Brands that can find lawful ways to share data while respecting customer privacy rights will have an advantage over their competitors. Armed with superior analytics, these innovators will gain greater market share by using more personalized strategies that result in higher conversion rates.
According to a 2020 report from Gartner, modern privacy regulations will safeguard approximately 65% of the world’s population by 2023. Businesses that fail to comply with this new wave of privacy rules can get negatively impacted.
The new regulations are causing significant changes in how businesses handle customer data. Marketers are moving towards a privacy-first strategy, which is a huge shift given how important data has become in modern marketing. To optimize results, todays algorithm-driven marketing strategies rely on data. Demographic and behavioral data are important to the success of strategies like omnichannel advertising and people-based marketing.
Apart from the walled gardens, few brands gather enough first-party data to feed the algorithms used by their data science teams to segment customers, optimize promotions, and personalize content. Data science teams are constantly reviewing new data sources to feed the machine. Some marketers collaborate and share data with other brands that target the same demographics (second-parties). Others rely on third-party data brokers who specialize in data enrichment and enhancement.
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The Impact Data Privacy on Marketing
Concerns about privacy law violations are prompting an increasing number of companies to abstain from sharing data, particularly the personally identifiable information (PII) required to match customers across CRM systems. Businesses that wish to avoid compliance penalties and poor press are increasingly blocking marketing and data science businesses that rely on external data to increase new customer acquisition and drive sales.
The challenges may appear daunting at first. In some cases, data sharing has been completely abandoned in certain regions. Many third-party data brokers have already left Europe as a result of GDPR. Other, however, are rethinking their data operations and establishing processes that protect the privacy rights of the consumers while simultaneously allowing for targeted, personalized campaigns that marketers seek.
It’s past time for a new approach to data collaboration. The existing method for sharing data with second and third parties is both inefficient and difficult. A data sharing project might take anywhere from six weeks to six months to complete and involves a lot of technical and legal wrangling. It can take up to a year for a company in a regulated industry to on-board a new data partner before data can be analyzed. For decades, these inefficiencies have existed. Ironically, the new data privacy requirements may be exactly what the sector needs to reconsider how the process should operate.
Zero-Trust Technology
In recent years, a new wave of technology has arisen, that allows enterprises to radically reimagine how business partners share data. Developments in cryptographic technology have made it possible to run analytics on customer files without ever having to move the data. Customer records can be matched without revealing the customers’ identities or resolving to a third-party identification graph. Without sending personal data outside the firewall, insights and signals can be obtained. Businesses can entirely eliminate the need for trust in the data sharing process by not losing control of consumer identities.
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Marketers will be able to benefit from the strong insights that data collaboration provides while maintaining customer trust.
Instead of taking months, the entire process can be completed in a matter of days. Consider what would happen if data teams could go to an online marketplace of data providers and search through a list of available attributes to identify the top candidates. To understand the overlap in advance, a rapid and secure “pre-match” of the brand’s customer file could be performed. Brands would be able to effortlessly mix and match the attributes they want from different data sources to get the finest possible insights into their customers with so little friction.
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