Director's Challenge | Data Science Trends en de kansen voor uw business Dat de impact van data science op business modellen groot is zal niemand zijn ontgaan. Het verandert traditionele proposities en klantrelaties en het opent deuren voor explosieve groei. Frontrunners als Google, Uber en Takeaway.com hebben bewezen dat een revolutionaire visie in combinatie met de toepassing van moderne technologieën kan leiden tot grootse successen. Toch blijkt de implementatie voor veel bedrijven nog niet zo makkelijk. Tijdens deze bijeenkomst zal Alexander op basis van de trends inzoomen op de waarde die data science aan uw business kan toevoegen en concrete handvatten bieden om er direct mee aan de slag te gaan. Nieuwsgierig geworden? We kunnen alvast in een tipje van de sluier oplichten in dit artikel: Top 4 Data Science Trends that will have a serious impact on your business in 2018!

1. Data science inspiratie
Flevum Directors Challenge

2. 2
Merwin de Jongh
Founder & CTO
Data science inspiratie
Introduction
Alexander van Eerden
Founder & CEO

3. 3
Data science inspiratie
Een korte introductie over Building Blocks
Tilburg
Amsterdam
2013
Consumer predictions
for retail and insurance
Blocks technology Some clients
43

4. 4
Data science inspiratie
Meerdere awards gewonnen in 2017 en 2018
32

5. 5
Descriptive ActionPredictiveDiagnostive
Simple
Complex
Complexityofdatastructures
Data analytics sequence
Data science
Big data
Business intelligence
What is data science?
Data science inspiratie

6. 6
Data science
What is data science?
Data science inspiratie
Artificial intelligence
Machine learning
Deep learning
Business knowledge
Data architecture
Decision making
Visualization

7. 7
Data science inspiratie
Consumer needs are changing, requiring data driven personalization
To maximize value we put the customer journey
central and rebuild the personalized customer
experience that was lost in the age of digitization
based on the data we have

8. 8
Consumer needs are changing, requiring data driven personalization
Predict
Our blocks generate consumer predictions in every phase of the customer
journey, empowering you to deliver personalized offers and services.
Data
processing
Variable
selection
Model
selection
Validation
& checks
How our blocks work
The best consumer predictions for retail and insurance

9. 9
Consumer needs are changing, requiring data driven personalization
Combine the blocks to optimize the customer journey
Combining the blocks from different phases enables you to optimize the customer
journey, resulting in higher value for you and your customer
Combine the blocks Optimize their value
Higher conversionOptimized margins
Maximize CLVMore cross-selling

10. 10
Consumer needs are changing, requiring data driven personalization
PREVENTIVE CUSTOMER SERVICE
Predict the questions of your
customer and be able to proactively
help them
SMART ASSORTMENT AND
INTELLIGENT REPLENISHMENT
Sell optimal products and have optimal
stock levels in all outlets
DYNAMIC PRICING
Offer products at a price that
optimizes margin or profit
And combine them to get solutions:
PERSONALIZED RECOMMENDATIONS
Predict the most relevant products in
your assortment based on your
customer's taste
We use generic Blocks:
Building Blocks offers ready-to-use SaaS solutions based on reusable components

11. 11
Business results
98% decrease in unsubscribers for e-mail marketing
151% increase in click-through rate in direct mailing
54% higher conversion rates on entire e-mail marketing
85% less mails send
Consumer results
Less irrelevant mails for consumers.
Increased consumer satisfaction
Relevant holiday suggestions
BLOCKS
Taste
Predicts the holiday options that
are best compatible with a
customer’s taste
Timing
Predicts the time with the highest
conversion rate
Recommendation
Predicts the holiday package with the
best fit with an individual consumer
CORENDON: ARTIFICIAL ASSISTANCE FOR INDIVIDUAL
HOLIDAY RECOMMENDATIONS
RECOMMEN
DATION
Products Services Use cases AboutPropositionVision

12. 12
Consumer needs are changing, requiring data driven personalization
Jeans centre demo case

13. 13
Consumer needs are changing, requiring data driven personalization
We add intelligence to robots to increase customer satisfaction and employee productivity
Personal interacts
with customer
Efficiently handles
customer intercations
Learn from each other
Employee Robot
Personalized and efficient
customer experience

14. 14
Consumer needs are changing, requiring data driven personalization
The solutions of Building Blocks have proven their success
6 weeks after kick-off
Self learning service robot
Can recognize >30% of incoming
questions generating an answer for those
Resulting in 12% time reduction on the
service department
Without additional input from customer

15. 15
Consumer needs are changing, requiring data driven personalization
The current customer service is not customer-centric nor efficient
human
▪ Personal interactions and
able to add emotions
▪ It can take long for a
customer to receive an
answer
▪ Employees spend time
searching for objective
information
Agent
▪ Efficient, handling easy
questions quickly
▪ Chatbots can fail at having
normal conversations
▪ This makes them not
customer friendly, for
example due to choice
menus
Robot
robot

16. 16
Consumer needs are changing, requiring data driven personalization
Ideally the Robot and the agent would complement each-other
Agent
Robot
Agent
Agent
Customer

17. 17
Consumer needs are changing, requiring data driven personalization
Ideally the Robot and the agent would complement each-other
Agent
Robot
Agent
Agent
Customer

18. 18
Consumer needs are changing, requiring data driven personalization
Ideally the Robot and the agent would complement each-other
Agent
Robot
Agent
Agent
Customer

19. 19
Consumer needs are changing, requiring data driven personalization
The self-learning element of the robot, improve results over time
Customer starts
New conversation Robot uses topic
discovery block to
recognize question
Robot recognizes question with no
need for personalization handles it
automatically
Robot recognizes question and
need for personalization. Sends
objective information to agent
Robot does not recognize question
and sends to agent
Robot learns from answer, enabling him
to recognize it in the future
Robot learns from answer, enabling him to
suggest personalization in the future
Robot adds question to database, further
improving his ability to recognize this
question

20. 20
Consumer needs are changing, requiring data driven personalization
The way the self-learning is set up is scalable
Time
% handled by agent
% handled by robot
Automated process
Able to handle new product releases
Ability to connect knowledge bases, FAQ
or forums

21. 21
Consumer needs are changing, requiring data driven personalization
The internet of things will start to show its true potential

22. 22
Data science inspiratie
Are there any questions?