The implementation trends of intelligence E-CRM in business process

Data quality in customer relationship management (CRM): Literature review

“… Practitioners of CRM classification require a standardized framework with simplified DM processes that could produce satisfactory results for CRM data in general, with all the DM challenges mentioned previously (Tu & Yang, 2013).  Ahmed et al (2016) have shown that using Java programming and SQL can define appropriate constraints and get 100% accurate data. Data from different users can be standardized for more accurate information and its processing.…”

“…It is only necessary to determine what percentage of the data should be captured.  To ensure that eCRM uses the latest user data, organizations can apply rules that require the use of different date formats from the same source, so the one with the most recent date will be selected and saved (Ahmed et al, 2016). The mobile CRM system is explicitly designed to work with data from a central CRM system, which provides sufficient data accuracy.…”

“… Inadequate integration of different data sources, so each product has unique identifiers in the database and storage. In order to see the relevant data of the user's order, it is necessary to access the files in the database (Ahmed, Amroush & Ben Maati, 2016).  Data collected and stored can have defects such as: incorrect (typing errors, misspelling, etc.)…”

Data Analytics in CRM Processes: A Literature Review

“…Among the seven data mining techniques, clustering (7 out of 51 articles, 14 %) is the most common data mining technique for which data analytics is used to support decision making. [20], [22], [23], [32], [36], [38] [35], [42], [51], [55], [59] Full list of reviewed publications with classification is available at https://drive.google.com/open?id=0Bwp9RlyV--pwcFg1dC1kSzlMNG8 VI. CONCLUSION Application of data analytics in CRM is an emerging trend in the industry.…”

“…Among the four CRM dimensions, customer development (19 out of 51 articles, 37.3 %) is the most common dimension for which data analytics is used to support decision making. [18], [27], [40], [46] , [47] , [50], [55], [67] Customer Attraction 16 31 % [19], [20], [29], [34], [37], [44], [45], [49], [52], [53], [57], [59], [61], [65], [66], [68] Customer Retention 7 14 % [17], [21], [24], [26], [28], [35], [64] Customer Development 19 37 % [3], [22], [23], [25], [30], [31], [32], [33], [36], [38], [42], [43], [48], [51], [56], …”