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How can you use AI to improve your marketing and analytics

Analytics Data privacy & security

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Published December 14, 2023 · Updated February 5, 2024

How can you use AI to improve your marketing and analytics

Since ChatGPT broke the internet in late 2022, artificial intelligence (AI) has been recognized as a transformative force capable of reshaping the world in much the way that the iPhone did. Generative AI models have captured the public imagination with their ability to mimic human conversation. However, they are just one of many tools we can use to gain a competitive advantage. 

The broader spectrum of AI technologies is driving a quiet revolution in the marketing sector. From predictive analytics to personalized content generation, artificial intelligence has become indispensable for marketers (and many others) seeking to navigate the complexities of modern consumer behavior.

While less sensational than their generative siblings, these technologies offer practical, accessible solutions that justify investment and integration into existing business processes. 

However, as tempting as it is to hand over the wheel to algorithms and reap the benefits from their work, their technological and legal limitations won’t allow it for quite some time.

The power of AI comes directly from the data that fuels it, which has become a concern. According to a 2020 survey by The European Consumer Organization, 45-60% of Europeans are afraid that AI could lead to increased abuse of personal data. This statistic underscores a critical challenge in the AI-driven marketing world: embracing the potential of AI while diligently safeguarding consumer privacy.

In this article, we delve into the multifaceted role of AI in marketing, exploring how it’s reshaping the industry, the benefits it brings, and the imperative of using it responsibly, particularly with regard to data handling and privacy concerns.

How is AI used in the digital world today?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. The term can also be applied to any machine that exhibits traits associated with a human mind, such as learning and problem-solving. AI systems are designed to handle tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

This definition of AI, provided by ChatGPT, while quite broad and general, accurately captures the essence of the concept. 

AI extends far beyond well-known applications like Siri or Alexa. AI-driven features include: 

  • Browser auto-suggestions.
  • Advanced focus mechanisms in contemporary cameras.
  • Spam filtering.
  • Email sorting.
  • Tools for detecting plagiarism.
  • Systems that assess sentiment.
  • Intelligent user interface, like Clippy, the Microsoft Office assistant.

Artificial intelligence is not a singular technology, but rather a collection of technologies designed to enable computers to process and interpret a variety of complex and sometimes abstract data simultaneously.

The use of AI in marketing

AI is all about data and, as data is “industry-agnostic,” it is widely used across a variety of sectors. Marketing, which embraced a data-driven approach a long time ago, has found itself at the forefront of these changes.

Moreover, there are many indications that this industry will be among the leaders in adapting to the use of predictive analytics and generative AI in compliance with current legal regulations. Why? In recent years, marketers have been inundated with data and forced to find effective means of analyzing it, owing to the huge demand for personalized customer experiences.

Two of the companies that found a way to do this – Facebook and Google – have created a duopoly in the digital advertising industry, employing AI-driven automation for years.

They both use a blend of audience segmentation with predictive analytics. Segmentation categorizes customers into groups based on gender, age, income, interests, and potentially countless other factors. Predictive analytics then determines which groups are most likely to be drawn to specific products or services.

Both companies have faced significant backlash and global debate over the misuse of personal data in their operations, highlighting the need for stricter regulation.

However, the potential of AI is not limited to the advertising sector. Here’s how the marketing industry is using AI-driven automation in its business models:

Personalized customer experiences

AI algorithms analyze customer data to create personalized marketing messages, product recommendations, and content. For example, such personalization exists in ecommerce, where AI suggests products based on browsing history and past purchases.

Predictive analytics

Marketers use AI to predict future customer behaviors based on historical data. This helps in anticipating market trends, customer needs, and potential areas for product development or marketing focus.

Social media insights and management

AI tools analyze social media trends and customer interactions to provide insights. They also automate social media post scheduling and track engagement metrics.

SEO and web optimization

AI tools help optimize website content to improve its search rankings. They can also test different website layouts and content placements to enhance user experience and engagement.

Voice search optimization

With the rise of voice-activated devices, AI is used to optimize content for voice search, adapting to how people verbally express their search queries.

Market research

AI-driven analytics tools provide deeper insights into market trends and consumer behavior, helping businesses make data-driven decisions.

Generative AI and marketing

Generative AI, a new and shiny subset of artificial intelligence, has brought even more tremendous potential for enhancing marketers’ capabilities, extending beyond just smart and real-time data analysis

The “creativity” of generative AI – its ability to create images, write texts, and compose music – has proven invaluable for marketers looking to automate and scale their content production. It also aids in creating personalized marketing messages and synthetic data, which can be safely used, for example, in campaign optimization.

However, the ChatGPT frenzy has highlighted the need for education related to copyrights among individual users. This is because they tend to trust generative AI a bit too much, forgetting that every piece of data input into the prompt is used to further model training.

The use of AI in analytics

With AI or generative AI taking all the glory, terms like machine learning seem to be losing prominence. This demonstrates how the “AI golden rush” is frequently detached from the business reality. That’s because machine learning has been here for years, identifying patterns and making predictions without explicit programming.

Machine learning has been the cornerstone of modern analytics. However, even before its advent, methods existed for handling data. Initially, there was rules-based automation (RBA), which refers to a system applying human-made rules to store, sort, and manipulate data. This was followed by its upgraded version, robotic process automation (RPA). In RPA, instead of a human crafting rules-based logic, software “bots” are capable of “observing” human behavior and mimicking it.

RBA RPA ML
What is it? A system that applies human-made rules to store, sort, and manipulate data. Software “bots” that are capable of observing human behavior and mimicking it, automating routine, rules-based processes. An area within artificial intelligence that uses algorithms and statistical models to learn from data and make predictions or decisions without being explicitly programmed.
What is the main functionality? Requires a human programmer to foresee all potential scenarios and program rules-based logic into the software ahead of deployment. Learns the rules on its own by observing human behavior and mimicking it. However, it still only works on routine, rules-based processes. Solves complex problems involving large amounts of data through predictive analysis. Examples include fraud detection, sentiment analysis, and customer behavior prediction.
What is its limitation? RBA is rigid and does not respond well to change. If the interface changes in any way, RBA will likely malfunction. RPA does not adapt well to change or anomalies. As a result, RPA software often breaks down and/or has to be re-worked. ML can adapt and learn from new data but requires a lot of it to learn and produce precise predictions.
Use cases Used in many different forms in business, but most commonly to automate certain tasks. Used for automating tasks like data entry, invoice processing, or report generation. Used for tasks like fraud detection, sentiment analysis, and customer behavior prediction.

From this point of view, generative AI appears to be the next significant step in data analysis advancements. Its “creativity” can be used to create texts and images and synthetic data.

This type of data can be employed to build supervised learning datasets, especially when real-world data is scarce, sensitive, or imbalanced. Generative AI creates additional data points that are similar to the original dataset but not identical. This approach can help improve the performance of deep learning algorithms, which often require large amounts of high-quality data to function effectively.

Additionally, there is the issue of privacy. Synthetic data can be utilized to replace sensitive or regulated data in AI and machine learning projects, thereby mitigating privacy concerns and ensuring compliance with data protection regulations.

The risks and challenges of using AI in analytics

Since artificial intelligence (AI) is only as good as the data that fuels it, its main challenges are connected with data and the legal implications of using it for scale.

  1. Data quality. Poor data quality can lead to misleading or erroneous insights, which can seriously affect decision-making. Ensuring data quality is particularly challenging due to the sheer volume, velocity, and variety of data that organizations deal with today.
  2. Data preprocessing. Data preprocessing is a crucial step in AI-driven data analytics. However, it can be a complex and time-consuming process, especially when dealing with large and diverse datasets.
  3. Bias and fairness. AI models can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes.
  4. Ethical and regulatory concerns. The use of AI to analyze and interpret sensitive data can lead to concerns about privacy, security, and consent. Ensuring compliance with data protection regulations is paramount.
  5. Complexity of AI algorithms. AI models can be complex and difficult to interpret, making it challenging to understand how they arrive at specific conclusions.
  6. High cost and complexity. AI can require significant investment and expertise to develop, implement, and maintain, as well as pose technical and operational risks.
  7. Privacy and security risks. As AI technologies become more sophisticated, the security risks associated with their use and potential for misuse also increase.

How the use of AI impacts data privacy and security

By nature, AI requires vast amounts of data to function effectively, and this data often includes sensitive personal information. The collection, storage, and processing of such data by AI systems can potentially infringe on an individual’s right to privacy, especially since the complexity of AI algorithms makes them difficult to understand. This means humans may not even be aware that their data is being used to make decisions affecting them.

Another significant challenge is ensuring compliance with data protection regulations such as the General Data Protection Regulation (GDPR). GDPR governs how personal data is collected, stored, and processed, introducing measures like the right to be forgotten or to access one’s own data.

However, GDPR does not provide explicit guidance on AI-related data protection issues, leading to uncertainties over data governance and ownership. With the rise of generative AI, these concerns have only grown: generative AI models can create realistic data that could potentially be used for malicious purposes, such as identity theft, disinformation, or cyberbullying.

How privacy laws affect the use of AI

The natural consequence of the hype around Generative AI is a growing need to craft legal frameworks specific to Generative AI that address all the new issues.

The Italian Data Protection Authority (DPA) has already started this process. And even though its response, limited to ChatGPT, may have been perceived as somewhat nervous and temporary, new general interpretations will certainly be needed. The problem is that no solid legislation is born overnight. Given the rapidly changing AI landscape, it may be hard to understand exactly what’s going on, even for tech-savvy individuals. Not to mention that the hasty adoption of new regulations may unintentionally stifle innovation and inhibit the realization of AI’s beneficial aspects.

The European Union released its initial version of the AI Act in April 2021. It suggested implementing varying degrees of regulatory oversight on AI systems based on their intended applications. Under this proposal, AI deployments in high-risk fields, such as law enforcement, would necessitate procedures such as risk evaluation and the implementation of mitigation strategies. However, the European Commission’s propositions have faced pushback.

France, Germany, and Italy, however, have advocated for a “balanced and innovation-friendly” approach to AI. They also aim to reduce “unnecessary administrative burdens” on companies that would hinder Europe’s ability to innovate. (Source)

Europe does not want to be outpaced once again by competitors from the United States, traditionally more relaxed in terms of regulating Big Tech. Interestingly, however, there has been a surge in state AI laws proposed across the US.

Several states have proposed task forces to investigate AI, while others have expressed concerns about AI’s impact on services like healthcare, insurance, and employment.

Best practices for using AI in analytics

  • Implement data anonymization. By effectively anonymizing sensitive data, businesses can protect individuals’ privacy while still harnessing valuable insights from their data.
  • Adopt privacy by design. It’s crucial to adopt a “privacy by design” approach, ensuring privacy is applied to AI and across business operations and other technology products.
  • Limit and monitor generative AI usage. It’s wise to avoid third-party AI tools that may store and potentially use your data. Instead, ensure you know how these tools utilize data, or consider developing in-house solutions.
  • Use good data quality. Only the data types necessary to create the AI should be collected. The data should be kept secure and only maintained for as long as is necessary to accomplish the intended purpose.
  • Protect personally identifiable information (PII). Most AI algorithms don’t need personally identifiable information (PII). Consider filtering PII out by design.

Conclusion

The rapid advancement of generative AI has ushered in a new era of technological innovation, accompanied by significant challenges and opportunities. 

While AI presents remarkable capabilities both in data analysis and content creation, proving itself to be a huge support for marketers and analytics, it also raises pressing concerns regarding privacy, data protection, and ethical use. 

As we witness a paradigm shift in AI’s role in various sectors, comprehensive, adaptable, and forward-thinking legal frameworks become paramount to harness AI’s full potential. Simultaneously, they are key to safeguarding fundamental rights and fostering an environment conducive to sustainable technological growth.

Author

Kaja Grzybowska

Copywriter

Content marketer with a background in journalism, experienced in creating various content formats to explain technical issues in a clear, straightforward manner.

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