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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">caer</journal-id><journal-title-group><journal-title xml:lang="ru">Central Asian Economic Review</journal-title><trans-title-group xml:lang="en"><trans-title>Central Asian Economic Review</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2789-4398</issn><issn pub-type="epub">2789-4401</issn><publisher><publisher-name>Университет Нархоз</publisher-name></publisher></journal-meta><article-meta><article-id custom-type="elpub" pub-id-type="custom">caer-249</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ФИНАНСЫ И АНАЛИЗ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>FINANCE AND ANALYSIS</subject></subj-group></article-categories><title-group><article-title>BIG DATA AS A TOOL FOR TUNNEL MARKETING</article-title><trans-title-group xml:lang="en"><trans-title>BIG DATA AS A TOOL FOR TUNNEL MARKETING</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Akmedov</surname><given-names>R.</given-names></name><name name-style="western" xml:lang="en"><surname>Akmedov</surname><given-names>R.</given-names></name></name-alternatives><bio xml:lang="en"><p>PhD assistant professor</p><p>Kaskelen</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Dzhengisheva</surname><given-names>A. A.</given-names></name><name name-style="western" xml:lang="en"><surname>Dzhengisheva</surname><given-names>A. A.</given-names></name></name-alternatives><bio xml:lang="en"><p>Kaskelen</p></bio><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="en">Suleyman Demirel University<country>Kazakhstan</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2018</year></pub-date><pub-date pub-type="epub"><day>28</day><month>06</month><year>2018</year></pub-date><volume>0</volume><issue>3</issue><fpage>37</fpage><lpage>51</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Akmedov R., Dzhengisheva A.A., 2018</copyright-statement><copyright-year>2018</copyright-year><copyright-holder xml:lang="ru">Akmedov R., Dzhengisheva A.A.</copyright-holder><copyright-holder xml:lang="en">Akmedov R., Dzhengisheva A.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://caer.narxoz.kz/jour/article/view/249">https://caer.narxoz.kz/jour/article/view/249</self-uri><abstract><p>В данной работе описывается применение программного обеспечения по сбору данных в маркетинге. Также в данной работе упомянута проблема с "Туннельным видением" которая рассматривается, как очень прогрессивная, которая исходит из-за некачественной информации о клиентах, которые компании собирают на конкурентном рынке. Применение новых программных обеспечений описаны как инструменты, способные усилить операции направленные на расширение данных о клиентах, с целью применения необходимых маркетинговых действий.</p></abstract><trans-abstract xml:lang="en"><p>Purpose – is to describe the relationship between Big Data and personalized marketing. This work’s object is to clarify how Big Data can become a point of new visions on marketing activities designed to customers and potential consumers.Methodology – Quantitative research is applied for this study to collect the amount of companies that has already used Big Data tools and their ability to create content that is most applicable by customers. Nonrandom sampling method is used in this study due to limit in time resources and money resources. In order to gather data about customers behavior toward advertisements through digital and non- digital tools and their purchase intention arising from these advertisements, random sampling method was applied.Originality/Value – Given research can be helpful to explain how Big Data can strengthen the effectiveness marketing activities of companies. Usage of Big Data can lead to get deeper knowledge about consumers of companies that will make these companies closer to their consumers. This research is also showing how marketing costs are reduced by using Big Data as a marketing tool that will help to avoid wastes. Big Data must be learned as a potential for marketers to increase the effectiveness of their activities with efficient inputs. It is important to understand how this software is able to solve any particular problems that marketing people face in the real business conditions.Findings – Implementing Big Data as a marketing tool can helpfully strengthen marketing activities by collecting and absorbing information about clients and create personalized content. This content will have an effect on capacity of customers that company has and it depends on how the information collected and analyzed by Big Data will be directed and used. From provided (ANOVA) test of H3 we can see that the difference in the number of customers present according to presence of Big Data as a tool in operations. Which showed that companies will have more clients when Big Data is applied. In the case of test of H4 (table 10), the direction from attention to purchase intention is constructed. The significant difference exists in all concepts which have been applied as components on the way to purchase intention of customers.</p></trans-abstract><kwd-group xml:lang="en"><kwd>Big Data</kwd><kwd>personalized marketing</kwd><kwd>customer capacity</kwd><kwd>purchase intention</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Adrian, M., Chamberlin, D. Orbitz Worldwide Uses Hadoop to Unlock the Business Value of Big Data. Gartner. – 2012. – URL: https://www.zotero.org/groups/leapforward/items/itemKey/BSHHXBHN (accessed: 06.11.2017)</mixed-citation><mixed-citation xml:lang="en">Adrian, M., Chamberlin, D. 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