Blurring the lines of privacy in a digital world of AI

In the current iterations of AI adoption, there is still no full control over what AI tools collect and generate. This can be challenging, but there are steps we can take to mitigate risks and ensure compliance with data protection regulations.

    1. Due diligence

      One critical aspect of marketing campaign decision-making is vendor due diligence. If you’re using third-party AI tools, review their data collection, processing, and privacy practices. Choose tools that align with your data privacy requirements and provide transparency regarding the data they collect and how it’s used.

      The biggest technology companies worldwide are striving to be a champion in AI adoption. Companies such as Apple, Amazon, Microsoft, Tencent, and Alibaba are leading the way in AI development. They’re using AI to create even more advanced products and services based on AI’s understanding of their massive historical data points.

      When conducting vendor due diligence, this trend of major technology companies investing heavily in AI development can offer valuable insights and implications. Understanding what they focus on in AI can help assess the vendor’s positioning within the market.

      Looking through the lens of vendor due diligence, it’s evident that the earlier example of McDonald’s making calculated decisions based on their assessment of Dynamic Yield’s performance and potential aligns with the evolving landscape of personalized digital experiences and their determination to make the most of their investments.

    2. Customer value alignment

      I know it’s easier said than done. Here’s when customer value alignment comes into play.

      Eliano Marques, the Executive Vice President of Data and AI at Protegrity, a data security firm, explains that AI plays a pivotal role in expediting the data identification process to enhance customer data privacy. In scenarios where companies manage numerous databases across both cloud and on-premises environments, pinpointing the data requiring protection is far from straightforward. The intricate nature of data ecosystems makes it nearly inconceivable to envision the process of data identification without harnessing the power of automation and AI.

      Balancing customer benefits and business goals when implementing responsible AI practices requires careful consideration and a strategic approach. So, I highly recommend tailoring AI-driven experiences to enhance customer value and satisfaction. When customers see the relevance and utility of your AI solutions, they’re more likely to embrace them.

    3. Seek insights from anonymized data

      You can use AI to glean insights from aggregated and anonymized data. This way, you can benefit from AI-driven analytics without compromising individual privacy.

      You gather data from various sources, such as purchase history, website interactions, and demographic information. However, you don’t focus on individual data points. Instead, you group the data into categories like product categories, age groups, geographic regions, and purchase frequency.

      Before using the data, remove any personally identifiable information (PII) that could link the data to individual customers. Now, you employ AI algorithms to analyze the data. These algorithms can identify trends, correlations, and patterns within the data. For instance, you might discover that a specific age group tends to purchase certain products during specific seasons.

      With the insights gained from AI analysis, you can develop targeted marketing campaigns. For example, you might create seasonal promotions that align with the buying patterns of certain age groups without revealing any individual customer information.

      TIFIN, an AI platform catering to wealth management, has unveiled a strategic partnership with Morningstar, a financial services firm, aimed at boosting its AI-driven distribution system. Through this collaboration, TIFIN AMP clients will access valuable insights extracted from Morningstar’s aggregated and anonymized advisor recommendation trends. These insights can be customized using key demographics such as region, firm size, and firm type.

      The TIFIN AMP platform offers flexibility, functioning as an independent system or seamlessly integrating into a company’s existing CRM and marketing automation capabilities. This adaptable approach ensures that firms, regardless of their size, can harness and apply these wealth management-focused insights to enhance their operations.

      Finding harmony between AI advancements and personal privacy in the marketing world is a challenge we must navigate. Share your thoughts by adding a comment below!