The Gist
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Data-driven personalization. Hyper-personalization thrives on analyzing customer data and enables tailored experiences that increase engagement and loyalty.
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Ethical marketing practices. Brands must balance hyper-personalization with transparency and guarantee data security to build trust and customer loyalty.
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AI-enhanced campaigns. Generative AI streamlines hyper-personalization by automating tasks and analyzing customer preferences.
When interacting with a company or brand, today’s consumers increasingly expect communication, offers and support that are personalized, consistent and timely. According to a recent survey by McKinsey & Company, 71% of consumers expect organizations to address them in a personalized way. Similarly, a study by the CMO Council found that companies are steadily more focused on marketing personalization in order to stand out from competitors.
New technologies such as artificial intelligence (AI) and generative AI are enhancing the ability for organizations to develop and deliver highly personalized 1:1 communication with their customers. Yet hyper-personalization requires these customers to trust the brands and companies who handle their personal data. Customers demand that companies and brands be transparent and responsible in how they handle and use personal data. A “black box” approach that lacks transparency is no longer sufficient.
Of course, consumers must also see tangible benefits from their interactions with brands, including timely and relevant offers and communication as well as responsive support.
Table of Contents
Responsible Marketing for Brands in the Digital Age
Today’s companies are inundated with data on customers, operations, finances and more. Building strong and lasting digital relationships with customers requires technologies that can help teams quickly access and analyze the right data, suggest the right audiences and assist in crafting compelling and relevant messages.
All this must be done in compliance with existing data regulations as well as evolving new rules for AI and generative AI. Also, the application of these powerful technologies must be done ethically and responsibly to reduce any unintended bias, protect and secure personal data and promote corporate responsibility.
Related Article: 5 Customer Data Protection Tips to Strengthen Cybersecurity
Navigating Data Governance and Privacy Challenges
Increasingly, zero- and first-party data require companies and brands to focus on data governance, data security and data privacy.
Customers intentionally and proactively share zero-party data with a company or brand (i.e., responding to polls and surveys, subscribing to newsletters and emails and indicating comms and product preferences). First-party data is collected by a brand during customers’ normal interactions with that brand (i.e., browsing a website and making purchases).
Strong, flexible customer data management delivers a competitive advantage to brands that embrace it. Given the growing complexity of legal and regulatory requirements around the use of data and AI, comprehensive data management is increasingly a necessity.
Bad data can also have highly negative consequences. Models trained with distorted or unbalanced data can lead to biased results and biased decisions. Entire groups of people may be disadvantaged, based on gender, origin social status or other factors. This can seriously harm brand reputation and lead to regulatory actions and fines.
Generative AI’s Role in Responsible Marketing Practices
To achieve responsible marketing, all marketers, regardless of their technical know-how, should be able to assess models for fairness and effectiveness and adapt them as needed. Some of the new generative AI tools available to marketers help them make and receive plain-language queries and get responses. No coding or technical expertise is needed for marketers to evaluate the effectiveness of a model and the results of a campaign.
For a long time, many organizations thought of advanced analytics like AI as the realm of quantitative experts like statisticians and data scientists. This is no longer the case, as tools like ChatGPT from OpenAI make AI accessible to a wider group of people, with varying job roles and experience. By engaging with data and AI at a conversational level and in real time, marketers can act quickly to improve campaigns, select audiences and respond to customers and their changing needs.
Marketers are already starting to benefit from generative AI augmenting their productivity and creativity. AI-based assistants can automatically take over repetitive marketing tasks, thus simplifying data analysis while enhancing campaign and customer journey management.
Meanwhile, AI-powered analysis can precisely evaluate customer interactions and preferences to create precise, hyper-personalized profiles. This allows for mailings tailored to each customer with individual subject line, text and imagery.
Related Article: Generative AI in Marketing: The Good, the Bad, the Unavoidable
The Importance of an Integrated Control Center for Data
A control center such as an integrated customer data platform (CDP) is essential for hyper-personalization and cross-channel next-best-offer in real time. A CDP that works with AI and analytics helps organizations track the movement of personally identifiable information and improve privacy and compliance.
A CDP can be assessed across three dimensions.
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Data: The CDP should adhere to all industry-specific and national/regional regulations for data protection and data privacy. Variables such as the degree of personalization or loyalty scores are relevant. And it is important to clarify the origin, quality and purpose of the data.
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Technology: It matters how exactly the technology is used. It’s also important to measure issues such as bias and sustainability. Technology audits provide the necessary transparency to understand which models are used to interpret data and whether this is done in compliance with the GDPR.
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Resource: The focus here is on the efficient use of resources like technologies and employees. This can be measured through metrics such as return on marketing investment (ROMI), P&L (profit & loss) attribution accuracy and CSR scores.
Managing Data Effectively: From CRUD to Data Flood
Before companies invest in developing AI models, they should carefully consider how they will manage the data at the heart of these models.
For data management in marketing, it’s helpful to follow the oddly named but often used (in software development) CRUD principles:
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Create: First make sure that the selected data is consistent with the purpose and relevance of the model. If data sets are selected prematurely, there is a risk that an organization will make decisions based solely on the availability of data (i.e., availability bias), rather than on its relevance.
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Read: It’s important to determine who has read-access to the data and who should be involved in the evaluation of analytical results. A “read” base is a good starting point for privacy and security. In addition, clear guidelines on how and when to engage employees help prevent bias caused by automation.
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Update: This principle is primarily aimed at the continuous adaptation of AI models. This is because, like all systems, these tend to develop deviations over time. They require a consistent update to avoid biased or outdated analysis results. Companies must therefore establish best practices for constant monitoring and improvement of AI models.
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Delete: As soon as a reference is no longer relevant, the associated data must be deleted. This includes regularly assessing stored data and removing redundant information, which will result in a leaner, more secure data ecosystem.
The Business Benefits of Ethical and Responsible Marketing
Responsible marketing driven by the careful application of technologies like CDPs, AI and generative AI brings economic benefits to brands. With a targeted, hyper-personalized approach, campaigns are more easily managed and are more effective. The automation of simple, repetitive tasks through generative AI frees up marketers to focus on more complex tasks where they can apply their creativity, experience and skill.
Customers appreciate companies acting responsibly with their data, while also providing relevant information, timely offers and agile support. When customers trust a brand and see value in their interactions with it, they will share personal customer data that powers a hyper-personalized approach.
Core Questions Around Responsible Marketing and Hyper-Personalization
Editor’s note: Here are two important questions to ask about hyper-personalized customer experiences.
How does hyper-personalization impact marketing strategies?
Hyper-personalization delivers highly targeted, relevant experiences tailored to individual customers. By using data such as browsing behavior, purchase history and preferences, brands can create personalized offers, communications and support that resonate with each consumer. This results in higher engagement, increased customer loyalty and improved conversion rates. Marketers can use tools like AI and generative AI to streamline personalization at scale while respecting customer privacy.
What are the benefits of responsible marketing using AI and data management?
Responsible marketing that integrates AI and strong data management helps brands build trust with customers. By focusing on transparency and ethical data use, companies can deliver hyper-personalized experiences while avoiding bias or misuse of customer information. This approach not only meets legal requirements but also enhances brand reputation and customer loyalty. Brands that adopt responsible marketing practices see improved customer satisfaction and more effective campaigns.
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