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AI and First-Party Data: Why a ‘Set It and Forget It’ Strategy Doesn’t Work

First-party data has been and continues to be an organization’s most valuable asset in its quest to engage target audiences. And while many brands do still supplement their digital marketing efforts with third-party cookies to try and obtain the elusive 360-degree view of their customers, that strategy will be short-lived with the sunsetting of third-party cookies expected in 2024.

Most forward-thinking organizations are already evolving past strategies that hinge on third-party data given the uncertainty around its future, but are still struggling to centralize and activate their first-party data across channels. AI will become a critical tool for omnichannel data activation to address the shortfalls created by data deprecation. The power of AI to help organizations target individuals with the right o**** at the right time to yield profitable results is unmatched.

But in the marketing landscape, AI can also go horribly wrong. Fortunately, with responsible strategies brands can ensure their AI is ethical and accountable to make the best decisions possible based on their existing data for the ultimate benefit of their customers.

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Here’s what to keep in mind when implementing your own AI-powered first-party data strategy:

Understanding AI and First-Party Data – The Good, the Bad, and the Ugly

AI applied to first-party data in the right way can yield massive results. It helps brands make customer engagement more dynamic and create healthier, more valuable long-term customer relationships. As consumer expectations for personalized, yet privacy-friendly, communications become higher than ever before, AI can analyze data signals and understand exactly what a particular customer needs at that moment in time. For example, when a flight is delayed or canceled, an airline offering to re-book a traveler on a comparable flight by serving those flight offers directly to their mobile app is a clear differentiator to those travel brands whose customers have to call customer service to perform the same action. This presents a more authentic and helpful touchpoint that goes a long way with customers and prevents them from seeking services from a competitor. 

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But it’s important to also understand how an AI-powered interaction can go wrong, and how to prevent that. When using AI technology without a well-thought-out data strategy behind it, the technology can operate with unintentional bias, incorrectly learning from the data it’s presented with. This can result in actions like offering a loan to someone who is about to file for bankruptcy, or offering a credit card with better benefits to male vs. female customers – a terrible experience for the customer and damaging to the brand’s reputation.  

AI and Ethics Should Go Hand in Hand

As brands use AI to interact with their customers, they need to ensure they’re doing so ethically. 

Part of the promise of delivering more ethical AI-driven engagements is the need for brands to self-regulate. And yes, many countries around the world are developing regulations and restrictions around AI in marketing and advertising to protect consumers, but the rate of innovation is outpacing regulators and just because something is legal doesn’t make it ethical. So, while legally it may result in a sale to o**** someone a new credit card who has bad credit and cannot pay their bills on time, this is not an ethical decision and ultimately reflects very poorly on the brand while causing harm to the customer. 

Keeping brands safe and customers satisfied requires a proper balance between a brand’s objectives and what’s best for the customer. At the end of the day, if you’re acting in the interest of your customers, your brand will develop stronger and more genuine relationships that will benefit both the company and customers in the long-term. 

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Brands can ethically use their valuable first-party data to understand and act on a customer’s unique needs in any given situation. Based on their existing information, is there an opportunity to help a customer in need of financial assistance during a tough time? Could you o**** them a better d*** on services they’re already currently using?

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AI can help sort through data to best understand customer context and needs, and prompt brands to present customers with the best interactions possible at any moment in time. 

Keep Transparency Top-of-Mind

When it comes to AI there are both transparent and opaque algorithms. Transparent algorithms are explainable to a human audience (i.e., we can clearly show how the AI made a decision). Opaque algorithms obfuscate decision-making processes from anyone who’d like to understand why a certain outcome was achieved. A mortgage r******** o**** represents a simple example of a process that can deliver either opaque or transparent actions.

A very straightforward approach would be for a financial institution to o**** existing members with credit scores above 800 the opportunity to r******** their mortgage at a low interest rate as part of a marketing program aimed at taking share from competitors.  There would only be two qualifiers in this scenario: that the applicant is an existing customer and that their credit s**** is equal to 800.  

To help ensure a brand is considered trustworthy when using AI with first-party customer data, algorithms and their decisions need to be transparent.

Transparency means that they need to be fair and explainable, not opaque or a black box. A transparent algorithm can be inspected to understand the lineage of the AI – who created it (human or machine), what data is used, how it is tested, and how a decision is made based on that data. As more and more consumers begin to understand AI and the power it wields, it’s likely they’re also going to want to know how their data is being used in this context. As a result, organizations that o**** a transparent look into their AI processes will ultimately gain a more trustworthy and respected reputation, not only among consumers but also in the greater industry. 

Be Accountable with Proper Governance

And finally, when it comes to AI and first-party data, accountability is a critical part of implementing an effective strategy. AI can’t be left to run wild. There needs to be people and processes in place to correct issues as they arise. From the most complex algorithm to basic marketing automation, there must be continuous monitoring of how AI is using first-party data to ensure it doesn’t veer off course, and if it does, knowing how to course correct swiftly so customers aren’t negatively impacted. Your AI systems are only as good as the data that feeds them – if an issue is detected and isn’t corrected by a human, the algorithm can reinforce biases that exist within that data. It’s imperative that all of us have a stake in enabling more ethical AI for marketing.  

When coupled with first-party data, AI and machine learning help business leaders get the most return on investment when it comes to their MarTech and AdTech stacks, but businesses also need to be careful that AI doesn’t lead to damaging bias. Organizations need to better manage and activate the extremely valuable and rich data they already have at their fingertips because, when implemented correctly, AI holds the power to deliver better outcomes for both the brand and its customers.

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[To share your insights with us, please write to sghosh@martechseries.com]

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