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


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)


Ice production1924


Ice productiontoday


Data storage1980


Data storagetoday


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


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


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


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


What customersbuy

What you must have


What customersbuy

What you must have

What you must build


What customersbuy

What you must have

What you must build...

...and can‘toutsource!


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


AWS allows cost efficiency in building data solutions and ease in replicating them

CloudformationEC2 Spot pricing Glacier


Multiple evironments can easily be created and connected

AD

DNS

AD


We aim to fully take advantage of cost optimization possibilities

Switching on and off Delete and launch

Incorporate in job scheduling


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


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


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


Innogy considers Belgium as a data lab, hence the goal is to keep testing new AWS features


‘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)


Our planet will be a better place, when we create a sustainable world in which innogy

inspires how people live and work.

Thank you!


Kuldip Singh, Director Digital & Data @ InnogyWannes Rosiers, Data Officer @ Essent.be

How AWS comes to life at innogy: driving retention and ...... · • Real-estate purchases •...

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