Customer database formation to build customer relations

By Avishek Majumder & Abhinash on May 1, 2019

A customer database forms the basis of what is commonly known as Customer Relationship Management (CRM). It is much more than just a client database; it is the management of all interactions with the customers. A customer database, for small businesses often start out as a small excel worksheet whereas larger organizations use different tools to manage millions of customers for a specific time period. The customer information database, therefore, is an organized collection of comprehensive info about individual customers and prospects. The information must be updated, accessible, and actionable.

According to Buttle, (2013) the steps of forming a customer database comprises of;

Different steps of customer database formation
Different steps of customer database formation

Defining the business functions for choosing the right database

The usage of different types of data requires the identification of the objectives for storing data entries. Customer information may comprise of customer transactions, registration information, telephone queries, cookie information, and information of every contact with a customer at different touch-points (Buttle, 2013). The primary function is about knowing the customer in a better way. Therefore, businesses will use data about a customer‘s past purchases, demographics, psychographics, mode of communication, and other useful information.

If the business wants to approach its customer on the basis of behavioural traits and purchase traits, businesses will use a person’s social factors, income, employment, gender, and others as customer data. Again, if the business targets customers on the basis of a wide range of product list, the businesses will use lifestyle based data (Kroenke and Auer, 2013). Similarly, using transaction history and shopping habits businesses will target customers on the basis of an impulse purchase.

However, it is imperative for businesses to choose the database on the basis of the CRM. A strategic CRM based business will use different data from that used by a business adopting analytical CRM. Strategic CRM needs data about markets, market offerings, customers, channels, competitors, performance and potential to be able to identify which customers to target for acquisition and retention (Coronel and Morris, 2016). Similarly, collaborative CRM implementations generally use the operational and analytical data as described below, so that partners in distribution channels can align their efforts to serve end-customers. On the other hand, analytical CRM uses customer-related data to support the marketing, sales and service decisions that aim to enhance the value created. Therefore, customer database formation remains dependent on business objectives.

Type of information required to meet business objectives

The database helps to store all sorts of information about the customer; individual-related information, company-related information, and marketing based information (Buttle, 2013). For instance, a business aimed towards intense marketing via digital campaign will gather information about:

  • open and click-through rates,
  • click-to-open rates (CTOR) from previous e-campaigns,
  • broken down by target market,
  • offer and execution.

Again, businesses look to make strategic CRM decisions find information on:

  • Market segmentation.
  • Products bought.
  • Other forms of purchase (purchase from a competitor or retail purchase).
  • Customer’s requirements.
  • Expectations and preferences.
  • Services opted.
  • Channels were chosen for purchase.
  • Communication methods by the customer.

The need for identifying information lies with storing personal information in a database so that businesses can easily look up client records and identify customers. Using Contact Information, businesses need to get in touch with clients for all sorts of reasons, from giving consulting advice to making sales calls (Coronel and Morris, 2016). Similarly, businesses need the information to remember past interactions with customers to tailor future experiences to meet the customer’s needs. For this, businesses need customer history such as order history, customer service complains, customer feedback and stated customer preferences. Therefore, business objectives play a crucial part in customer database formation.

Internal sources of data gathering for customer database

Information sources of customers are mainly internal and external (Buttle, 2013). Internal sources mainly comprise of data from the existing customer database. Internal data is usually retrieved from inside the company to make decisions for successful operations. This information is important to determine whether the strategies of the business are successful. The four different areas a business can gather internal data from;

  • Sales
  • Finance
  • Marketing
  • Human resources

Marketing helps gather internal data on;

  • market size,
  • market segmentation,
  • customer profiles,
  • customer acquisition channels,
  • marketing campaign records,
  • product registrations and requests for product information.

On the other hand, sales help gather data on;

  • customer purchase history,
  • frequency and monetary value,
  • important buying criteria,
  • purchase via discounts and payment period,
  • responses to proposals,
  • competitor products and pricing and,
  • customer requirements and preferences (Kroenke and Auer, 2013).

Finance helps gather internal data on credit ratings, accounts receivable and payment histories. Lastly, service-based data comprises of service histories, service requirements, customer satisfaction levels, customer complaints, enquiries, and loyalty programmes.

External sources of customer data collection

On the other hand, external data has three groups;

  • Compiled list data
  • Census data
  • Modeled data

External data is collected from the market that includes customers and competitors. Businesses collect external data from surveys, and customer feedback. The main function of external data is to help understand the business’ customer base and the competitive landscape of the market (Kroenke and Auer, 2013). They also form the compiled list data. Census data are obtained from government census records to understand the market and segment the potential customers. The census data also helps in pricing the products per capita income, average household size, and financial records. Lastly, modelled data are generated by third parties from data that they assemble from a variety of sources (Buttle, 2013).

For instance, an automobile dealership before setting up in a particular location collects data on occupational status, car ownership and media consumption with the help of third parties.

However, both internal and external data sources are primarily divided as primary and secondary sources (Buttle, 2013).

Hierarchy of primary and secondary sources of customer databse formation
Hierarchy of primary and secondary sources of customer databse formation

Database management platforms

Every business needs an efficient way of both storing and employing the data. Therefore, businesses mainly use different types of data management platforms (Coronel and Morris, 2016).  They help in housing customer data, sales data and campaign data. Data management platforms help marketers and advertisers build customer segments and their performance based on demographic data, past browsing behaviour, location, device and more.

For instance, Salesforce data management platform, help businesses looking to capture, unify and process data to reach out to multiple customers at a time (Salesforce, 2019).

These types of data management platforms use artificial intelligence and machine learning to provide businesses with complete customer data profiles, multi-channel engagement and help engage with existing customers as well as find new customers. Other types include Oracle data management platform, Adobe Audience Manager and Nielsen data management platform.

However, these data management platforms are also commonly known as Relational database management system (RDBMS) (Buttle, 2013). They allow businesses to create, update and administer a relational database. Relational databases store data in two-dimensional tables comprising of rows and columns that provide a unique form of identification for each record. These types of databases help businesses gather and store both internal and external databases.

Final steps include populating and maintaining customer database formation

These final two steps are congruent to each other. Using a tool helps capture data and is used for processing. Populating the data means, businesses will use the collected data to replicate and collect larger data for understanding its market (Buttle, 2013). Populating helps the businesses to validate the primary data collected using the modes of sourcing, verification, validation, de-duplication, and merging two or more databases. Once the database is populated, businesses need to maintain the data by constantly following the methods of populating the data from different sources (Buttle, 2013). This means businesses need to keep the database updated at all times. Other methods include auditing, regular de-duplication of databases, and source data from both primary and secondary sources.

Customer database formation is not the end process

The customer database formation, however, ends here, but the process of maintenance and further development never ends. Customer database comprises of different functions that allow the continued flow of data collection and taking action on the data. This comprises of data mining, data warehousing, data integration and managing the desirable data attributes. The customer database formation is a mere updating process for better customer management and sales and marketing drive and meets the business objectives. However, these processes are possible only by using the mentioned different data management procedures using integration and collection procedures.


  • Buttle, F. 2013. Customer Relationship Management Concepts and Technologies. 2nd edn. Burlington: Elsevier. doi: 10.1017/CBO9781107415324.004.
  • Coronel, C. and Morris, S., 2016. Database systems: design, implementation, & management. Cengage Learning.
  • Kroenke, D.M. and Auer, D.J., 2013. Database processing(Vol. 6). Prentice Hall.
  • Salesforce. 2019. Salesforce DMP and Data Studio documentation. Retrieved from on 28-04-2019.

I am currently working as a Research Associate. My work is centered on Macroeconomics with modern econometric approach. Broadly, the methodological research focuses on Panel data and Times series data analysis for causal inference and prediction. I also served as a reviewer to Journals of Taylor & Francis Group, Emerald, Sage.