Unit 3 - Database Management, Data Mining & Data Integration in CRM

 

 

Database Management

Introduction

 Database management are processes that ensure proper application performance, avoid compliance risk and protect valuable business data. As the volume of business data has grown, database management has become vital to data optimization and security. Data is a corporate asset that helps make more-informed business decisions, improve marketing campaigns, optimize business operations and reduce costs. 

 

DATABASE MANAGEMENT

The management of data in a database system is done by means of a general-purpose software package called a data-base management system. The database management system is a major software component of a database system.

 A Database Management Systems or DBMS is software designed to assist in maintaining and utilising large collections of data. DBMS provides an environment that is both convenient and efficient to use".

 Database management is heart of CRM. It works non-stop at the very core of your CRM, pumping the life blood formation to and from all parts of the system, connecting management, sales, marketing, the call centre, and customer service. A contact database management system centralizes, organizes, and then shares the data seeded to get the job done.

 

Castomer database includes personal information, such as contact addresses and phone numbers, as well as, family size, location, and other demographic information. Many companies confuse a customer mailing list with a customer database. A customer mailing list is simply a set of names, addresses, and telephone numbers. A customer database contains much more information, accumulated through customer transactions, registration information, telephone queries, cookies, and every customer contact. Ideally, a customer database also contains the consumer's past purchases, demographics (age, income, family members, birthdays), psychographics (activities, interests, and opinions, media-graphics (preferred media), and other useful information.

 

Example : The catalog company Fingerhut possesses some 1.400 pieces of information about each of the 30 million households in its massive customer database.

  

Information to be included in Customer Database

 

Information to be included in Customer Database.

Prospect and customer database is the most powerful marketing and research tool that a firm must develop,

 1. Contact Names: Primary names, titles etc (search out for some groups, segments etc.)

2. Job Title and Job Definitions: It is helpful if one needs to make contact with multiple people in the company.

3. Demographic or Psychographic Information: To understand, age, likes and dislikes and life style of customers.

4. Name of the company: : with correct spellings.

5. Address:

6. Methods to Contact Them: email, website, faox, social networks, whats app no.

7. Buying History: date of first purchase & subsequent purchases and amount of purchases. Other transactional information

8. Sources of Lead: How the person came in contact with the company.

9. Sources of Sale: How the sale took place on sales closed, methors of follow-ups, complains etc.

10. Special Needs of the Customer:

  

Database Construction

It consists of six major steps.

 1    Define the database functions.

 It can have current, past or future perspective.

 It may be about individual customer, customer segments, market segments or entire market.

 It may have product information, competitor inf. regulatory data or anything pertinent to development and maintenance of customer relationship.

 ·         The purpose may be acquisition, retention on development.

 It may be Operational, Analytical or Callaborative

·         Operational to help in everyday running of business eg. Access to customer record when a telephone enquiry comes.

 

·         Analytical - Data is used to enhance value to the customer. To support marketing, sales and service decisions.

 

·         Callaborative - Data is organised in two abouve ways.

 

2    Define the information requirements

 

Those who interact with customers it. are the best to define it.

 Contact Names / Job Title & Job Definition / Demographic and psychographic inft. / Name of Company/ /History - transactional or contact/current pipeline/products/communication or preferences.

  

3    Identify Information Sources.

 Secondary Data 

      i.        Internal data from other departments - marketing, sales, customer service, finance. 

    ii.        External Data - Imported from a number of sources, eg. market research companies, marketing companies. 

   iii.        Others - club memberships, school enrolments, magazine subscribers, purchases of competitors product, supplementary products etc.

 Primary Data 

Collected first time by CRM – Sales competition entries, Subscriptions, Registrations, loyalty programs.

 

4    Select Database. Technology & Hardware Platforms 

      i.        Hierarchical- eg a family tree where child can have only one parent but parent can have many children.

    ii.        Net work -

   iii.        Relational - (Relational Database Management System-RDBMS) Where data is stored in two dimensional tables comprising of rows & columns. These have one or more fields that provide unique identification record.

 Choice of Hardware Platform in influenced by size of the Database, existing technology, number of users & location. 

 

5   Populate the Database 

It is difficult to gather even basic data of customer like email. The main steps in ensuring that the database is populated is as follows -

 

      i.        Sourcing - Organisation should develop explicit process for this eg. when interactions occur.

    ii.        Verification- A labour intensive work

   iii.        Validation – incorrect titles, miss spelt names, salutations,

   iv.        Range validation- when entries are outside possible range.

    v.        Missing values - columns are empty.

   vi.        Duplication - 

Merge & Purge - when two or more data bases are merged

 

6     Maintain the Database 

·         Ensure that date from all transactions, compaigns and communication is inserted immediately. 

·         Regularly de- duplicate databases. 

·         Audit a subset yearly & measure amount of degradation. 

·         Identify the sources of degradation. 

·         Purge inactive customers - decide the time period before purging. 

·         Drip-feed the database - Every customer contact is an opportunity to add data. 

·         Gel customers to update their own records. 

·         Remove customer records when requested.

 

  

Data Mining

 

Data Mining 

·         Is concerned with the analysis of data .

·         Use of software techniques for finding patterns and regularities in a set of data. 

·         Search for relationship and global patterns. that are hidden among vast amount of data. 

·         Identify nuggets of information for decision making, decision support, prediction, forecasting and estimation.

 

* Characterstics of Data Mining

 

Extracts Valid Information 

Analyses Data 

Reveals Information 

Provides Accurate Results 

Oltams Time Knowledge - Traditional data bases typically store only current state of data, losing historical information. But these retain information about past, present & potentially future states of real world." 

Provides Huge Paybacks. 

Easy Processing

 

* Need of Data Mining in CRM

 

Customer Profiling Information 

Database Marketing 

Merchandise Planning & Allocation 

Call Detail Record Analysis 

Customer Loyalty 

Customer Segmentation

 

 

 

Customer Data Integration (CDI)

Customer data integration (CDI) is the process of defining, consolidating and managing customer                        information across an organization's business units and systems to achieve a "single version of the truth" for customer data. This golden record is generated by integrating information from all available source systems, including contact details, customer valuation data and information gathered through interactions such as direct marketing.

A customer data integration strategy can improve business processes and enable better information sharing among departments. As a result of this focus on improving customer service, CDI efforts are an essential element of customer relationship management (CRM).

Importance of Data Integration

Although many companies have been gathering customer data for years, it hasn't always been managed effectively. As a result, companies might maintain outdated, redundant and inconsistent customer data that was gathered through phone calls, emails, websites, webchats, surveys or in-person engagements with customers.

Customer data integration policies can help establish order over the data generated by these disparate source systems. This can lead to the following benefits:

·         Improved sales. Accurate customer data enables organizations to better understand customers and personalize cross-selling and upselling opportunities.

·         Better customer service. Customer service agents can better understand the entire customer journey when responding to service calls.

·         More efficient data management on an ongoing basis. Once CDI policies and data quality efforts are in place, it becomes easier to update customer records, manage real-time data collection and consolidate data silos.

 

Types of data integration

Four data integration techniques used in CDI strategies include:

·         Data consolidation. Data is copied from multiple sources and integrated into a single data store.

·         Data propagation. Applications copy and push data from one location to another.

·         Data federation. Data federation software enables an organization to aggregate data from multiple source systems into a virtual database for use in business intelligence (BI) and other analyses.

·         Data warehousing. Data warehouses store data collected by various operational systems; the data is captured for access and analysis, rather than transaction processing.

 

Core components of a customer data integration strategy

Here are the important components of a customer data integration strategy:

1.    Define customer data and discover where it resides. Gain an understanding of the customer journey throughout the organization to understand the business processes involved with collecting and storing customer data.

2.    Analyze the data sources, as well as how and where information is saved. Craft a common definition of who the organization's customers are. Determine who accesses the data and why.

3.    Construct a customer data integration strategy. Ensure the strategy addresses data cleansing and duplication and promotes data quality.

4.    Implement the customer data integration technology. Choose tools that support data quality, governance and integration across platforms.

 

Challenges of customer data integration

While CDI can offer numerous benefits, it also presents several challenges, such as the following:

·         Data quality issues. Inconsistent or inaccurate data from legacy systems can pollute the master record.

·         Integration complexity. Merging data from multiple formats, platforms and databases can require advanced technical architecture.

·         Privacy and compliance. Compliance with local and national regulations.  Secure handling of personal customer data across all touch-points.

·         Organizational silos. Some apartments might resist sharing customer data, creating gaps in integration efforts.

Overcoming these challenges often requires executive support, cross-functional collaboration and investment in the right tools and governance practices.

Key technologies for CDI implementation

Modern CDI relies on a suite of technologies that work together to ensure data consistency and accuracy:

·         Customer data platforms. CDPs unify customer data from multiple sources into a single view, often used in marketing automation.

·         Data integration tools. ETL (extract, transform, load) tools and API-based middleware help transfer and synchronize data.

·         Data quality tools. Used to cleanse, deduplicate and validate incoming customer data.

·         Master data management systems. MDM systems provide centralized governance and management of core customer records across systems.

Selecting the right mix of technologies depends on the size of the organization, the complexity of its data environment and its specific use cases.