Business Intelligence: OLAP

This post will explain the relationship and the difference between online analytical processing (OLAP) systems and customer relationship management (CRM) systems within a business intelligence (BI) program. Also this post will explain how this relationship bolsters an organization’s marketing efforts.

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Within a Business Intelligence (BI) program online analytical processing (OLAP) and customer relationship management (CRMs) are both applications have strategic uses for the company and are dependent on the data warehouse to help analyze multidimensional datasets stored in them to provide data-driven solutions to queries. They are both systems that require data analytics to turn all the multidimensional data into insightful information. OLAP’s multidimensional view of the data warehouse data sets can occur because it is mapped onto n-dimensional data cubes, where data can then be easily rolled up, drilled down, slice and dice, and pivot (Conolly & Begg, 2014). OLAP can have many applications outside of customer relationships.  Thus, OLAP is more versatile compared to CRMS, because CRMs are more targeted/focused with their approach, analysis of the customer relationship to the company/product.  CRMs main goal is to analyze internal and external data stored in the data warehouse, to come up with insights like “predicted affinity to buy” of a customer, the “cost or profit” of a customer, “prediction of future customer behavior”, etc. (Ahlemeyer-Stubbe & Shirley, 2014).  The information gained from the CRM can empower employees at the company on a customer’s affinity towards a product to either sell similar items or items of the result in a market basket analysis.

OLAP is the online analytical processing application, which allows people to examine data in real time from different points of view in aid to driving more data-driven decisions (McNurlin et al., 2008).  With OLAP, computers can now make what-if analysis and goal-based decisions using data. The key ability of OLAPs systems are to help answer the “Why?” question, as well as the typical “Who?” and “What?” questions (Conolly & Begg, 2014).  Connolly and Begg (2014) further explain that OLAP is a specialized implementation of SQL. Unfortunately, data queried is assumed to be static and unchanging.  Hence, the low volatile aspect of a data warehouse, with multidimensional databases is ideal for OLAP apps.  They value of the data warehouse does not come from just storing the right kind of data, but through making and conducting analysis, to solve queries that will in the end help make data driven decisions that are the best for the company.  According to Conolly & Begg (2014), OLAP tools have been used in studying the effectiveness of marking campaigns, product sales forecasting, and capacity planning.  However, it is of the opinion of Conolly & Begg (2014) that data mining tools can surpass the capabilities of OLAP tools.

CRMs, on the other hand, focuses a wide range of concepts revolving how companies store, capture and analyze customer, vendor, and partner relationship data. Information stored in CRMs could be interactions with customers, vendors or partners, which allow the company to gain insights based on previous interactions and could even be grouped/associated into different customer segments, market basket analysis, etc. (Ahlemeyer-Stubbe & Shirley, 2014). CRMs can assist in real time with making data-driven decisions with respects to a company’s customers (Mcnurlin, Sprague, & Bui, 2008).  The goal is to use the current data, to help the company build more optimal communications and relationships with it customers, vendors or partners.  Both internal and external data of the company is usually added to the data warehouse for the CRM. Through the use of the internet, companies can study more about their customers and their noncustomers, to aid a company to become more customer centric (McNurlin et al., 2008).  McNurlin et al. (2008) stated a case study with Wachovia Bank purchasing a pay-by-use CRM system from salesforce.com.  After the system was set up within six weeks, sales reps had 30 more hours to use on selling more bank services, and managers can use the data that was collected by the CRM to tell the sales reps which customers would have the highest yield.

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