Thursday, December 20, 2012

The main purpose of this project is to provide reliability assessment for MapInfo software in alignment with CRM ecosystem. MapInfo would extend and visualize spatial data structures in CRM ecosystem and value chain process through geographic analysis patterns. Visualized CRM value chain process promotes decision making in CRM ecosystem.
CRM value chain identifies key steps in CRM ecosystem strategy development process and it determines how optimal customer strategy aligns in variety business models. Innovative CRM value chain process ensures among others high value products, low cost performance, and delivers competitive advantage for customers.
Over one hundred CRM systems exist in the business markets with and without GIS technology. Certain business enterprises align CRM framework with basic GIS coordinates. CRM solution is based on traceability for customer’s needs; therefore, it targets data mining techniques for analyzing customers’ data model. Metadata for business transactions and customer preferences are accessible for use of partners within CRM ecosystem.
GIS frameworks deal with storage, retrieval, mapping, and analysis of geographic/spatial data.  GIS applications automate query execution in database and show metadata in the form of maps to end users. GIS can translate implicit geographic data (such as a street address) into an explicit map location in terms of x and y coordinates. MapInfo software uses a range of desktop GIS functions in order to visualize spatial phenomena.
The project tests reliability of MapInfo alignment in CRM ecosystem by using three case studies. Alignment benefits would boost CRM ecosystem and value chain process through geographic analysis pattern. Effective use of CRM value chain process enhances customer lifecycle profitability. Deductive reasoning method employs primary from the more general to the more specific. Project concept initiates with theoretical framework about topic and it narrows that down into more specific hypotheses. Implementation method is based on join tables in MapInfo (customer metadata) and (customer geographic map). Apply a set of synthetic matching scenarios in three case studies with regard to showing real life events because of privacy and data protection. GIS buffer and proximity analyses can be used by customer service team for capturing data in CRM ecosystem and value chain process. Case studies are based on “manufacturing efficiency and product synchronization”, “logistical performance”, and eventually “marketing assessment”.
This paper suggests alignment of MapInfo technology in CRM ecosystem due to the fact that MapInfo alignment supports visualization algorithms in CRM value chain process according to three case studies. Transparency ensures optimal decision making for future collaboration with customers. Progressive CRM value chain builds ultimate competitive advantage for ecosystem and collaborative partners. Small and medium sized CRM enterprises can align MapInfo and retain present CRM technology with maximum ROI for customer service portals without interoperability issues.  

Key Words: Customer Relationship Management (CRM), Geographic Information System (GIS)

 MapInfo professional extends traceability approach to CRM value chain and neighborhood partners

General implementation

Map implementation

Three case studies

This project uses three case studies in order to test reliability of MapInfo in alignment with CRM ecosystem and exams visualization of spatial data structures in CRM ecosystem and value chain process.  Transparent spatial CRM value chain ensures optimal business decision making model. Three Case studies reviews how customer transparency extends from ecosystem boundaries to neighborhood partnerships. Case studies focus on customers’ needs and domains of ecosystem by spatial data. Transparent activities in customer domains enhances reliability in service delivery. 

Implementation of the first case study

SQL statements can be selected by customer service team in CRM ecosystem. Desire algorithms allocate specific customer profiles on MapInfo desktop application.  SQL statements can be allocated number of customers, who purchase product Q-500. 


Implementation of analogical mapping in the first case study

The outcome of geographic analysis patterns in the first case study leads to seven analogical mapping. Buffering around polygon features shows attractive attributes in customer neighborhood. Similar characteristics requires assessment in part of geographical data. Analogical mapping implies further investigation of similarities and customer contacts.
Spatial approach in the first case scenario is based on filtering of customer locations where effects on purchasing products. Customer service team can scrutinize geographical variations, new product feature model, and customer product strategy. Spatial data identifies key factors beyond marketing automation platforms which leads to profitability for all transaction partners.

Implementation of the second case study
The second case study focuses on logistical performance and distances between customer locations and dealership location.
Company M_100 manufactures innovative security products for wide variety business activities. Manufactured products transport by aircrafts, ships, and trucks to customer enterprises. Company M_100 discovers a better way of transporting products to customers. Curtailing costs is number one priority in logistics activities. Company P_200 is a transportation firm and purposes the best suited freight rate structure for Company M_100. Structural model is based on transportation of products within certain regions and distances. The freight rate structure is cost-effective way to enhance activities within logistics in Company M_100. Company P_200 becomes a new logistic partner for Company M_100. They offer special discount freight rate to customer enterprises where located north central region of Phoenix within four hundred miles away from logistical center in Company M_100.


Implementation of the third case study

The third case study focuses on marketing performance in CRM ecosystem.
Company T_700 locates in Florida. Product design manager in product factory, where locates in California, would like to do marketing research for one of the selective stores of CRM ecosystem.  One of the stores of CRM ecosystem locates in Sweden. Product design manager wonders how many men between the ages of 20 and 40 reside 10 KM from store.


The outlook of current CRM market

The successful CRM implementations among enterprises pave the way for CRM applications to become a densely populated market. CRM key customer success expands with popularity in wide variety industries like securities, telecommunication, medicine, consultation, insurance, network technology, manufacturing, and banking. Over one hundred sorts of CRM technologies exist in customer service delivery platforms.
Customer centric point of view is main goal of CRM strategy and customer service platforms. Most present CRM frameworks in the market miss geographic context analysis and spatial interoperability. Encapsulated CRM software with context modeling for geographic applications are built by different vendors today but their system applications are not compatible with present CRM software (without embedded GIS functions). Google earth can be used as an alternative by enterprises, which adapt CRM portals with template coordinate systems in customer database. Google earth applications visualize CRM customer metadata and spatial phenomena; however, structural functions for spatial analysis apply only to constraints using. CRM implementation requires a large capital investment for technology and employee education program; therefore, small and medium sized enterprises prefer to retain present CRM frameworks. Large enterprises target an encapsulated CRM and GIS framework in current IT market status.
MapInfo software has potential to deliver visualizing spatial data to small and medium sized enterprises without interoperability issues. MapInfo software can also promote visualization of CRM value chain process with lower maintenance costs for tight budget environments according to software reliability assessment model in this project.