use of green power.
Switching from old to renewable power and
storage solutions
Conventionalpower generation
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Innogy aims to lead the green energy
transition
With intelligent power grids and efficient energy storage solutions, innogy is not only safeguarding our future supply but also contributing to the energy transition in Germany and other European markets
There will be no energy transition
without digital transformation
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innogy’s retail data strategy is aimed at creating ideal 360° views of energy consumers and households
ENRICHMENT
Smart Meter
House Profile
ECOSYSTEM Social Media
Media Partners
RetailPartners
Market Research
/ Panel
BAG
Service Partner
Other Sources
EDM
Other Partners
Installations & maintenance status
• Electricity & gas usage• Energy generation
• Size of house• Energy profile
(solar, insulation,…)
• # people• Demographics • Bisnode
Demographics
Contracts
Comms preferences
Consents ID
• Weather• City data• Traffic data • Price comparisons
• Real-estate purchases• Interests, e.g. soccer
• Industry comparisons• Surveys
• Leads for E&G• Installations• Service status
Smart Home• Sensor data from
home (temperature, humidity, etc.)
• Connected devices
• Interest profile• Purchase intent• Moving intent
Marketing campaigns• Email openings and CTR• Given reviews and stars
Customer Service • Service contacts
Intent & Interest
eMobility• Charging station• Charging service
CORE
Note: Highlights the main identified data sources
Account• Product, price,
location, recency
Purchases via retail
Online Usage• Browsing• Purchase• Video
Mobile app usage
Satisfaction• Purchase to loyalty• Customer Effort• NPS
• Facebook• Google• Twitter• Search engine terms
Household Profile
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innogy’s data strategy is built on a layered MarTech architecture, resulting in effective channel decisions
1
3
2
4
360° house 360° customer
1st2nd
3rd
Combining house and customer data with relevant 3rd party data further personalizes offerings andmakes it possible to offer E+ products to commodity users and vice versa.
Some examples scale in EUR: Solar panels for house owners with a
suitable rooftop Isolation for proven outdated
neighbourhoods Energy efficient products in
cooperation with electronic retailers via disaggregation
Using the data of service partners and household data builds a location focused profile On and offline data help both digital marketing and call center agents in a real time manner
* Results are not significant and tested on a small groupof commercial contact center agents
We see a 70%*+ in conversion of
E+/green products
A 360° data use case happens in NL, combining house, customer, and third party data in a CDP
A great innovation is the AI pilot within Energiedirect: optimizing shows promising results (+29% sales)
Sales:
+29% €238k extra
margin* in 9 weeks
* Extrapolated figure (based on sample results)
Energiedirect.nl started in Q2 ‘18 an AI-pilot with Sentient…1 2
…During the testing period of 9 weeks, 256 variations of the sales funnel have been tested (instead of ~3 in traditional A/B-testing)…
…Resulting in an impressive uplift (+29%) in Sales3
“Amazon is a technology company. We just happen to do retail.”
Werner VogelsCTO, amazon.com
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What customersbuy
What you must have
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What customersbuy
What you must have
What you must build
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What customersbuy
What you must have
What you must build...
...and can‘toutsource!
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In Belgium, innogy has a strong data fundament to create value via in-house data science competences
ProcessingOrchestration
Ingestion Data Lake
Stor
age Egestion
Anal
ytic
s
Sandbox
API
Serv
ice
Airflow
Amazon API Gateway AWS Lambda
Amazon EMR
AWS DMS Amazon S3 Amazon RDS
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AWS allows cost efficiency in building data solutions and ease in replicating them
CloudformationEC2 Spot pricing Glacier
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Multiple evironments can easily be created and connected
AD
DNS
AD
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We aim to fully take advantage of cost optimization possibilities
Switching on and off Delete and launch
Incorporate in job scheduling
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The data flow in Belgium clearly reflects AWS value
Incremen-tal loads
“1” daily image
Daily view
stored
Daily calculated
“1” daily image
No storage
No storage
BI
DWP*
Marketingautomation
Online
Call center
Smart home
BI
Marketingautomation
Raw Cleaned Features Models ResultsIngestion Egestion
Orchestration + scheduling
* DWP = Digital Work Place – The single “screen” every employee needs to open
AWS DMS Amazon S3 Amazon S3 Amazon S3 Amazon S3 Amazon RDS
API Gateway
AWS Lambda
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How it works under the hood: customer value
BI
Call center
Amazon S3
AWS DMSAPI Gateway
Amazon EMRCleaning
Airflow
Amazon EMRSurvival
Amazon EMRCost features
Amazon RDS
Amazon API Gateway
AWS Lambda
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How it works under the hood: customer value
BI
Call center
Amazon S3
AWS DMSAPI Gateway
Amazon EMRCleaning
Airflow
Amazon EMRSurvival
Amazon EMRCost features
Amazon RDS
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Innogy considers Belgium as a data lab, hence the goal is to keep testing new AWS features
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‘Fight to keep’ in BE is an impactful example of a pro-active retention initiative based on predictive analytics Starting from identifying potential
churners (85% likely)…
Max feature date
Input features
Output
Today
90 days
Loss message receivedCustomer facts
Input dates90 days
Today
1 month1 month * 11
… IIIII
Churn probability = 85%Churn label = Very likely
• Daily vs. monthly churn prediction(pending)
• Standardized reporting (pending) • 360 consumer view not available• Connection with customer service
(inbound) not available yetRenewal date
Bad payers
MarginOther campaigns
2
…Filters applied to target the right customer…3
Fight to keep: retention campaign to prolong customer lifetime
Current customers & revenue
New level of customers &
revenue
3) ARPU
Obtain higher revenue from
existing customers
2) RETENTION
Prolong current customer lifetime
1) ACQUISITION
Acquire new customers
1
…Still technical improvements need to be settled to fully optimize the model 4
Dec17 - Apr 18: Pilot phase
1250 potential churnersidentified
Significant uplift reached vs. not reached (renewal)
Initial mail shows outstanding open and click ratio
7% accepted mail offer
May-Jun 2018: Roll-out with updated data model
88 customers renews(gold and silver customers)
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Our planet will be a better place, when we create a sustainable world in which innogy
inspires how people live and work.
Thank you!
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Kuldip Singh, Director Digital & Data @ InnogyWannes Rosiers, Data Officer @ Essent.be
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