摘要：In this paper we have presented analytics as a crucial factor in marketing decision making.Th e banking environment is turbulent and complex today.Th e client is well educated and his needs are constantly changing.He has access to lot of information and has power of choice.His digital expectations are high: he needs to access banking products and services from any place and at any time.Also,the client is leaving data everywhere: in bank`s database and at diff erent internet sites.As data are comparative advantages today,the bank is eager to collect them in order to analyze data and make marketing decisions.Analytics is helping bank in this new era of doing business.Analytics assumes analysis,interpretation and communication of understandable patterns in the data.It relies on mathematics and statistics techniques in order to fi nd new knowledge and meaning of existing data.Th ere are many analytics techniques which are based on algorithms and databases.Depending on which problem a bank needs to solve or what it aim wants to achieve,the bank uses one or more analytical techniques.Survival Analysis,Nearest Neighbor Classifi cation,Neural Network,Logistic Regression and Decision Tree are the most common techniques used in banking sector.Marketing analytics models support marketing decisions.Marketing models enable bank to predict outcome (e.g.if a client is likely to leave) or to identify diff erences between group of clients.In order to achieve results,the bank has created diff erent marketing models such as Response Models,Queues,Retention Models,Market Basket Analysis,and Win-Back Models.Marketing models are helping the bank to predict if the client will answer on off er which bank is off ering through marketing campaigns.Th e aim of these models is to create target group of clients or segment with likelihood to increase their relationship with the bank.In order to create marketing model,the bank defi nes the aim which it wants to achieve.Usually,the bank wants to keep most profi table clients and to decrease costs.After defi ning the aim of marketing model,the bank collects,analyses and transforms data needed for creationof the model.Also,it is necessary to estimate data quality.If data are no longer of high quality,there can be issue with model results.Also,the bank has to take into consideration the volume,velocity and variety of data.Large data are collected from lots of data sources and stored in data warehouse or data marts using modern technologies.Model technologies help to convert data into valuable information which can be used for making decisions.After creation of a model,it is necessary to estimate its accuracy,comprehensibility and level of confi dence in results given by the model.Also,every model has to be managed (quarterly or yearly) in order to test if the results are still valid or it has to be changed with a new model.Analytics gives competitive advantages to the bank.It can improve eff ectiveness of processes and organization and improve effi ciency in making marketing decisions.Th e bank as a profi t oriented organization tends to contact profi table customer in order to increase their value through customer lifetime value.In this way,the bank has a possibility to invest in relationship with clients which can be valuable in the long run.Analytics gives knowledge about the customer.It helps to discover pattern in large amount of data.Th e contribution of analytics can be seen in decreasing marketing costs by identifying clients who are likely to respond on marketing campaigns.Also,it contributes in pricing,channel management,selling,segmentation and product development.Today,text analytics is also important for banking business,as lots of data are unstructured and can be found in form of documents,blogs,video sharing and comments on internet sites.In order to use this kind of data,text analytics helps the bank to understand data and read them with certain limitation.However,there are also challenges which the bank faces when implementing analytics.Limited budget,employees without necessary skills for the development of models,poor quality of data,inadequate and unintegrated softer tools,problems with protection of client data as well as imprecisely defi ned aim of model can be resulted in unsatisfactory realization and poor position of analytics in the bank.In order to overcome these challenges,the bank needs to set up a strategy of analytics and to link it with all the internal processes in organization.
其他摘要：Savremene tedencije u bankarstvu stavljaju akcenat na klijenta i moderne komunikacione tehnologije.Sve veća količina podataka koja je se kumulira u bazama banaka i na internet sajtovima daje mogućnost da banka bolje upozna svoje klijente.Analiza podataka je prisutna u svim sferama bankarskog poslovanja i njen doprinos se ogleda u procesu donošenja marketing odluka.Analitika,kroz otkrivanje šablona i obrazaca ponašanja klijenata omogućava kvalitetnije marketing odluke i bolju konkurentsku poziciju banke koja analitiku koristi.Ona omogućava povezivanje poslovnih procesa i marketing odluka sa ciljevima koje banka želi da ostvari.Takođe,brojni marketing modeli,koji se baziraju na matematičkim i statističkim tehnikama,predviđaju ponašanje klijenata na osnovu prikupljenih istorijskih podataka.Na osnovu cilja koji se želi postići,banka bira marketing model a samim tim i ciljnu grupu koja ima najveću verovatnoću odgovora na marketing kampanju koju banka sprovodi.Analitika omogućava da banka izgradi dugoročne i lojaln e odnose sa profi tabilnim klijentima.
