Modern aspects and trends of Customer Intelligence development

The current digital transformation provides great prospects for both businesses and consumers in the field of data collection and processing. The current development of data collection and processing systems opens not just new, but breakthrough opportunities for business in general and marketing in particular. Now not only structured data is being processed, but also unstructured data is becoming a part of marketing analytics.The aim of this research is to consider and present current aspects, tasks and trends of Customer Intelligence (CI) as the part of Marketing Intelligence. Today any organization can use digital tools and methods in their marketing activity. Companies have got huge ability through a lot of internal and external resources to gather and alter (processing) customer data, including personal feedback from customers. In their turn customers get more and more possibility to contact brands and companies directly. The involvement of consumers in a dialogue with a business and their influence on all areas of business activity have already become an integral part of our life.Authors did a literature review of 28 scientific and business resources to determine the nature and the role of CI in marketing activity through a prism of marketing intelligence and its four components - market, product, competitive and customers. Nowadays Customer Intelligence is one of the key marketing activity components with a huge capacity which makes a real impact on marketing performance.

Marketing Intelligence, Data Driven Marketing, Customer Intelligence, Resources for Customer Intelligence, Customer identification, Customer attraction, Customer retention, Customer value development

1. Chen H., Chiang R.H.L. and Storey V.C. (2012). Business intelligence and analytics - From big data to big impact, MIS Quarterly, 36, 1165-1188.

2. Amarouche K., Benbrahim H. and Kassou I. (2015). Product opinion mining for competitive intelligence. Procedia Computer Science, vol. 73, 358-365.

3. France S.L. and Ghose S. (2018). Marketing analytics: Methods, practice, implementation, and links to other fields. Expert Systems with Applications, vol. 119, 456-475.

4. Nguyen Anh Khoa Dam, Thang Le Dinh and William Menvielle. (2019). Marketing Intelligence from Data Mining Perspective - A Literature Review, International Journal of Innovation, Management and Technology, 5 (10).

5. Worldwide Semiannual Digital transformation SpendingGuide, Internationa lData Corporation URL:https:// www.idc.com/getdoc.jsp?containerId=prUS45612419, (date of the application 03.03. 2020)

6. © Cambridge University Press, URL: https:// dictionary.cambridge.org/ru/% D1%81%D0%BB%D0%B E%D0%B2%D0%B0%D1%80%D1%8C/%D0%B0%D0 %BD%D0 %B 3%D0% BB%D0% B8%D0%B9%D1%81% D0%BA%D0%B8%D0%B9/customer-intelligence, (date of the application 26.02. 2020)

7. Philippe Baecke, Dirk Van den Poel. (2012). Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison. EXPERT SYSTEMS WITH APPLICATIONS, 12105-12113.

8. Navarro-García A. et al., (2016) Market Intelligence Effect on Perceived Psychic Distance, Strategic Behaviors and Export Performance in Industrial SMEs, 3(31), 365-380.

9. Chan Joseph O. (2005). Toward a Unified View of Customer Relationship Management. Journal of American Academy of Business, 37.

10. Liang T.P. and Liu Y.H. (2018). Research Landscape of Business Intelligence and Big Data Analytics: A Bibliometrics Study.

11. Rygielski C., Wang J.C. and Yen D.C. (2002). Data Mining Techniques for Customer Relationship Management, 4(24), 483-502.

12. Fan S., Lau R.Y.K. and Zhao J.L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 1(2), 28-32.

13. Doan A., Ramakrishnan R. and Halevy A.Y. (2011). Crowdsourcing Systems on the World-Wide Web, 4(54), 86-96.

14. Park D.H. et al. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 11(39), 10059-10072.

15. Sivarajah U. et al. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.

16. Skorobogatyh I.I., Efimova D.M. (2019). Marketingovye issledovanija i situacionnyj analiz, 39.

17. Jean Paul Isson. (2018). Unstructured Data Analytics, 35,38.

18. Ngai E.W.T., Xiu L. and Chau D.C.K. ( 2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 2(36), 2592-2602.

19. Amado A. et al. (2018). Research trends on big data in marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 1(24), 1-7.

20. Baars H. and Kemper H.G. (2008). Management support with structured and unstructured data - An integrated business intelligence framework. Information Systems Management, 2(25), 132-148.

21. Hosseini S. M. S. , Maleki A. and Gholamian M.R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 7(37), 5259- 5264.

22. Payne A. and Frow P.J. (2005). A Strategic Framework for Customer Relationship Management, 4(69), 167-176.

23. Markey R. (2020). Are you understanding your customers? It is time to start measure and managing their worth. Harvard Business Review, January-February.

24. Solomon J.. “What is Customer Life Time Value (CLTV). Why is it considered an important SaaS Metric”. URL: https://www.chargebee.com/ blog/saas-metric-customer-life-time-cltv. (date of the application 29.03. 2020).

25. Baker L. “Amazon’s Search Engine Ranking Algorithm: What Marketers Need to Know”, URL: https://www.searchenginejournal.com/amazon-search- engine-ranking-algorithm-explained/265173/, (date of the application 05.02. 2020).

26. A conversation with Vanguard chairman emeritus Jack Brennan. Over time, the market will demand this information. Harvard Business Review. January-February 2020.

27. Rhiannon Gainor and France Bouthillier. (2014). Competitive Intelligence Insights for Intelligence Measurement. International Journal of Intelligence and Counter Intelligence, 27, 591.

28. Econsultancy and Adobe. (2019). Report “Digital Trends”, 13-19.