Presentation of Artelnics, a company specialized in advanced analytics.

2. Index
Introduction
Advanced analytics
Many methods
Company
Technology:
OpenNN
Neural Designer
Some customers
Business cases:
Increasing sales of telemarketing campaigns in a bank
Reducing churn of customers in a telecommunications company

3. Nowadays, the amount of data created and
stored in organizations is increasing significantly.
But most of them are stuck at
lower-value descriptive analytics.
More sophisticated analysis can bring greater business value.
Introduction

4. These techniques are used to discover intricate relationships,
recognize complex patterns or predict current trends in your data.
Advanced analytics
Advanced analytics obtains useful insights that result in
smarter decisions and better business results.
What
Happened?
Why did it
Happen?
What will
Happen?
How can we make it
happen?

5. There are many techniques for advanced analytics
(k-nearest neighbours, decision trees, neural networks…).
Main methods
Neural networks is considered the
most powerful method for advanced analytics.

6. Artelnics is a company specialized in the development of
advanced analytics technology based on neural networks.
Company
Our team has more than 15 years of experience in applying
these methods to different sectors.

7. Artelnics develops the world-class neural networks library OpenNN.
OpenNN
OpenNN has been applied to many innovation projects:

8. We also develop Neural Designer, a professional tool for
advanced analytics.
Neural Designer
Neural Designer allows data scientists to build the
most powerful models in a simple way.

9. Some of our customers

10. Increasing sales of
telemarketing campaigns in a bank
BUSINESS CASE 1

11. A bank wants to predict which customers will buy a certain product,
by analyzing the data from previous campaigns.
The initial conversion rate is 1%.
Data set Value
Number of customers: 1 million
Number of features: 500
Total data: 500 million
Objectives
conversionfailure

12. Neural networks can analyze any number and type of data.
We designed a neural network that predicts the
probability of conversion for every potential customer.
Predictive model

13. We have multiplied the conversion rate by x2.5.
If we call all potential customers the conversion rate is 1%;
but selecting those customers with more than 50% of probability,
the conversion rate increases to 2.5%.
Conversion rates
initial final
1 % 2.5 %

14. The profit for the company increases in $400.000.
If the unit cost per contact is $5, and the unit profit per sale is $1.000,
we can maximize the total profit by calling the top 35% of customers.
Achieved profits

15. Reducing churn of customers in a
telecommunications company
BUSINESS CASE 2

16. Objectives
Data set Value
Number of customers: 3 million
Number of features: 600
Total data: 1.8 billion
A telco wants to predict which customers will leave the company,
in order to carry out a retention campaign and prevent churn.
The churn rate is 4%.
loyal
churn

17. We conduct exhaustive tests to corroborate that
our predictive model is reliable.
Binary classification tests
Test Value
Accuracy 76%
Sensitivity 77%
Specificity 75%
The model is predicting churn with
more than 75%of quality for all tests.

18. Cumulative gain
Contacting 25%of the clients
we approach 75%of those who are going to leave the company.
Now we simulate the performance of a retention campaign.

19. The next step is to examine the most influential variables
for each customer, in order to make personalized offers.
Individual prescriptions
We need to offer this customer a discount on international calls.
international calls charge
total charge
data signal

20. artelnics.com
Artificial Intelligence Techniques, SL
Carretera de Madrid 13
37900 Santa Marta de Tormes
Salamanca (Spain)
Telephone: +34 923 133 612 Ext.13
E-mail: artelnics@artelnics.com
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