|
|
Strategy Competitive Fight
Vyhlídal, Martin ; MBA, Stanislav Heža, (referee) ; Bartes, František (advisor)
The thesis deals with finding such a competitive strategy, which enables a well-established Copany to remain on the leading edge of sales finance market in the Czech Republic. To do that, it is necessary to acquire as detailed information about competitors´ activities, professional environment development and end-user preferences trend, as possible. Based on this information it proposes possible models of strategic options including the impact on customers, business partners and internal issues.
|
|
|
Strategy Competitive Fight
Vyhlídal, Martin ; MBA, Stanislav Heža, (referee) ; Bartes, František (advisor)
The thesis deals with finding such a competitive strategy, which enables a well-established Copany to remain on the leading edge of sales finance market in the Czech Republic. To do that, it is necessary to acquire as detailed information about competitors´ activities, professional environment development and end-user preferences trend, as possible. Based on this information it proposes possible models of strategic options including the impact on customers, business partners and internal issues.
|
|
|
The Real Knowledge Discovery Task
Kolafa, Ondřej ; Berka, Petr (advisor) ; Kliegr, Tomáš (referee)
The major objective of this thesis is to perform a real data mining task of classifying term deposit accounts holders. For this task an anonymous bank customers with low funds position data are used. In correspondence with CRISP-DM methodology the work is guided through these steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment. The RapidMiner application is used for modeling. Methods and procedures used in actual task are described in theoretical part. Basic concepts of data mining with special respect to CRM segment was introduced as well as CRISP-DM methodology and technics suitable for this task. A difference in proportions of long term accounts holders and non-holders enforced data set had to be balanced in favour of holders. At the final stage, there are twelve models built. According to chosen criterias (area under curve and f-measure) two best models (logistic regression and bayes network) were elected. In the last stage of data mining process a possible real-world utilisation is mentioned. The task is developed only in form of recommendations, because it can't be applied to the real situation.
|
guest ::
