The Role of AI-Based Marketing in a Post GDPR World

Transparency, trust, model bias, a consumer’s right to an explanation, fairness, consumer preference management and compliance are evolving as critical business requirements directly associated with marketing in a post GDPR world. Yet the arrival of AI and its current role in marketing stands in direct conflict with many of these requirements, because of the black box nature of machine learning algorithms.

In a Wall Street Journal article titled, Tech Giants Launch New AI Tools as Worries Mount About Explainability, it’s reported that “About 60% of 5,000 executives polled in a recent study by IBM’s Institute of Business Value, said they were concerned about being able to explain how AI is using data and making decisions in order to meet regulatory and compliance standards.”



GDPR (Global Data Protection Regulation) is the new standard for consumer privacy and the bar couldn’t be higher in the realms of customer marketing and management, at a time when AI’s emerging role is limited by its ability to provide the level of transparency and trust required by its regulations.

So what is the role of AI in marketing then, given this challenge? There is no question AI provides tremendous efficiency gains for analytic driven marketing. Faster modelling with greater precision provides significant efficiency gains that translates into faster campaigns, speed to market and improved results. There are also many discrete tasks where AI adds value in automating, typically ones that are highly manual or real-time transaction based.

However, without explainability or transparency, there are significant limitations in the potential role of AI in marketing. If machine learning algorithms were explainable, it would open up a range of possibilities that are critical in a post GDPR world. The good news is there is explainable machine learning technology that operates at scale and speed, with precision. Here are areas where explainable AI can break from AI’s traditional black box limitations and expand its role in a post GDPR world.



Insight driven businesses can discover information about customers, transactions, products, geographies, or even vast amounts of social media comments and opinions it didn’t previously know, at greater speed and granularity. For example, uncovering the fact that certain customer segments who buy one product, don’t buy another, vs. other segments that do, is a pretty significant discovery if you understand why.

Knowing why enables businesses to change its offers at greater speed with greater relevancy to the right audiences. One approach could increase sales, the other reduce cost. These basic decisions can only be supported if algorithms provide explainability.



Patterns can be subtle. The cause and effect of weather, political or sporting events, economic changes, the change or action of a competitor, negative publicity, etc. can correlate to a broad range of impacts in ways that are not easily seen.

Explainable algorithms can see these correlations and reveal the main drivers behind them. If we see a certain event occur, what patterns in behaviour do we understand from the past, to predict and act on the likely to occur behavioural effects of that event happening again.



Seeing new, anomalous instances of behaviour or activity is a way to notice an emerging trend. The data supporting a new anomalous event, such as a fraud attempt that has never before been seen, will provide the foundation for spotting an emerging trend.

What characteristics define it? Will another event with similar characteristics occur and if so how often? If it does occur again are their characteristics about this event that make it preventable from a business perspective? Emerging trends can be dangerous or positive – either is a missed opportunity to prevent significant losses with fast action, or realise a new opportunity.



Knowing the cause or reasons that explain a predicted behaviour allows for immediate action. Let’s take churn as one example. Not knowing why someone is predicted to churn paralyses a company from appropriately preventing the churn from happening. The same applies to product recommendations.

Explainable algorithms can reveal this basic information if fed the right information. In a world where relevancy is critical, explainability makes algorithmic predictions actionable and customer communication relevant.



Innovation can be constantly fed from existing customers and how they use your product or service. If high value customers demonstrate common characteristics of usage or lack thereof, perception, inactivity, neglect, reduction in volume, etc. This can lead directly to innovations that are fuelled by why customers behave or think as they do.



Explainable AI enables a company to trace back and reveal why a consumer decision was made, with regard to any dimension of customer decision-making. Why was a specific offer made, channel used, or credit / loan application accepted or denied? Being able to explain for each individual decision, why that decision was made is an increasingly vital requirement for businesses to have in place. Explainability enables this at great speed and scale.



The ability to audit how and what data an algorithm is using and how important each data element is to an algorithmic decision is increasingly important. What appears to be innocuous data, can often be heavily correlated with race or gender, inadvertently. Without explainability, the inappropriate use of data would not necessarily be caught and could lead to issues down the road. Explainable AI can help spot or disprove how and what role specific data is playing in making consumer decisions.

The role of explainable AI based marketing is far greater in a post GDPR world than black box approaches. Businesses would be wise to invest in AI that is explainable, to ensure that as regulations evolve, they are positioned to meet the stringent regulatory requirements while leveraging the use and impact of AI across their business as broadly as possible.

About Dave Irwin

Dave Irwin is the CMO of simMachines, and a 25-year industry veteran with expertise in analytic driven marketing spanning traditional CRM, digital and addressable TV and online video.