Presentation on my dissertation

1. FACULTE DES LETTRES ET SCIENCES HUMAINES
DEPARTEMENT D’ANGLAIS ET
INFORMATIQUE DES AFFAIRES
Mémoire réalisé par :
TADIAMBA PAMBI Augustin
ANNEE ACADEMIQUE 2018-2019
Directeur : Prof. KAFUNDA KATALAY Pierre
Co-directeur : Prof. SANGABAU Raymond

3.  CONTEXT ET MOTIVATION
PROBLEM AND HYPOTHESIS
THE OBJECT OF THE STUDY
 THE OBJECTIVE OF THE STUDY
METHOD
RESULTS
CONCLUSION

4. Currently, telecommunications companies find
their strength in customers. It is important for
the latter to manage these customers in a
progressive and scrupulous way through their
daily consumption of products. This management
embodies the follow-up of customers in terms of
performance.

5. The lives of these companies depend on the customers
they own and how they invest in them. This gives rise
to the idea of knowing the level of performance of
customers or the level of customer behaviour within
these companies.

6. Today, these companies have more than 9
million customers, commonly known as
subscribers. The latter behave differently
in terms of their social classes, wages,
ages, gender or what they earn
individually per day, month or year, in
short their profile.

7. The knowledge of the performance of
customers within this company with a large
volume of data received every minute, that is
to say the one to know which customer is more
efficient than the other in terms of
consumption or who consumes the most and
he has what age to direct him towards the
offers that suit him best in order to increase
his loyalty(customer loyalty).

8. To this end, we propose, in our work, to build a Data
warehouse considered as our data source to analyze the
performance of Vodacom's customers using the
decision tree method. Thus, the decision-making tool
that will be implemented will allow us to deduce
whether or not the customer is performing well by
taking into account the credits consumed per day or
month. It will have the advantage of generating the
idea of customer loyalty through offers or bonuses and
managing their departure. Thus, when Vodacom notices
that a customer no longer consumes a lot of credits
than before, it will look for ways and means to make
him/her even more efficient, i.e. to pump out bonuses
thanks to the knowledge of what he/she wants most
(credits) in order to strengthen his/her loyalty and
encourage him/her to always be a successful consumer.

9. THE OBJECT OF THE STUDY
Seek for analyzing the performance of
customers of a telecommunication
company called Vodacom Congo

10. proposing to decision-makers of Vodacom Congo
a decision-making tool for analyzing the
performance of customers to be aware of their
behaviour within the company in terms of
recharge

11. For the realization, this work was based on the
decision tree method with the CART algorithm to
implement our predictive tool. A tree decision
constitutes one of the supervised learning methods
carried out on the knowledge gained from the
expertise of a supervisor. A decision tree is a
structure that allows you to infer a result from
successive decisions, we used it for illustrating the
result of an analysis.

12. Thus, the study we investigated on the peroformance of
customers of the Vodacom company, has first led me to
have a dataset at my disposal on excel containing the data
of some customers pour loading them on Orange for the
analysis for we to infer from the performance on the latter
ones and unperformance. So, we did not stop only with a
simple dataset. Hence, we drew from it some knowledge to
have the results of our analysises with the tree decision
generated from Oraange
The results obtained are presented as follows:

13. Figure IV. 13. Architecture de notre outil (arbre de décision)
Tenons en compte l’exemple d’un client dont la recharge de la journée est inférieure à 31 et dont celle du soir
inférieure à 15, il n’est pas performant. Mais celui dont la recharge du soir est supérieure à 15, nous testons
encore la recharge du soir, alors si celui dont la recharge du est supérieure à 20, il est performant. Mais celui dont
la recharge du soir est inférieure à 20, nous testons encore la recharge de la journée, donc celui dont la recharge
de la journée est inferieures à 25, n’est pas performant.

14. confusion Matrix
Nous avons 83 données qui devraient être classées négativement et 116 données qui
devraient être classées positivement.
Ainsi, le modèle prédit pour les données qui devraient être classées négativement, à
98,8% qui sont des vrais négatifs et un taux d’erreurs de 1,2 % qui sont des faux
négatifs.
Sur 116 données qui devraient être classées positivement, le modèle prédit à 99,1%
des vrais positifs et un taux d’erreurs de 0,9% qui constituent des faux positifs.

15. This work focused on the decision tree to analyze the
performance of Vodacom's customers, which is one of the
telecommunications companies in the DRC. The decision
tree method has probably helped us to determine which
customer is performing or not in order to prepare the
company's decision-makers to make decisions for the
company's health.

16. Moreover, the cutomer being an important element for all
company, is called to be satisfied, loyal,listened and known
individually. So, imagine that a company does not have
any predictive tool and wants to achieve its objectives on
the customer. Hence, for it to carry them out successfully,
it must be directed towards a knowledge of each customer
individually through this predictive tool. This knowledge is
essential for developing a lasting relationship with him and
proposing him an appropriate offer.

17. With this tool, the company will be able to strengthen
sales, increase results, reduce churn, improve loyalty,
improve the quality of contacts, and make the customer
an ambassador. So, we see how much this tool
contributes to customer relationship management. Any
decision to be taken by the company on the customer
will quickly become operational only by referring to this
tool, because to each customer performance test must
be applied a decision. This decision is nothing other
than to implement new strategies for the company's
perpetual health.

18. Hence, to achieve this, the use of decision trees has been
useful to us, through its performance in prediction. Thus,
to give them meaning, the prior presence of a sample of
data is important, it was simulated because of the
difficulties experienced in obtaining it from the company,
and it is thanks to this sample that we were able to
implement our predictive tool.
Despite the lack of a significant amount of data, the
precision estimate was satisfactory for our model.
However, there is always a way to optimize it.

19. In short, we believe that our modest work will help
the company's decision-makers to be able to
increase customer loyalty and predict churn by
taking into account their performance
(consumption capacity by customers) embodying
the way they consume Vodacom products, which in
our case are none other than the credits recharged
or consumed. It directs them towards the idea of
maintaining a lasting relationship between its
customers and the company. Finally, we will not
fail to say praiseworthily and unpretentiously that
this work is of vital interest to anyone who wants
to carry out investigations in this field because
they have all the necessary information.

20. However, we acknowledge the fact that the
work was not perfect. We therefore welcome
your comments and constructive criticisms in
the hope of bridging the lapses in the work
and making any further research on a similar
topic more effective.