Customer data: Difference between revisions
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In the internet age, a prominent method for collecting customer data is through explicit [[online survey]]s,<ref>{{Cite web |last=Dean |first=Kevin |date=2022-09-28 |title=An Open Letter to Marketers and Data Scientists |url=https://analytics-iq.com/an-open-letter-to-marketers-and-data-scientists-struggling-to-keep-up-with-customer-behaviors-from-chief-strategy-officer-kevin-dean/ |access-date=2023-10-02 |website=AnalyticsIQ |language=en-US}}</ref> but also through concealed methods like measurement of [[click-through rate|click-through]] and [[abandonment rate]]s.{{Citation needed|date=November 2023}} |
In the internet age, a prominent method for collecting customer data is through explicit [[online survey]]s,<ref>{{Cite web |last=Dean |first=Kevin |date=2022-09-28 |title=An Open Letter to Marketers and Data Scientists |url=https://analytics-iq.com/an-open-letter-to-marketers-and-data-scientists-struggling-to-keep-up-with-customer-behaviors-from-chief-strategy-officer-kevin-dean/ |access-date=2023-10-02 |website=AnalyticsIQ |language=en-US}}</ref> but also through concealed methods like measurement of [[click-through rate|click-through]] and [[abandonment rate]]s.{{Citation needed|date=November 2023}} |
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[[Online surveys]] are a direct approach, allowing companies to gather detailed customer insights by asking specific questions. This method provides qualitative data, which can be analyzed to understand customer preferences, opinions, and behaviors. |
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Measurement of [https://support.google.com/google-ads/answer/2615875?hl=en click-through rates] (CTR) is another vital method. CTR measures how often people who see an online ad or link end up clicking on it. This metric helps companies assess the effectiveness of their marketing campaigns and understand what attracts their audience's attention. |
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[https://www.geckoboard.com/best-practice/kpi-examples/call-abandonment-rate/ Abandonment rates] measure how often users leave a website or an online process before completing their intended action, such as filling out a form or making a purchase. High abandonment rates can indicate problems with website usability or the customer journey, providing valuable data for improving user experience and increasing conversions. |
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Together, these methods offer a comprehensive view of customer behavior and preferences, enabling businesses to make data-driven decisions and enhance their marketing strategies. |
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Customer data is gathered for [[customer research]], especially [[customer satisfaction research]] and purportedly serves to increase overall [[customer satisfaction]].<ref name="Shandrow 2015"/> |
Customer data is gathered for [[customer research]], especially [[customer satisfaction research]] and purportedly serves to increase overall [[customer satisfaction]].<ref name="Shandrow 2015"/> |
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Latest revision as of 12:53, 2 December 2024
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Customer data or consumer data refers to all personal, behavioural, and demographic data that is collected by marketing companies and departments from their customer base.[1] To some extent, data collection from customers intrudes into customer privacy, the exact limits to the type and amount of data collected need to be regulated.[2][3] The data collected is processed in customer analytics. The data collection is thus aimed at insights into customer behaviour (buying decisions, etc.) and, eventually, profit maximization by consolidation and expansion of the customer base.[4]
In the internet age, a prominent method for collecting customer data is through explicit online surveys,[5] but also through concealed methods like measurement of click-through and abandonment rates.[citation needed]
Customer data is gathered for customer research, especially customer satisfaction research and purportedly serves to increase overall customer satisfaction.[6]
Levels of information
[edit]A possible classification of business customer information was proposed by Minna J. Rollins, who distinguished the levels a) market b) organizational c) business unit, and d) individual.[7] For private consumers, different levels are a) personal identifying data b) psychographics data, c) transactional (buying) data, d) demographic, and e) financial data.[6] While the individual data level for business customers has some overlap with the data gathered from individual consumers, the other business-related levels roughly correspond to the demographic part of individual customers.[8]
See also
[edit]References
[edit]- ^ "Customer Information definition". Law Insider. Retrieved January 24, 2020.
- ^ Kroll, Lee; Feldman, H. Leigh; Schienberg, Alan (May 23, 2019). "It's time to embrace customer data privacy and security". IBM RegTech Innovations Blog. Archived from the original on Jul 8, 2019. Retrieved January 24, 2020.
- ^ Gupta, Sachin; Schneider, Matthew (June 1, 2018). "Protecting Customers' Privacy Requires More than Anonymizing Their Data". Harvard Business Review. Retrieved January 24, 2020.
- ^ Brown, Brad; Kanagasabai, Kumar; Pant, Prashant; Pinto, Gonçalo Serpa (2017-03-15). "Capturing value from your customer data". McKinsey & Company. Retrieved 2018-08-15.
In an increasingly customer-centric world, the ability to capture and use customer insights to shape products, solutions, and the buying experience as a whole is critically important. Research tells us that organizations that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.1 Customer data must be seen as strategic. ... Information on what customers purchase, how many times they contact customer service, and how long they linger on a given website can create an insightful narrative about buying habits and preferences.
- ^ Dean, Kevin (2022-09-28). "An Open Letter to Marketers and Data Scientists". AnalyticsIQ. Retrieved 2023-10-02.
- ^ a b Shandrow, Kim Lachance (February 8, 2015). "10 Questions to Ask When Collecting Customer Data". Entrepreneur. Retrieved January 24, 2020.
- ^ Rollins, Minna J. (Oct 2014). "Types of customer information collected about business customers". Customer information usage and its effect on seller company's customer performance in business-to-business markets – an empirical study (Report). Archived from the original on 12 Mar 2024 – via ResearchGate.
- ^ Chui, Michael; Hazan, Eric; Roberts, Roger; Singla, Alex; Smaje, Kate; Sukharevsky, Alex; Yee, Lareina; Zemmel, Rodney (June 14, 2023). "Economic potential of generative AI". McKinsey. Retrieved 2023-10-02.
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