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10 marketing trends to watch now | TechTarget, 24 may 2026
Owning the Seams: Where CX Leadership Earns Its Seat | CMSWire, 23 may 2026
How accessible packaging is changing the retail experience | Retail Customer Experience, 22 may 2026
Marketing Media Money: Advertising Spend | CNBC Africa, 22 may 2026
EMPLOYER BRANDING HAS LOST ITS WAY – AI WILL MAKE THAT IMPOSSIBLE TO HIDE | Recruiter, 21 may 2026
Branding Touchpoints and Why They're Vital | American Coin-Op, 21 may 2026
UK researcher studies how sensory cues in retail influence consumer behavior | Lane Report, 20 may 2026
Generative AI in Marketing: Five Steps to Scale for Real ROI | Bain & Company, 19 may 2026
In the age of AI and data, market visits remain the real source of consumer insight | Exchange4Media, 18 may 2026
Redesigning Your Marketing Organizaition for the Agentic Age | Harvard Business Review, 08 may 2026
Marketing Anchors: The case for capability in an era of transformation | Ipsos, 19 march 2026
Trendspotting in 2026 - How brands can separate passing fads from valuable consumer insights | AdAge, 05 march 2026
January 2024
Mohammad Anas Wahaj | 11 jan 2024
According to the research 'Reidentification Risk in Panel Data: Protecting for k-Anonymity' (Authors: Sachin Gupta of Cornell University; Shaobo Li of University of Kansas; Matthew J. Schneider of Drexel University; Yan Yu of University of Cincinnati), published on 07 oct 2022 in Information Systems Research, nearly all market research panel participants are at risk of becoming de-anonymized. The commitment of a market research company towards privacy of panelists cannot be totally practiced as there are ways around it. Prof. Sachin Gupta says, 'When organizations release or share data, they are complying with privacy regulations, which means that they’re suppressing or anonymizing personally identifiable information. And they think that they have now protected the privacy of the individuals that they’re sharing the data about. But that, in fact, may not be true, because data can always be linked with other data.' Earlier research (2006) 'How To Break Anonymity of the Netflix Prize Dataset' (Authors: Arvind Narayanan of Princeton University; Vitaly Shmatikov of Cornell University) showcases the similar risk. Researchers developed a de-anonymization algorithm, Scoreboard-RH, that was able to identify up to 99% of Netflix subscribers by using anonymized information from a 2006 competition, aimed at improving its recommendation service, coupled with publicly available info on Internet Movie Database. Both of these researchs rely on 'quasi-identifiers' or QIDs, which are attributes that are common in both an anonymized dataset and a publicly available dataset, which can be used to link them. The conventional measure of disclosure risk, termed unicity, is the proportion of individuals with unique QIDs in a given dataset; k-anonymity is a popular data privacy model aimed to protect against disclosure risk by reducing the degree of uniqueness of QIDs. Prof. Gupta suggests that even though privacy laws are getting tougher but market researchers will continue to collect and store data, and the challenge of privacy remains. He says, 'The nature of the problem will probably reduce and change, but I don't think it's going away. Read on...
Cornell Chronicle:
Protecting identities of panelists in market research
Author:
Tom Fleischman
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