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How to Safeguard Trust in the Digital Age? Data analytics, AI, and other tools of the digital age can help marketers improve CX, but they also may raise concerns about data usage and privacy. Brands have an opportunity to build trust by showing they use these technologies wisely—and always in service to the customer.

Advanced technologies that gather and apply customer data can be significant differentiators for a brand—and their use is becoming ubiquitous. Social media sites grant consumers “free” usage of services and, in return, give advertisers access to users’ personal information and activity. Grocery stores hand out customer discount cards to capture and leverage purchase history, often in partnership with outside vendors. Online retailers use and sometimes sell data to build predictive recommendation engines for shoppers. In an era of connected technology and big data, brands have powerful tools to personalize marketing and improve the customer experience (CX). Yet with great power comes great responsibility. As customer champions, marketers are expected to be trusted stewards of consumers’ information, accessing, using, and storing their data in a manner that maintains—and builds—trust with the customer. Similarly, marketers have a responsibility to use advanced technologies such as AI in a way that supports the mission of the organization and enhances its relationship with consumers. Indeed, fostering trust is a top marketing trend for this year, according to Deloitte’s 2020 Global Marketing Trends. Big Data Needs Big Trust As marketers collect more customer data and leverage that data through increasingly sophisticated AI technology, they face complex choices about how to handle the information they gather. Deloitte’s 2019 Global Consumer Pulsing Survey in the United States, United Kingdom, China, and Brazil shows that consumers have a strong aversion to companies profiting from thesale of their data—even as many are unaware of how pervasive the practice is. Among 4,000 global consumers surveyed, 53% said they would never use a company’s products if the company were selling consumer data for profit, while 40% believed none of an organization’s profits should be derived from selling data. However, 27% of respondents acknowledged that they never consider how a company uses their data while they are making purchase decisions. (Conversely, 19% always consider company data usage.)Transparency about how an organization uses customer data can benefit brands and consumers alike. In one study, 86% of surveyed customers indicate they would be more likely to trust companies with their information if they knew how it provided a better CX. The takeaway for marketers: It’s increasingly important to get messaging right when communicating how data and AI strategies provide a fair exchange of value for customers. Align Data Policies to Purpose Organizations looking to foster trust with consumers can begin by ensuring that data capture and usage align with the core company mission— and by extension, support the brand’s relationship with the customer. As more data protection and privacy regulations emerge—such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act in the United States—brands have an opportunity to build public trust by proactively reviewing their own consumer data processes. Data policies become even more complicated when brands acquire data from second- and thirdparty vendors. In these cases, it can be difficult to fully understand how data was acquired and what customers know about how that data would be used. Nonetheless, it’s important for brands to consider what consumers would expect from those possessing their data and ensure data is managed in a way that is congruent with the company’s purpose.Build Toward Trusted AI With data as the foundation, brands are leveraging AI to identify and segment audiences, optimize performance, and create a better CX. Yet using AI for these purposes raises two concerns. One is that human bias can lead to flawed methodology assumptions, biased data for training models, or incorrect interpretations of outputs, which, in turn, can result in discriminatory advertising. Another concern relates to transparency. Because AI uses complex modeling that progressively improves without human intervention, it can be difficult to sufficiently explain the methodology when seeking consumer consent. Regulations like the GDPR incorporate clauses about the use of AI, such as the need to explain to consumers the logic behind automated decision-making. In addition, organizations might consider the following measures to maintain consumer trust regarding AI practices: Partner in developing an AI strategy. As AI trust issues escalate, new institutions are forming to help businesses with best practices. For instance, the Algorithmic Justice League partnered with the Center on Privacy and Technology to launch the Safe Face Pledge. Organizations can use this platform to publicly commit to not abusing facial analysis technology. Further, the league offers to assess code to minimize the opportunity for bias and provides instruction on inclusive algorithmic design. Design for relevance, not personalization. Sometimes, special incentives that are meant to be personal can feel invasive. Take, for example, the use of algorithms that infer a woman is pregnant based on web search or purchase history, then serve targeted ad offers. Instead, organizations can pivot algorithms to provide relevant recommendations based on circumstance, not personal history—say, by offering an umbrella on a rainy day rather than an umbrella after someone buys a raincoat. Explain how consumers benefit. Companies can demonstrate how AI leads to better innovation and, therefore, better experiences for the customer. For instance, Amazon gleans insights from its purchase data to better build its supplier network to match consumer demand. Notably, this data is used at an aggregate rather than an individual level. ***** As the use of data analytics and AI continues to expand, so does their potential impact on brand trust. Companies are likely to feel increasing pressure to show they are good stewards of customer data. In this light, they could do well to build a high level of trust with stakeholders by proactively and transparently demonstrating good behavior. —by Jeff Simpson, principal, Deloitte Consulting LLP

Answer 1

Data analytics and other tools can improve the customer experience but there are concerns regarding the protection of th data. Brands can build trust by using these technologies wisely.As great powers comes with great responsibility marketers are expected to use and store their data in a way that builds trust as in this era of connected technology abrands have powerful tools to personalize marketing and improve the customer experience .At the same time marketers have responsibility to use AI in a way that enhances customers relations with them.Deloitte’s 2019 Global Consumer Pulsing Survey in the United States, United Kingdom, China, and Brazil shows that consumers have a strong aversion to companies profiting from thesale of their data—even as many are unaware of how pervasive the practice is. Transparency in the use of data benefits brand and consumers as study shows that, 86% of surveyed customers are. more likely to trust companies with their information if they knew how it provided a better CX.

The takeaway for marketers: As more data protection and privacy regulations emerge—like General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act in the United States—brands have an opportunity to build public trust by proactively reviewing their own consumer data processes.Using AI brands are able to, optimize performance, and create a better CX. Yet using AI raises two concerns. One is that human bias can lead to flawed methodology assumptions,etc thatcan result in discriminatory advertising. Another concern relates to transparency as AI uses complex modeling.Organizations might consider the following measures to maintain consumer trust regarding AI practices: Partner in developing an AI strategy.Organizations can use this platform to publicly commit to not abusing facial analysis technology. Further, the league offers to assess code to minimize the opportunity for bias and provides instruction on inclusive algorithmic design. Design for relevance, not personalization, explain how consumers benefit by demonstrating how AI leads to better innovation and better experiences for the customer.This data is used at an aggregate rather than an individual level. ***** As the use of data analytics and AI continues to expand, with trust Companies are likely to feel increasing pressure to show they are good stewards of customer data. And thus, they could do well to build a high level of trust with stakeholders.

My thoughts are similar to article as it says great power comes with creat responsibility and it is the responsibility of the organization to use data analytics and advanced technologies like AI to protect and safeguard the data to improve customer experience and trust. As there are several data security concerns where organization are selling customers data and earning profits and customers are not aware of it. Improving customers experience leads improve trust , greater collaboration and building of trust with stakeholders.