Using Customer Profiles to Enhance Service and Product Marketing

Customers are the lifeblood of any business. Understanding the unique and rich data that comes from their core customer base helps in creating services that truly meet the needs of those customers as well as marketing the most relevant programs to the most interested parties. It creates a higher level of sales and satisfaction spurred by the interconnectivity of customers and the organization. The customer’s needs are better fulfilled with the offering of products and services they are actually interested in. Precisely how this is done is a process that can be learned and adapted.

With the advancement of the Internet and e-commerce the use of social research to understand customer behavior becomes possible. With the increase in customer data it is possible to create greater data mining and clustering of customer profiles to understand buying patterns and behaviors (Prasad & Malik, 2011). It is through the development of higher levels of data analysis that services can become more effective and beneficial. 

Let us look at an example. Analysis of a large database finds that customers who bought airplane tickets also purchased beach related products. Yet what if these customers were also found to purchase more outdoor gear and spent a greater amount of money on outdoor activities? It would be possible to build a customer profile based upon their exploratory and thrill seeking behavior. 

In order to understand unique social purchasing behaviors requires the categorization and analysis of profile customers. It requires a method of making meaning out of the historical data (i.e. purchases over time) being presented. Qian et. al. (2006 suggests the following:

• 1.)    Standardize profiles
• 2.)    Screen out uninteresting profiles
• 3.)    Using basic functions to categorize profiles
• 4.)    Apply algorithms to the categorizations
• 5.)    Identify unique profiles for further analysis

Once the profiles are standardized it is possible to categorize their behavior into clusters. These clusters are used for additional analysis and the determining of patterned behavior. That patterned behavior indicates that there are latent psychological functioning occurring and it would be beneficial to use multiple analysis methods to better highlight their behavioral thought processes. 

This process is fairly accurate and can lead to better marketing techniques based upon profile attributes and responses to previous marketing (i.e. previous purchases).  One simply needs to draw connections between the different sets of data and tests that were conducted over time. A study by Leung (2009) found that out of 1,500 profiles analyzed that 91.73% of customer profiles were segmented correctly. 

High levels of accuracy and a process for separating and analyzing consumer behavior is a benefit that organizations should not ignore. The use of more pin pointed marketing techniques further encourages efficient use of company resources by ensuring that products are actually of interest to the customer. Social research techniques can help identifying latent psychological functions that further enhance organizational profits.

Leung, C. (2009). An inductive learning approach to market segmentation based upon customer profile attributes. Asian Journal of Marketing, 3 (3). 

Prasad, P. & Malik, L. (2011). Generating customer profiles for retail stores using clustering techniques. International Journal on Computer Science & Engineering, 3 (6). 

Qian, Z. et. al. (2006). Churn detection via customer profile modeling. International Journal of Production Research, 44 (14).