In the media, dinner parties and almost every meeting that your marketing department is having right now, discussions on AI are catching everyone’s attention: how will it change our lives for the better — or potentially for the worse?
Artificial Intelligence (AI) is truly transforming the world of marketing, enabling businesses to scale their operations and improve their targeting, among other benefits.
But the use of AI in marketing also presents a risk — biases inherited from previous campaigns that could limit the potential of machine intelligence. This article explores the dangers of bias in AI and how marketers can avoid it to make the most of the technology.
The danger of AI bias
AI-based “smart” algorithms, also called Weapons of Math Destruction (WMDs), are models designed to operate at scale and “optimise” for profit or other measures of success without taking concepts such as fairness into account.
The fundamental flaw of WMDs is, you guessed it, bias.
By being selective in the data they learn from, using proxies for behaviours that can’t be measured directly, and constantly learning through feedback loops that confuse the models’ performance with reality, WMDs are often implicitly discriminatory.
They perpetuate previous bad practices and compound these issues by reinforcing their flawed decision-making over time.
In the marketing context, biases inherited from previous campaigns could limit the effectiveness of AI. When applying AI to marketing, we unleash the “brain” power of machine intelligence to make our decisions better and more effective.
However, we constrain it by having it learn only from what we’ve done and what we’ve achieved. We might show it data from past campaigns and have it refine and improve our targeting, or take our existing segmentation to much greater levels of granularity and detail. This can produce valuable results and high returns, but we are narrowing the algorithm’s view, so it considers only the scope of our existing efforts.
How to avoid marketing bias with AI
To avoid the dangers of marketing bias with AI, we need to be more willing to run experiments and tests.
Rather than focusing only on what has worked in the past, be open to exploring new opportunities and ideas. For example, send offers to a sample of customers who fall outside your standard marketing selection and add their responses to the data used to train your AI model.
This enables the model to learn any patterns that indicate who should be additionally included. You can also run a test campaign against a random sample of customers and have AI learn from scratch, bias-free, where it works and where it doesn’t.
Another way to avoid marketing bias is to use data and machine learning to find new microsegments that might be overlooked by traditional segmentation.
Clustering algorithms can automatically find groupings in data in as many dimensions as you wish. Marketers can use these microsegments as a targeting aid — look for microsegments with high purchase propensity — and understand customer characteristics that can help them shape better marketing content.
Best practices for AI-powered marketing automation
In addition to avoiding biases in AI, several best practices for AI-powered marketing automation can help businesses optimise their campaigns and deliver better results.
Predictive analytics is a valuable tool for businesses looking to optimise their marketing campaigns. By analysing customer data and behaviour, businesses can use predictive analytics to identify patterns and make predictions about future behaviour.
This lets them tailor their marketing messages and offers to individual customers, increasing engagement and conversions. According to a recent survey by Forbes, 86% of companies that use predictive analytics experience a positive impact on their business, with the most significant benefits being an improved understanding of customers, better decision-making, and increased revenue.
Predictive analytics also helps businesses optimise their marketing spend by identifying the channels and tactics that are most effective in reaching and engaging their target audience. By analysing data on customer behaviour and preferences, businesses can allocate their marketing budget more effectively and maximise their return on investment (ROI).
Overall, predictive analytics is a powerful tool that businesses can use to stay ahead of the competition by providing insights into customer behaviour and preferences.
Focus on omnichannel automation
Omnichannel marketing automation is a powerful way to improve the customer experience and increase engagement. By using AI to deliver personalised messages across all touchpoints, businesses can create a seamless experience for customers, whether they are interacting with the brand on a website, social media, email, or in-store.
According to a recent survey, businesses that adopt omnichannel automation see an average increase of 18.96% in engagement rates and a 12.8% increase in customer retention rates.
AI-powered omnichannel automation can help us keep track of these interactions and respond to customers in real time, increasing engagement and satisfaction.
For example, chatbots can handle basic customer queries and escalate more complex issues to a human representative. AI-powered sentiment analysis can also help us identify negative customer feedback on social media and other channels, allowing us to address concerns and improve our overall customer experience.
Need more proof that omnichannel marketing automation is the way to go? Recent studies show that customers who have a positive omnichannel experience are more likely to make repeat purchases and recommend the brand to others.
According to a study by Harvard Business Review, omnichannel customers have a 30% higher lifetime value than those who shop using only one channel. Another study by Accenture found that 72% of customers expect businesses to know their purchase history across all channels.
By leveraging AI to create a seamless omnichannel experience, we can improve customer loyalty and boost sales.
But, it’s still important to ensure that AI-powered automation is not used as a substitute for human interaction. Customers will always value a personal touch, and businesses need to strike the right balance between automation and personalization.
How to avoid bias in generative AI
Like with all forms of AI, avoiding bias in generative AI is crucial to ensure fair and equitable outcomes. To mitigate bias, several strategies should be implemented.
First, use diverse and representative training datasets, encompassing various demographics, cultures, and perspectives. This helps prevent the model from inadvertently favouring any particular group.
Second, continuous monitoring and evaluation of the model’s outputs for biased content are necessary, along with the utilization of predefined guidelines to flag potentially biased or sensitive outputs. Regularly updating and refining these guidelines based on user feedback is essential.
Third, fine-tuning the model on specific tasks using data that has been carefully reviewed and debiased can help minimize the risk of generating biased content.
Lastly, fostering interdisciplinary collaboration involving ethicists, sociologists, and domain experts can provide a more holistic understanding of potential biases and aid in developing effective bias-mitigation strategies. By implementing these approaches, generative AI can produce more equitable and unbiased results that benefit all users.
Take ChatGPT, for example.
To mitigate bias, OpenAI employs a multi-stage process. Initially, a diverse range of data is used for training, encompassing various perspectives and viewpoints. This helps the model avoid becoming skewed towards any particular group. However, biases can still emerge during training. To address this, the model is fine-tuned with the help of human reviewers who follow guidelines explicitly designed to avoid favouring any political, social, or cultural group.
These guidelines highlight potential pitfalls related to bias, controversial topics, and other sensitivities. OpenAI maintains an iterative feedback loop with reviewers through weekly meetings, allowing them to learn from each other’s insights and enhance the model’s performance over time.
This careful oversight and collaboration help ChatGPT produce more balanced and unbiased responses, making it a prime example of how proactive strategies can be employed to counter bias in generative AI systems.
Best practices for AI in marketing
To ensure that our use of AI in marketing is effective and ethical, there are several best practices we should follow:
Use diverse data: To avoid bias in AI models, use diverse data sources that include a variety of perspectives. And regularly review your data sources to ensure they remain up-to-date and accurate.
Continuously monitor AI models: AI models should be constantly monitored to ensure that they’re making decisions that align with your values and goals. Regularly review and update your models to ensure that they remain effective and relevant.
Ensure transparency: To build trust with customers and stakeholders, it’s important to be transparent about your use of AI in marketing. This includes disclosing the data sources you use, how you train your models, and how you make decisions based on AI-generated insights.
Invest in human talent: While AI can help us automate repetitive tasks and improve decision-making, it’s important to invest in human talent to ensure that you have the skills and expertise to effectively manage AI-powered marketing. This includes hiring data scientists, machine learning engineers, and other professionals with experience in AI and marketing.
Use AI to augment human intelligence: AI should be used to enhance human intelligence, not replace it. (Read that sentence again, please… we’ll wait.)
Use AI to automate repetitive tasks and provide insights that help you make better decisions. But never forget the value of human creativity, empathy, and intuition in marketing — especially when it comes to creating truly personalised customer experiences.
AI has the potential to revolutionise marketing by enabling us to create more personalised, effective, and ethical campaigns. And in many ways, it already has.
But to fully realise these benefits, we must be aware of the potential for bias in AI models and take steps to ensure that our use of AI in marketing is transparent, diverse, and aligned with our values and goals.
By following best practices and leveraging AI to augment human intelligence, we can all create a brighter future for marketing — one that’s more engaging, more satisfying, and more profitable for businesses and customers alike.