Customer profile describe typical customer for particular products. It is used for R&D activities to develop products which are crafted for particular group of customers. Dome criteria for creating customer profile include: Socio-demographic characteristics:
- place of residence,
- frequency of purchase,
- intensity of product use,
- reasons for the purchase,
- customer reaction to marketing instruments,
- degree of loyalty to the brand,
Customer profile can be built by gathering data from one or many touch points. Since today customers are much more multi-channel than in the past, marketers are building 360-degree customers profiles by collecting data from all available touch points like websites, mobile channels, beacons, wi-fi networks, social media etc. It allows them to provide much more personalized customer’s experience than ever before .
Customer profile use cases
Direct marketing campaigns
One knowing his customer can easily provide more relevant direct campaigns like e-mail newsletters, SMS campaigns or mobile push notifications campaigns. In today’s world where customers are getting so many commercial messages, making communication more personal is crucial for any kind of businesses. Ability to speak in customer’s language can give marketers significantly higher ROI.
Estimating Customer Lifetime Value
Customer Lifetime Value (CLV) was defined in 1974 by Kotler as “the present value of the future profit stream expected over a given time horizon of transacting with the customer” . It's really important concept in marketing and the ability to predict Customer Lifetime Value gives marketers opportunity to make more profitable actions, plan marketing strategies and to define budgets better .
Customers behavior can be used in many different kinds of fraud detection systems. For example, banking institutions are using online user’s behavior to prevent unauthorized attempts to access customer’s account. They are assuming that any significant divergence of the standard user behavior can be signal of fraudulent activity. Another example of using customer profile in fraud detection is credit card fraud prevention. Credit card institutions are looking for any anomalies or deviations from typical customer spending behaviors. If customer never bought anything online, then first online transaction can be a signal that someone else is using his card credentials .
Customer segmentation based on behavioral or socio-demographic criteria are one the most important things to consider while planning your advertising campaigns. Creating ads which are personalized and relevant to specific customers’ segments allows to significantly lower ad cost and drive better results.
Customer profiling and segmentation are the basics of serving dynamic personalized content on websites. Companies can differentiate their products offering based on age, gender or customer’s purchase history. More relevant content help to remove friction in user experience, drive better conversions, lower bounce rate and make customers feel more important.
Utilizing customer profile, his or her needs or purchase history gives ability to recommend the most relevant products to a particular customer in a given context. Product recommendations can be made by analyzing user profile to assign him to specific customer segment where most relevant items can be recommended. If system knows that similar customers who bought product X also bought product Y, it can recommend product Y to customers who bought product X but didn’t bought Y yet. This technique is used by many e-commerce retailers and service providers like Amazon or Netflix.
Bank institutions are building customers’ profiles to determine if one can return the loan or not. Based on the income, education level, marital status, purchase behaviors and many other factors banks are trying to segment customers by their future reliability .
- Khajvanda M., Tarokhb M.J. (2010) , Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. "ScienceDirect".
- Kotler, P. (1974). Marketing during periods of shortage. "Journal of Marketing 38(3)".
- Kovach S., Ruggiero W.V. (2011) , Online Banking Fraud Detection Based on Local and Global Behavior. "ResearchGate".
- Park, S. H., Huh, S. Y., Oh, W., & Han, S. P. (2012) A social network-based inference model for validating customer profile data. MIS quarterly, 1217-1237.
- Peterson M., Gröne F., Kammer K., Kirscheneder J. (2010) , Multi-channel customer management: Delighting consumers, driving efficiency. "Springer".
- Vasilev J. (2014) , Creating a Customer Profile in a Credit Institution. "ResearchGate".
- M. Peterson, F. Gröne, K. Kammer, J. Kirscheneder (2010).
- Kotler, P. (1974), s.20-29.
- M. Khajvanda, M.J. Tarokhb (2010).
- S. Kovach, W.V. Ruggiero (2011).
- J. Vasilev (2014).
Author: Paulina Baczyńska