Customer profile: Difference between revisions
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'''[[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: | '''[[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: | Socio-demographic characteristics: | ||
| Line 41: | Line 24: | ||
'''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 <ref>M. Peterson, F. Gröne, K. Kammer, J. Kirscheneder (2010).</ref>. | '''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 <ref>M. Peterson, F. Gröne, K. Kammer, J. Kirscheneder (2010).</ref>. | ||
== Customer profile use cases == | ==Customer profile use cases== | ||
===Direct marketing campaigns=== | |||
=== 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. | 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 === | ===Estimating Customer Lifetime Value=== | ||
Customer Lifetime Value (CLV) was defined in 1974 by Kotler as | 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" <ref>Kotler, P. (1974), p. 20-29.</ref>. 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 <ref>M. Khajvanda, M.J. Tarokhb (2010).</ref>. | ||
=== Fraud detection === | ===Fraud detection=== | ||
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 <ref>S. Kovach, W.V. Ruggiero (2011).</ref>. | 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 <ref>S. Kovach, W.V. Ruggiero (2011).</ref>. | ||
=== Advertising === | ===Advertising=== | ||
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 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. | ||
=== Content personalization === | ===Content personalization=== | ||
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. | 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. | ||
=== Product recommendations === | ===Product recommendations=== | ||
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. | 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. | ||
=== Credit scoring === | ===Credit scoring=== | ||
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]] <ref> | 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]] <ref> | ||
J. Vasilev (2014).</ref>. | J. Vasilev (2014).</ref>. | ||
{{infobox5|list1={{i5link|a=[[Consumer buying behaviour]]}} — {{i5link|a=[[Customer loyalty]]}} — {{i5link|a=[[Heavy users]]}} — {{i5link|a=[[Analysis of customer]]}} — {{i5link|a=[[Nicosia model]]}} — {{i5link|a=[[Target population]]}} — {{i5link|a=[[Brand equity measure]]}} — {{i5link|a=[[Relationship selling]]}} — {{i5link|a=[[Market reach]]}} }} | |||
==References== | ==References== | ||
* Khajvanda M., Tarokhb M.J. (2010) ''[https://fardapaper.ir/mohavaha/uploads/2017/12/Fardapaper-Estimating-customer-future.pdf , Estimating customer future value of different customer segments based on adapted RFM model in retail banking context]''. "ScienceDirect". | * Khajvanda M., Tarokhb M.J. (2010) ''[https://fardapaper.ir/mohavaha/uploads/2017/12/Fardapaper-Estimating-customer-future.pdf , 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)". | * Kotler, P. (1974). ''Marketing during periods of shortage.'' "Journal of Marketing 38(3)". | ||
* Kovach S., Ruggiero | * Kovach S., Ruggiero W.V. (2011) ''[https://www.researchgate.net/profile/WV_Ruggiero/publication/228616927_Online_Banking_Fraud_Detection_Based_on_Local_and_Global_Behavior/links/0f31753b47484639f5000000.pdf , Online Banking Fraud Detection Based on Local and Global Behavior]''. "ResearchGate". | ||
* Park, S. H., Huh, S. Y., Oh, W., & Han, S. P. (2012) [https://www.jstor.org/stable/pdf/41703505.pdf?casa_token=R6MGD_a7iXcAAAAA:j4_KtUdwa3MOeLPchG6TJCtvI2CfNV6k6s-fcor54jD38QNJA48u3RyuSENrQT2He8zpBJ-KI8Ty2Nzmpud2qqePMuvrs_BX_Vzfmr9KdvG7rdQVmRk3 A social network-based inference model for validating customer profile data]. MIS quarterly, 1217-1237. | * Park, S. H., Huh, S. Y., Oh, W., & Han, S. P. (2012) [https://www.jstor.org/stable/pdf/41703505.pdf?casa_token=R6MGD_a7iXcAAAAA:j4_KtUdwa3MOeLPchG6TJCtvI2CfNV6k6s-fcor54jD38QNJA48u3RyuSENrQT2He8zpBJ-KI8Ty2Nzmpud2qqePMuvrs_BX_Vzfmr9KdvG7rdQVmRk3 A social network-based inference model for validating customer profile data]. MIS quarterly, 1217-1237. | ||
* Peterson M., Gröne F., Kammer K., Kirscheneder J. (2010) ''[https://link.springer.com/article/10.1057/dddmp.2010.16 , Multi-channel customer management: Delighting consumers, driving efficiency]''. "Springer". | * Peterson M., Gröne F., Kammer K., Kirscheneder J. (2010) ''[https://link.springer.com/article/10.1057/dddmp.2010.16 , Multi-channel customer management: Delighting consumers, driving efficiency]''. "Springer". | ||
* Vasilev J. (2014) ''[https://www.researchgate.net/profile/Julian_Vasilev/publication/261273716_Creating_a_Customer_Profile_in_a_Credit_Institution/links/0c96053c7b06b2fbfa000000/Creating-a-Customer-Profile-in-a-Credit-Institution.pdf , Creating a Customer Profile in a Credit Institution]''. "ResearchGate". | * Vasilev J. (2014) ''[https://www.researchgate.net/profile/Julian_Vasilev/publication/261273716_Creating_a_Customer_Profile_in_a_Credit_Institution/links/0c96053c7b06b2fbfa000000/Creating-a-Customer-Profile-in-a-Credit-Institution.pdf , Creating a Customer Profile in a Credit Institution]''. "ResearchGate". | ||
== Footnotes == | ==Footnotes== | ||
<references /> | <references /> | ||
{{a| Paulina Baczyńska}} | {{a| Paulina Baczyńska}} | ||
[[Category:Marketing]] | [[Category:Marketing]] | ||
Latest revision as of 20:36, 17 November 2023
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:
- sex,
- age,
- education,
- income,
- place of residence,
Behavioral criteria:
- frequency of purchase,
- intensity of product use,
- reasons for the purchase,
- customer reaction to marketing instruments,
- degree of loyalty to the brand,
Psychographic criteria:
Consumer profiling is one of the most important market segmentation method used in marketing.
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 [1].
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" [2]. 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 [3].
Fraud detection
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 [4].
Advertising
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.
Content personalization
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.
Product recommendations
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.
Credit scoring
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 [5].
| Customer profile — recommended articles |
| Consumer buying behaviour — Customer loyalty — Heavy users — Analysis of customer — Nicosia model — Target population — Brand equity measure — Relationship selling — Market reach |
References
- 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".
Footnotes
Author: Paulina Baczyńska
