Wiland Blog

The Role of AI in Targeted Marketing: What You Need to Know

To say that there is some hype about AI in the marketing industry would be an understatement. But what are the real-world uses of AI for improving the performance of data-driven marketing?

By Wiland Editorial Team | January 23, 2024

AI Targeted Marketing

Artificial intelligence (AI) has dominated conversations in the marketing industry perhaps more than any other topic over the past year. With the rise of easily accessible AI tools such as ChatGPT, it has gone from just a buzzword to a legitimate capability that has captured the attention of forward-looking advertisers and fundraisers. Nevertheless, the overwhelming confusion and misconceptions around AI need to be addressed.

So we spoke with a panel of experts from across the Wiland team—Will Clayton (SVP, Digital Products), Cameron Popp (VP, Solutions and Innovation), and Hayley Howard (Director, Predictive Client Solutions)—to gain their perspectives on the role of AI in data-driven marketing. They discussed how Wiland is using AI to create high-performance marketing audiences and enhancement data, what marketers and fundraisers need to consider to ensure the ethical use of AI, and what the future may hold for this exciting technology.

Q&A Blog with Will Clayton, Cameron Popp, and Haley Howard

How have you seen perceptions about AI evolve in recent years?

Cameron: “AI has been in use for decades, but the recent conversation around it doesn’t always reflect the reality of its evolution in marketing and other use cases. This is evident in how many misconceptions remain, such as AI being synonymous with ChatGPT, that AI is a new technology, or that AI will be taking people’s jobs. It’s amazing how rapidly AI has come into focus in recent years not just as a tool, but as a cultural phenomenon. It will be interesting to see how the AI conversation will change over the next twelve months.”

Will: “Think back to when IBM taught its ‘Watson’ to play (and win) on the TV game show Jeopardy in 2011, which was a paradigm-shattering moment for AI. I think that AI had been considered a limited technological agent prior to that. But then people started thinking more about the potential for AI to move beyond reacting to ‘if’ and ‘when’ statements and better understand and ‘speak’ human language. AI has been used in the marketing industry to analyze data for a long time, but that interest has spiked recently because of advances in ‘consumer-grade’ AI. Recent Generative Pretrained Transformer (GPT) systems from OpenAI and others are showing signs of what we’d actually call intelligence within very user-friendly products.”

Hayley: “I agree. AI may have just burst into the zeitgeist in recent years with the introduction of easily accessible public tools, but the recent popularity of the term also draws attention to how AI has been used with great success in optimizing marketing solutions for many years.”

Is there misinformation about AI that you think needs to be corrected?

Hayley: “Yes. The over-use of ‘AI’ as a term has created a lot of confusion and oversimplified a very complex topic. There are multiple forms of AI that are used to solve many different problems.”

Cameron: “The biggest hurdle for responsible AI adoption right now is confusion about what AI can do and how to harness its capabilities. For instance, AI doesn’t ‘do’ anything by itself. Instead, it does what its creators have programmed it to do and what its users instruct it to do. Thinking of AI as a broader set of tools is the first real step to fixing misunderstandings about how to use it.”

Will: “I think that the biggest misconception about AI is that it will take away people’s jobs. This type of fear has accompanied every major technical advancement in recent history, and the opposite ends up being true. As with other technologies, AI can help with handling the ‘heavy lifting’ of many tasks for operators, which will actually help them move up the labor value chain and devote more of their time to strategic thinking and product enhancement.”

How would you define AI and machine learning?

Cameron: “This is a great question, as AI and machine learning actually encompass a large variety of different specific technologies that roll up into these umbrella terms. It’s important that we use precise language to talk about AI.”

Hayley: “We actually discussed this in a blog post back in 2021, and the definitions used there are still accurate and relevant. In that piece, we defined AI as a software program that makes its own decisions about how to solve a problem or accomplish a task within a set of rules determined by a data scientist. There are two primary types of AI: Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI). AGI is the human-like intelligence that you often see in science fiction movies. ANI—also known as machine learning—is the real-world AI that operates within Wiland’s analytics platform. It is a crucial element in creating custom-modeled audiences with the highest likelihood to engage with and respond to a brand’s marketing. Essentially, machine learning is a type of AI that iterates on a task in massive cycles to improve performance.”

Within the realm of marketing and fundraising, who stands to gain the most from advances in AI?

Cameron: “Honestly, everyone! But it’s all dependent on two things: first, the quality of the underlying data sets being used, and second, how well the AI-driven solutions are implemented.”

Hayley: “I agree that any marketer or fundraiser can benefit from using AI-driven solutions. AI can be leveraged to target campaigns more effectively and make them more accurately personalized based on audience preferences and behaviors. This improved targeting drives more efficient marketing and helps organizations better reach the individuals most likely to engage. AI should drive ROI!”

Will: “AI stands to fill in large swaths of tactical analysis and marketing production tasks. But the organizations that stand to benefit the most from AI will be distinguished by two factors: strategic acumen and high-quality data. AI is not magic. It’s crucial that managers are able to understand how their teams can best use AI and then ensure that the AI is being applied to the most relevant, impactful data to achieve positive results and long-term success. In some cases, that means working with a partner who has the AI acumen or the data that a brand or nonprofit needs but may lack internally.”

We should strive to use AI in ways that reflect the best of humanity, not our shortcomings.

What are some of the most crucial use cases for AI in marketing and fundraising?

Will: “Underlying all of AI’s capabilities is its capacity to easily understand how data points fit together, their correlative impacts, and ultimately what they mean to a marketer. Marketers have spent decades creating handfuls of personas each with their own communications, creative, and strategy, all in pursuit of the elusive ‘one-to-one marketing.’ AI opens the door to the possibility of recognizing and responding to very large numbers of discreet market segments that human beings simply do not have the bandwidth to address at that scale. Mass marketing may never achieve true one-to-one engagement with every individual consumer, but we are starting to see AI tools that could conceivably get close.”

Cameron: “There are many use cases, but three main areas in which marketers can leverage AI: 1) creative inspiration and content development using generative AI tools; 2) deploying abilities to augment human work in things like expanding operational capacity in scheduling, resource management, and logistics; and 3) tapping into large datasets for analytic insights and marketing action. This third area is where Wiland comes in.”

Hayley: “Specifically, AI can be used in predictive modeling to improve customer, donor, and subscriber segmentation—allowing marketers to optimize their housefiles more effectively and only market to people likely to respond. Similarly, AI can be used in predictive modeling to create the most responsive acquisition audiences for prospect marketing by analysis of trillions of spending and interest-intensity signals.”

How does Wiland use AI in its data analysis and predictive modeling processes?

Will: “Wiland’s laser focus has always been on understanding consumers and their attributes better than anyone else. To that end, we have pioneered the use of the most effective AI and machine learning techniques to inform the creation of high-response marketing audiences and predictive data elements.”

Hayley: “We have used AI and machine learning for many years to improve modeling efficiencies that increase the responsiveness of marketing audiences for our clients. AI programs can analyze huge volumes of data to find variables that predict response that is simply not possible with human analysis alone. Our AI-enabled platform analyzes trillions of dollars in spending, billions of purchase intent signals, and billions of social media signals for over 250 million U.S. adults. Our AI programs not only analyze this information, but identify the dynamic interactions within the data used in a model to recognize unexpected, yet highly relevant, patterns and correlations. This not only results in better-performing marketing audiences, but also in more time for our data scientists to be able to think strategically about model usage and collaborate even more closely with our clients.”

With bad data, AI will just take you to the wrong place faster.

Why is a foundation of high-quality data so important when creating AI-driven marketing solutions?

Cameron: “As with any tool built on data, the function and precision of AI solutions are dependent on the quality of the data being used in an AI system. AI doesn’t care about how clean or accurate that data is. Instead, it is programmed to ingest the data irrespective of quality or hygiene and to produce the output per the programming. As our colleague Roger Hiyama (EVP, Solutions and Innovation) says, ‘With bad data, AI will just take you to the wrong place faster.’”

Hayley: “AI, and specifically machine learning, trains itself to find the best possible model algorithm. In the use case of creating an acquisition audience for a brand, if the data being analyzed doesn’t represent a complete picture of the brand’s customers, donors, or subscribers, the model will still use that data to run its analysis and create a model algorithm. The consequences could be that an audience is not as representative of the brand as it could have been with more complete, higher quality data.”

Will: “Source data is key. AI can only respond to the data on which it is trained and the information it is given to interpret. We are always careful to apply AI-driven solutions using the most comprehensive, relevant, high-quality data. That’s the only way that AI can be used to produce the most predictive, most impactful results.”

What do marketers need to think about when it comes to ensuring they (and their partners) are using AI ethically?

Cameron: “AI can’t play by different rules than any other tool for data management, creative development, or marketing. That means that AI must adhere to data privacy and permission restrictions in the same way that any user or platform accessing that data must. AI runs on data and, if that data belongs to your donors, customers, or prospects, you have a responsibility to treat that data properly. Simply put, marketers using AI can’t pass the buck on important ethical and legal requirements any more than they could when using a non-AI application. There is massive opportunity with AI, but marketers have a responsibility to use any data or AI platforms in ethical and privacy-compliant ways.”

Hayley: “Marketers must stay informed about all regulations surrounding AI and continuously evaluate their data practices and those of their partners. Marketers should also consistently check to ensure that there are no biases being introduced by the AI to ensure that the solutions it’s helping produce are truly objective and accurate.”

Will: “When considering any applications of AI, there must be awareness that these tools will only ‘know’ what we teach them and behave in the ways we instruct them. We must constantly consider how the outputs that these tools create will impact the human beings that we serve. From ensuring that the data that AI is founded on is accurate and unbiased to staying aware of AI’s limitations when it comes to issues such as copyright violation, we should strive to use AI in ways that reflect the best of humanity, not our shortcomings.”

Marketers using AI can’t pass the buck on important ethical and legal requirements any more than they could when using a non-AI application.

How do you foresee AI impacting the marketing landscape moving forward?

Cameron: “Like any tool, AI will continue to be shaped by the people who use it wisely and prove its utility in multiple marketing use cases. Marketers with real imagination who pioneer new, innovative ways to use AI ethically will make very impactful changes for their organizations and the industry as a whole.”

Hayley: “Marketing analytics will continue to advance as AI and machine learning methods improve. For Wiland, that means continuous improvement in the results that our AI-driven, predictively modeled audiences produce across all platforms and channels for our clients.”

Will: “The best marketers will quietly grow their AI capabilities to strengthen their brands—using AI tools to make themselves, their teams, and their organizations more effective. At Wiland, we will continue to leverage the best AI capabilities that help us improve how well we serve our clients in this world of exciting new possibilities—all with the highest ethical standards at the core of our practices.”