Importance of customer database in the current generation CRM strategies

A very important component of customer relationship management (CRM) is the customer database (Tsou, & Huang, 2018). To begin with, businesses that focus on customer database management are more interested in the adoption of the operational type of CRM. Although, both analytical and strategic type of CRM use customer database, the application differs in both. Businesses adopting strategic CRM use customer data to identify which customers to target. Whereas, businesses adopting analytical CRM use customer data to search for new patterns (Buttle, 2013).

Furthermore, businesses adopting collaborative CRM use customer data to apply in building the relationship amongst company, partner and customer value. However, it is claimed that the customer database is the ‘foundation for the execution of CRM’.

Apart from CRM, the customer database is also used in;

  • sales
  • marketing
  • service
  • logistics
  • operations
  • customer management
  • and accounts.

Types of customer database

The types of customer databases revolve around three main aspects historic sales, current opportunities, and future opportunities (Elmasri and Navathe, 2017). A customer database may comprise of a huge amount of data, however, the variables get classified on the basis of the CRM function. For promotional relations, the CRM uses customer portfolio information for name, address, contact details, contact preferences, age, marital status, birth-date, and anniversary. This use of customer data helps in building relationships with the customers.

For instance, OYO Rooms, a hospitality company in India sends emails and app notifications about discounts to clients on the occasion of birthdays and anniversaries.

On the other hand, for promoting products or introducing new products to existing customers, product managers use customer data on product preference, price band and product categories explored (Elmasri and Navathe, 2017).

For instance, any cosmetics company producing an alternative new product will need the information of customer’s product preference and product categories to make their existing customers aware of the new product.

Managers can make use of customer data such as transaction history, product categories, and source of purchase for making sales strategies.

Customer database forming the customer portfolio

However, many organizations, mainly the multi-nationals and the conglomerates categorize their customer database on the basis of;

  • descriptive
  • behavioural
  • interaction,
  • and attitudinal (Khan, et. al., 2012).

Descriptive customer database comprises of data like gender, age, geography and income. On the other hand, behavioural data comprises of purchase-related information such as products in a wish list, product search and surfing behavior (Mickelsson, 2013). Businesses mainly e-retailers, also keep a tab on their customer portfolio of different types of devices used to either surf products or purchase. Furthermore, behavioural data helps businesses to understand the purchase behaviour of the customers and predicts what they tend to purchase in future.

Customer database forming the customer portfolio
Customer database forming the customer portfolio

Interaction type of customer database comprises of clicks, navigation paths and browsing activities. Businesses collecting data on attitudes of customers comprise of preference data, customer opinions, desirability, branding and brand sentiments. Subsequently, these four types of the database forms the customer portfolio, that helps the businesses in identifying their CRM strategies to build strong relations (Elmasri and Navathe, 2017). In addition, these four categories of customer data remain broadly divided into operational and analytical. Descriptive and attitudinal data acts as operational, whereas, behavioural and interaction type of customer data acts as analytical.

Modes of the collection of customer data

The customer data collection is usually either online or offline (Elmasri and Navathe, 2017). To begin with, instances of offline methods include filling out survey forms in retail stores, whereas online methods include creating a profile before purchasing the product. However, offline methods of customer data collection are one of the oldest and a more conventional method. This method is more prevalent in B2B types of businesses.

For instance, the production unit of Coca Cola collects data from its vendors with respect to stocks of soft drinks and keeps a track of the number of sales. Similarly, when customers fill out the survey data in retail stores, it subsequently helps to predict the type of products the customers are interested in.

On the other hand, online data collection starts from providing telephone numbers and customer name while billing to create a user profile on e-retail platforms (Mickelsson, 2013). Sharing of a number and name allows retail stores to record the purchase amount and the types of products purchased forming a customer profile. Similarly, while purchasing online customers need to create a user profile or link their social media profile to make a purchase. In this regard, the e-commerce platforms keep the record of the mode of purchase, type of purchase, navigation paths, and frequency of visits.

New age data collection methods have now come up for big data mining and big data analytics (Brito, et. al., 2015). Different tools are used by businesses dealing with millions of consumers per day. These tools, subsequently, help in the development of both customer acquisition as well as customer retention strategies.

Applications of the customer database

The most important application of the customer database is to help the businesses in clustering or creating segments of customer data on the basis of their activity patterns (Mickelsson, 2013). Subsequently, the activity patterns include the types of products viewed and the type of sources used to view the product. Segmenting the customers on the basis of the customer comprises of aspects such as;

  • similar and different types of products purchased,
  • region,
  • purchase capacity of the customer,
  • and frequency of similar or different products purchased.

This segmentation helps the businesses in strategizing customized offers for customers of one segment, whereas, the offer varies in different segments. Another application of the customer database is that businesses are able to make personalized recommendations (Talón-Ballestero, et. al., 2018).

For instance, while purchasing online one may view recommended options or products.

Segmentation of the customers on the basis of regions also allow the businesses to display or recommend the right product of choice for that particular region. Customer data is also used to customize promotions and offers to provide long-term customer loyalty and satisfaction to profitable customers. Furthermore, customer data in the form of feedback from customers helps to improve products and services. New age applications of customer data include conversion of customer data to infographics and presentation in business reports (Talón-Ballestero, et. al., 2018). Nowadays, IT and finance based companies also use their customer data to provide organizational effectiveness and reduce risk and fraud.

References

  • Brito, P.Q., Soares, C., Almeida, S., Monte, A. and Byvoet, M., 2015. Customer segmentation in a large database of an online customized fashion business. Robotics and Computer-Integrated Manufacturing36, pp.93-100.
  • Buttle, F. (2013) Customer Relationship Management Concepts and Technologies. 2nd edn. Burlington: Elsevier. doi: 10.1017/CBO9781107415324.004.
  • Elmasri, R. and Navathe, S., 2017. Fundamentals of database systems. Pearson.
  • Khan, A., Ehsan, N., Mirza, E. and Sarwar, S.Z., 2012. Integration between customer relationship management (CRM) and data warehousing. Procedia Technology1, pp.239-249.
  • Mickelsson, K.J., 2013. Customer activity in service. Journal of Service Management24(5), pp.534-552.
  • Talón-Ballestero, P., González-Serrano, L., Soguero-Ruiz, C., Muñoz-Romero, S. and Rojo-Álvarez, J.L., 2018. Using big data from customer relationship management information systems to determine the client profile in the hotel sector. Tourism Management68, pp.187-197.
  • Tsou, H.T. and Huang, Y.W., 2018. Empirical Study of the Affecting Statistical Education on Customer Relationship Management and Customer Value in Hi-tech Industry. Eurasia Journal of Mathematics, Science and Technology Education14(4), pp.1287-1294.

Avishek Majumder

Research Analyst at Project Guru
Avishek is a Master in Biotechnology and has previously worked with Lifecell International Private Limited. Apart from data analysis and biological research, he loves photography and reading. He loves to play football and basketball in his spare time with an avid interest in adventure and nature. He was also a member of the Scouts in his school and has attended Military training.
Avishek Majumder

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1 Comments.

  1. Nicely described. Thank you so much for sharing an informative article.

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