Examining CRM data warehouse

CRM DATA WAREHOUSE

 

OBJECTIVE OF THIS ARTICLE

At the end of this article, readers should be able to:

    • Define Data warehouse
    • Explain what customers – centric data repository is
    • Explain the types of data warehouse
    • Some key project tasks that are associated with the data warehouse.

When we said that a data warehouse is customer-centred, that means it has all the regulated data that is needed in order to solve customers issues completely. For the data to be useful, the kit must be able to answer questions that either back office or the front offices have about customers.

The data warehouse in a CCE integrates customer into a unified whole. This process brings together all customer data that are scattered in various departments in the Organisation into a unified whole so that it can be used in making important decisions about the customers.

  There are different sources through which customers’ information can be sourced in any Organisation. This has to do with departments that have to do with order processing, customer support, inquiring systems, marketing and those that deal with transaction systems.

CUSTOMER-CENTRIC DATA REPOSITORY

Customer-centric data repositories are part and parcel of a well-formulated data warehouse. They are meant to keep customers’ information for the use of both the front and the back office of the Organisation. Having a data repository will allow the back office to have a complete picture of what the customers’ value of the entire Organisation should be. Most at times, Organisation might need more than their basic customers’ information in order to meet them at the right place with the right products and services. They might need demographic information, income distribution data and some other information that might not necessarily fall into the category of customer information which is needed in order to make well-informed decisions about the customers.

crm data warehouse

DATA ANALYSIS

In analyzing data in a customer-centric enterprise, the Organisation has to adopt any of the two methods of analysis that are available, which are:

  1. Predictive Analysis
  2. Retrospective Analysis

Predictive analysis enables the Organisation to predict or forecast future behaviours or values of customers while Retrospective analysis allows the Organisation to analyses based on multidimensional activities of customers in an Organisation.

  1. Predictive Analysis: As I have said earlier, the predictive analysis looks at customers’ past in order to predict what they are likely to do in the future. This analysis makes use of data mining in order to predict customers purchase behaviour and preferences which enables Organisation to focus on their high-value customers, examine their past in order to know what they really want, and use the outcome of the analysis to come up with the right products and services for them.
  1. Retrospective analysis, on the other hand, deals with present situation and realities. It examines what is happening now in order to come up with strategies that will allow Organisation to come up with the best package for their customers. This includes online analytical processing, which looks at customers transactional data in order to deduce facts. We can also talk of query and reporting which were been used when Organisation needs to understand existing customer data by transaction, location, product and time.

You should know that as a CRM consultant, the Organisation needs to understand existing systems and access the data gaps while they are trying to put in place Analytical solutions. The Organisation must know the exact place to find missing information about their customers. The information that the Organisation needs might be found in a transactional system, customer call centre archive, web portal among others.

Questions to ask

When an Organisation management is trying to examine their data, there are some Germaine questions that they need to ask themselves.

    • Where does all the customer data reside? Are they in the right location?
    • What information is duplicated in multiple databases?
    • Can customer service representatives access the information; is it available to a stakeholder that needs it?
    • Is there room for information sharing in the Organisation? Is there any central database for information?
    • When the shared information is used, how are revenues affected?

KEY PROJECT TASKS

 First and foremost, you should know that the process associated with building and maintaining a data warehouse is known as “ITERACTIVE DEVELOPMENT” process.

As a CRM consultant, you should know that there are some back process that are involved in building and implementing a data warehouse. These are shown in the diagram below.

Diagram Fig 4.

  1. GATHER REQUIREMENT:

This refers to the process of identifying the business needs and defining the key elements and relationships important in solving he business problem.  When building a data warehouse for a particular organisation, you must be able to know what the problems are in the organisation and how they can use the resources at their disposal to solve the problem.

  1. UNDERSTAND THE LINE OF BUSINESS:

The line of business that a particular organisation is into will go a long way in determining the nature of their data warehouse.  This is also needed in order to validate the business process or logical model and providing the infrastructure to respond to the business need.

  1. DESIGN SCHEMES:

This refers to the physical implementation of he data model.  Having understand the line of business you have to design a data model that will suit the business needs.  It has to be implemented effectively form a physical perspective so that queries are completed in the desired time frame.

  1. CREATE ETL TEMPLATE:

This refers to the process of developing and implantation the transformation/loading scripts.  These define the source to target mapping and transformation rules that must be applied as data is extracted from their source location and as the target data warehouse is populated.

  1. BUILD QUERIES FOR ANALYSIS:

This is been done in order to know the proper way of using the data models that have been created. These queries are set in order to check some key questions that will assist the user in the data warehouse in understanding the value of the available data.  This allows decision makers to explore available information in the organisation, rather than more pre-defined, static user interface generally associated with packaged operational applications. 

  1. IMPLEMENT AND TEST:

Before a data warehouse can be implemented, it must be tested.  Testing before full implementation will also help the organisation build an accurate data warehouse.

SUMMARY

At the end of this lesson, we have been able to discover that:

  • In building data Warehouse all required data is needed in order to solve customers issues completely
  • The customer-centric repository is part and parcel of the data warehouse.
  • There are two types of data analysis which are:
    • Predictive analysis
    • Retrospective analysis
  • Predict analysis deals with the past behaviours of customers while retrospective analysis deals with the present.
  • Some basic project task in Data Warehouse includes: Other requirements, Understand the line of business, Design schemes, Create ETL Template, Build queries for analysis, and implement and test the data model.

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