Examining Customer-Centric Data Mining in CRM

Examining Customer-Centric Data Mining in CRM 1

Examining Customer-Centric Data Mining in CRM

 

 

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

    • Define Data Mining
    • State its benefits
    • Discuss its components.

Customer-centric Data Mining is a collection of techniques and methods that enable the business to engage and retain its share of the market.  The goal of data mining is to provide the ability to convert high-volume data into high volume information

Data Mining helps in discovering enterprise knowledge from historical data and combines historic enterprise knowledge with current conditions and goals in order to reside uncertainty about enterprise outcome.

 

 

COMPONENTS OF DATA MINING IN CRM

  1. Discovery: In this, the organization examines its data and try to discover a pattern of the transaction.  For example desolate as a telecommunication organization can discover that a large number of their youth segments likely to use their airtimes to browse the internet, which may inform the way they have come up with easy cliq platform.
  1. Exploration: Under exploration, the organization tries to work on the pattern that they have discovered.  This will now use this pattern to come up with a solution to a problem that customers in the organization have been facing while using products and services from the organization.  This can also be used in segmenting and profiling the organization’s customers models which are solutions to some of the problems discovered can be a solution to a single problem or array of problems confronting the organization’s customers.

Examining Customer-Centric Data Mining

Data Mining you should provides a formal and well-organized way of discovering a pattern in data.  Let’s say for example you have discovered raw gold in a play you now want to dig up.  That is exactly what data mining does.  It tries to find out useful information about the customers.  Which can help the organization to minor their pattern of interaction and come up with products and services that will satisfy their needs?  Data Mining applications serve information and correlated customers’ data in a comprehensive manner. Data mining tools you should know can identify the relationship that exists between historical data.

 

Data Mining applications have tools and techniques that will allow organizations to get the best out of their customer information.  They will not just store the information but they will be able to make an informed decision through the active use of data.

 

Data Mining help organization to maximize their return on investment (ROI).  Through it, the organization will be able to know the potential of their customer base.  They will be able to know which caliber of their customers are likely to make a repeated purchase and what they need to do in order to sustain their profitable customers.  Data Mining has the potential of extracting profiles of customers and identify business opportunities that can be developed through them.

 

Data Mining helps in answering two basic questions that every CCE has to ask themselves.   These  questions include:

  • Which of my high-profit customers are most likely to leave? The organization has to be proactive in order to retain them.
  • Which of my low-profit customers are least likely to leave? Raise their price and make them more profitable.

Ever Data Mining application user must be able to maximize the amount of useful information that they are able to extract from their passive customer information.  They should also be able to minimize loss of productivity that results from the information retrieval effort, prohibitive access, communications difficulties, or retrieval of meaningless, incorrect or corrupted data among others.

 

In order to satisfy the curiosity of Analysts and the need for the information to be applicable to different groups of people, Data Mining must consist of different associations of data, it must be able to works with multiple channels and networks, and it must be able to analyze, interpret and display information in a variety of meaningful ways.

 

 

NOTE:

Combining discovery-driven analysis with the more pervasive assumption – driven methods will ultimately produce the most comprehensive analysis for the greatest business benefit.

One thing you should know is that while using Data Mining, there is a high tendency to incur an unnecessary cost if the application is faulty.  Once the organization is able to get rid of unnecessary data, it will be able to put costs under control.

One other thing is that business forecasting is very essential if an organization wants to get the best form of Customer-centric Data Mining.  They must be able to use the available information about customers at their disposal to predict what sales and responses are going to be like in any given year and continue to crosscheck their data in order to validate their predictions.  The forecasting we are talking about here goes beyond seeing what cash flow is going to be like but it involves the organization predicting what makes is going to like, judging from their available historical data.

 

 

About all, the main essence of Data Mining is the ability of the organization to estimate the lifetime value of their customers, which will allow the organization to know how profitable the organization is going to be in the future.

 

 

SUMMARY

At the end of this lesson, we have discovered that:

    • Data Mining is the collection of methods and techniques that allow the organization to retain its share of the market.
    • We have two components of Customer-centric Data Mining which are discovery and exploration.
    • Data Mining allow an organization to get the best out of their customer information
    • It also helps them to maximize their ROI
    • Data Mining helps the organization’s to know how profitable it will be in the future.
    • The main purpose of Data Mining is the ability of the organization to maximize the lifetime value of their customers.

Customer-centric Data Mining applications have tools and techniques that will allow organizations to get the best out of their customer information.  They will not just store the information but they will be able to make an informed decision through active use of data.

 

Customer-centric Data Mining help organization to maximize their Return on investment (ROI).  Through it, the organization will be able to know the potential of their customer base.  They will be able to know which calibre of their customers are likely to make a repeated purchase and what they need to do in order to sustain their profitable customers.  Data Mining has the potential of extracting profiles of customers and identify business opportunities that can be developed through them.

 

 

Customer-centric Data Mining helps in answering two basic questions that every CCE has to ask themselves.   These  questions include:

  • Which of my high-profit customers are most likely to leave? The organization has to be proactive in order to retain them.
  • Which of my low-profit customers are least likely to leave? Raise their price and make them more profitable.

Ever Customer-centric Data Mining application user must be able to maximize the amount of useful information that they are able to extract from their passive customer information.  They should also be able to minimize loss of productivity that results from the information retrieval effort, prohibitive access, communications difficulties, or retrieval of meaningless, incorrect or corrupted data among others.

 

In order to satisfy the curiosity of Analysts and the need for the information to be applicable to different groups of people, Data Mining must consist of different associations of data, it must be able to works with multiple channels and networks, and it must be able to analyze, interpret and display information in a variety of meaningful ways.

 

NOTE:

Combining discovery-driven analysis with the more pervasive assumption – driven methods will ultimately produce the most comprehensive analysis for the greatest business benefit.

One thing you should know is that while using Data Mining, there is a high tendency to incur an unnecessary cost if the application is faulty.  Once the organization is able to get rid of unnecessary data, they will be able to put costs under control.

 

 

One other thing is that business forecasting is very essential if an organization wants to get the best form of Customer-centric Data Mining.  They must be able to use the available information about customers at their disposal to predict what sales and responses are going to be like in any given year and continue to crosscheck their data in order to validate their predictions.  The forecasting we are talking about here goes beyond seeing what cash flow is going to be like but it involves the organization predicting what makes is going to like, judging from their available historical data.

About all, the main essence of Data Mining is the ability of the organization to estimate the lifetime value of their customers, which will allow the organization to know how profitable the organization is going to be in the future.

 

 

SUMMARY

At the end of this lesson, we have discovered that:

    • Data Mining is the collection of methods and techniques that allow the organization to retain its share of the market.

 

Examining CRM Data Warehouse in Organisations

 

 

At the end of this section readers should be able to:

    • Define CRM 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 the 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.

Examining CRM data warehouse in organisations

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

The 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.

 

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.

 

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 the 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 the management of particular 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 FOR CRM DATA WAREHOUSE

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

As a CRM consultant, you should know that there is some back process that is involved in building and implementing a data warehouse.

 

  1. GATHERING REQUIREMENT AS PART OF CRM DATA WAREHOUSE ACTIVITIES:

This refers to the process of identifying the business needs and defining the key elements and relationships important in solving the 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 AS PART OF CRM DATA WAREHOUSE ACTIVITIES:

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. DESIGNING SCHEMA AS PART OF CRM DATA WAREHOUSE ACTIVITIES:

This refers to the physical implementation of the 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. CREATING ETL TEMPLATE AS PART OF CRM DATA WAREHOUSE ACTIVITIES:

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 are extracted from their source location and as the target data warehouse are 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. IMPLEMENTING AND TESTING AS PART OF CRM DATA WAREHOUSE ACTIVITIES:

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.

 

Examining CRM Intelligence Management Cycle

 

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

  • Define CRM intelligence management Cycle
  • Identify the four steps in the CRM intelligence Management cycle
  • Benefits of Analysis in CRM
  • Measurement indicators in CRM

 

A four process illustrates the CRM Intelligence Management Cycle which is a collection of data, analysis, action and effective management of the process.

 

#1 COLLECTION

As an Organisation formulates the data warehouse in order to cater for customer data, the Organisation must be able to evaluate and establish a mechanism that will cater to all customer data throughout the entire Organisation.

 

 

The Organisation must be able to cater for all operational and financial data of the customers in order to enable the Organisation to meet customers’ demands and understand the financial implication of current and future behaviour of the customers. The Organisation needs to gather as much as possible about the customers in order to have an insight into their behaviours and most importantly how to make their purchase decisions.

 

 

 

#2 ANALYSIS Examining CRM Intelligence management cycle

After you have gathered all the data that you need, you must be able to analyse it. You should know that CRM analysis is different from ordinary analysis. This is because the CRM analysis is more specific and complex in nature. The purpose of analyzing data include:

a. Customer Behaviour and Preferences: You should know that the more an Organisation can have a complete view of customers, the better the relationship can be conducted and fortified. Understanding customer behaviour will enable the Organisation to come up with customers based on strategies that will meet customers’ needs.

b. OPERATIONAL FACTORS: Analysis will enable channel capacity and design, sales effectiveness across multiple channels and channels effectiveness. It will enable the Organisation to know how their touch points are points and things that can be done in order to boost their effectiveness.

c. FINANCIAL FACTORS: This includes the study of customer profitability, cost allocation, consumption of resources to sell/service the customer e.t.c. In order to determine the key opportunities for improving profitability, and relate them to customer-related activities or operational activities.

In order to achieve this, there is a need for the Organisation to embark on a set of analysis and modelling tools. Analysis tools can provide the ability to evaluate customer profiles and behaviour, and identify communication potentials. In most cases, they also enable the evaluation of customer response and behaviour.

 

#3 ACTION

After the analysis of customer data, there is a need for action on the part of the Organisation. The Organisation must be able to plan and execute communication-based on analytical intelligence. The Organisation must be able to automate all types of customer communications from prospecting to multi-channel interactions over time.

In doing this, the Organisation must consider the uniqueness of every customer and the need for personalization of services for customers. The main thing that will determine this is the past behaviour of the customers.

Optimization capabilities can prioritize communications across all channels and ensure effective communications.

Optimization should be based on the priority of the message and the availability of resources to act within a particular time window. This should encompass all touch points such as direct mail, kiosks, the point of sale, call centre, web, E-mail, ATM, Store/branch and sales contacts. Optimization is intended to reduce the conflicts that may occur in channels.

 

#4 MEASUREMENT OF RESULTS:

The Organisation must measure the effectiveness and impact of its CRM activities on a regular basis to ensure the meeting of its objectives.

There are different ways by which performances of CRM analytics can be measured which includes Campaign costs, Marketing campaign gross margin and Revenue, total customers in database showing real profitability, Revenue and gross margins per customer in the database, revenue and gross margin from targeted customers and the percent change in CRM-related revenue and cost year to year.

The real value of CRM is its ability to identify customers at the point of their needs. They must be able to know what their customers want and give them exactly what they want. This will help greatly in reducing attrition in the Organisation.

If the Organisation can identify their customers at the point of their needs, they will be able to render personalized services to their customers. If the Organisation is able to meet its customers through the appropriate channel, it will enhance customer relationships which will ultimately lead to an increase in marketing success and customer loyalty.

 

 

Above all, with analytical CRM, an enterprise-class data warehouse and a well-formulated knowledge of the customer base, an Organisation will be able to satisfy the interest of its shareholders and stakeholders.

 

 

SUMMARY

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

  • CRM intelligence Management cycle has four steps which include: collection, Analysis, Action and Measurement of results.
  • Analysis has to do with the logical sequence to any data

 

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Adeniyi Salau Scrum Master Certified , CCNA R&S , BeingCert and Scrum Certified Digital Marketing Professional, CEP, MOS, MCP, CSCU (Project 2016), Microsoft Certified Security and Networking Associate is a Google and Beingcert Certified Digital Marketer, Project Manager and SEO Expert of repute with about a decade of Blogging and online marketing experience. He is always ready to share his experience with others.

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