Many companies - and the marketing teams that support them - are rapidly adopting intelligent technology solutions to encourage operational efficiency while improving the customer experience. These intelligent solutions often come in the form of Artificial Intelligence (AI) marketing platforms. Through these platforms and generative attribution, marketers can gain a more nuanced, comprehensive understanding of their target audiences. The artificial intelligence-driven insights gathered through this optimization process can then be used to drive conversions while easing the workload for marketing teams.
What is AI (Artificial Intelligence) Marketing?
AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in digital marketing efforts where speed is essential. Generative AI is often used in digital marketing efforts where speed is essential. AI marketing tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s digital marketers, Generative AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.
AI Marketing Use Cases Include:
- Data Analysis: Collecting and sifting through large amounts of marketing data from various campaigns and programs that would otherwise have to be sorted manually.
- Natural Language Processing (NLP): Creating a human-like language for content creation, customer service bots, experience personalization, and more.
- Media Buying: Predict the most effective ad and media placements for a business to reach its target audience and maximize marketing strategy ROI.
- Automated Decision-Making: AI marketing tools help businesses decide which marketing or business growth strategy they should use based on past data or outside data inputs.
- Content Generation: Writing both short and long pieces of content for a marketing strategy, such as video captions, email subject lines, web copy, blogs, and more.
- Real-time Personalization: Changing a customer’s experience with a marketing asset such as a web page, social post, or email to fit the customer’s past preferences to encourage a certain action, such as clicking a link, signing up for something, or buying a product.
What Are the Types of AI Marketing Solutions?
It’s clear that artificial intelligence holds a vital role in helping marketers connect with consumers. The following components of AI marketing make up today’s leading solutions that are helping to bridge the gap between the vast amounts of customer data being collected and the actionable next steps digital marketers can take in their future campaigns:
Machine Learning
Machine learning is driven by artificial intelligence, which involves computer algorithms that can analyze information and improve digital marketing campaigns automatically through experience. Devices leveraging machine learning analyze new information in the context of relevant historical data, which can inform digital marketing campaigns based on what has or hasn’t worked.
Big Data and Analytics
The emergence of digital media has brought on an influx of “big data,” which has provided opportunities for digital marketers to understand their efforts and accurately attribute value across channels. This has also led to an over-saturation of data, as many digital marketers struggle to determine which data sets are worth collecting. AI marketing can help parse through all that data at lightspeed, filtering it to its essentials, analyzing it, and recommending the best elements of future digital marketing campaigns.
AI Marketing Platforms & Tools
Effective AI-powered marketing solutions provide digital marketers with a central platform for managing the huge amounts of data being collected. These AI marketing platforms have the ability to glean insightful marketing intelligence from your target audience so you can make data-driven decisions about how to reach them best. For example, frameworks such as Bayesian Learning and Forgetting can help marketers better understand how receptive a customer is to a specific digital marketing effort.
What Are the Challenges for AI Marketing?
Modern marketing relies on an in-depth understanding of customer needs and preferences and the ability to act on that knowledge quickly and effectively. The ability to make real-time, data-driven marketing decisions has brought AI marketing solutions to the forefront for marketing stakeholders. However, marketing teams must be discerning when deciding how to best integrate AI into their campaigns and operations. The development and use of AI marketing tools is still in its early stages. Therefore, there are a few challenges to be aware of when implementing AI into your digital marketing strategy.
Training Time and Data Quality
AI marketing tools do not automatically know which actions to take to achieve marketing goals. They require time and training, just as humans do, to learn organizational goals, customer preferences, and historical trends, understand the overall context, and establish expertise. This learning process also requires data quality assurance. Suppose your AI marketing tools are not trained with high-quality data that is accurate, timely, and representative. In that case, you’ll end up with inaccurate data decisions that don’t truly reflect consumer desires, making your shiny new AI marketing tool nothing more than a toy.
Privacy
Consumers and regulating bodies are cracking down on how organizations use their data. Digital marketing teams must ensure they are using consumer data ethically and in compliance with standards such as GDPR while implementing an AI marketing strategy or risk heavy penalties and reputation damage. Unless your AI marketing tools are specifically programmed to observe specific legal guidelines, they may overstep what is considered acceptable in terms of using consumer data for marketing personalization.
Getting Buy-In
It can be difficult for digital marketing teams to demonstrate the value of AI marketing investments to business stakeholders. While KPIs such as ROI and efficiency are easily quantifiable, showing how AI marketing has improved customer experience or brand reputation can be less obvious. With this in mind, digital marketing teams need to ensure they have the right measurement tools, like generative attribution, to attribute these qualitative gains to generative AI investments.
Deployment Best Practices
Because AI marketing is a newer tool in the digital marketer’s toolbox, definitive best practices haven’t yet been established to guide marketing teams’ initial deployments, every digital marketing team should think about the long-term effects of implementing an AI marketing strategy, not just the short-term benefits they could reap.
Adapting to a Changing Marketing Landscape
With the emergence of AI marketing comes a disruption in day-to-day marketing operations. Marketers must evaluate which jobs will be replaced and which will be created. One study suggested that nearly 6 out of every 10 current marketing specialist and analyst jobs will be replaced with marketing technology.
What Are the Pitfalls of AI in Marketing?
Here are some common pitfalls marketers should consider when implementing AI in their marketing campaigns.
Legacy Models with Flawed Data and Tech Limitations
Legacy models with flawed data and tech limitations are no longer relevant in today's fast-paced, data-driven marketing landscape. These models served a purpose in the past, but advancements in technology and the availability of big data have made them ineffective. AI for marketing should not rely on outdated models built for the past's restrictions and limitations. Instead, new and innovative models must be developed to fully leverage the potential of AI and the wealth of data available today. Marketers must adapt and embrace these new models to stay competitive in the ever-evolving digital landscape.
Lack of Transparency and Interpretability
AI marketing algorithms and decision-making processes require transparency and interpretability. Marketers must trust the algorithm, and to build trust, they should include internal and external experts who understand algorithms. The AI marketing partner they're considering should be completely transparent with methodology & algorithms. Interpreting the results of AI algorithms can be challenging due to their complex and difficult-to-understand decision-making processes. To address these issues, marketers should prioritize transparency by clearly explaining how AI algorithms work and sharing the data and methodologies used. There must be a clear and transparent ability to compare algorithm recommendations with real-world truth sets to ensure trust and confidence in AI marketing processes.
Ethical Concerns and Bias
AI marketing involves using AI algorithms to analyze consumer data and create personalized marketing campaigns. However, ethical concerns and bias are essential aspects to consider. Privacy and data protection issues can arise due to AI algorithms' access to personal information. There is also a risk of bias in AI algorithms, which may result in discriminatory and unfair marketing campaigns. It is crucial to explore and understand the impact of these issues on marketing practices. To address bias, it is important to conduct audits of algorithms, ensure diversity in training data, and implement transparency and accountability measures. This includes ensuring that the model training data represents the business and the marketplace.
Overreliance on AI without Human Expertise
The rise of AI in marketing has raised concerns about relying too heavily on AI without human expertise. AI can automate marketing processes and offer useful insights, but it lacks the human touch and emotional intelligence found in humans. Human expertise is crucial in AI marketing as it allows for comprehending and interpreting complex consumer behavior, emotions, and preferences. Without human involvement, there is a risk of overlooking vital nuances and context that AI might not understand. Therefore, it is important to balance AI capabilities and human insights for effective marketing strategies that connect with the target audience.
Data Privacy and Security Risks
Extensive customer data collection and analysis can result in breaches and unauthorized access to sensitive information. This can lead to identity theft, financial loss, and damage to a company's reputation. Therefore, marketers must understand the potential risks of handling customer data and implement best practices for ensuring data privacy and security in their AI marketing efforts. Robust security measures, such as encryption and secure storage, should be implemented, along with adherence to privacy regulations and industry standards, to protect customers and the company's brand.
Access to Proprietary Data
Marketers need access to proprietary data to gain insights about their target audience, industry trends, and market competition. However, it's crucial to protect this data from being accessed or used by AI providers. The AI-tech partners should not be able to share or use the marketer's data beyond the specified boundaries set by the marketer's company. This ensures confidentiality, security, and the preservation of the marketer's competitive advantage and sensitive information.
How to Use and Leverage AI in Marketing Campaigns
It’s important to begin with a thorough plan when leveraging AI in marketing campaigns and operations. This will ensure marketing teams minimize costly challenges and achieve the most value from their AI investment in the least amount of time.
Before implementing any AI marketing tools, there are a few key factors for digital marketers to consider:
Establish Goals
As with any marketing program, it is important that clear goals and marketing analytics are established for your AI marketing program from the outset. Start by identifying areas within campaigns or operations that could stand to be improved, such as segmentation. Then, establish clear KPIs that will help illuminate how successful the AI-augmented marketing campaign has been – this is especially important for qualitative goals such as “improve customer experience.”
Data Privacy Standards
At the outset of your new marketing program, be sure that your AI marketing platform will not cross the line of acceptable data use in the name of data personalization. Be sure data privacy standards are established and programmed into your AI marketing platforms as needed to maintain compliance and consumer trust.
Data Quantity and Sources
In order to get started with AI marketing, digital marketers typically need to have a vast amount of data at their disposal. This data will train the AI marketing tool in customer preferences, external trends, and other factors that will impact the success of AI-enabled marketing campaigns. This data can be taken from the organization’s CRM, previous marketing campaigns, and website data. Additionally, marketers may supplement this with second and third-party data, including location data, weather data, and other external factors that may contribute to a purchasing decision.
Acquire Data Science Talent
Many marketing teams lack employees with the necessary data science and AI expertise, making it difficult to work with vast amounts of data and deliver insights. To get AI marketing programs off the ground, organizations should work with third-party organizations that can assist in collecting and analyzing data to train their tools for optimal performance and facilitate ongoing maintenance.
Maintain Data Quality
As machine learning and AI marketing programs consume more data, they will learn to make accurate, effective decisions. However, if the data fed to your AI marketing program is not standardized and error-free, the insights will not be useful. They can actually cause decisions that hurt your bottom line rather than help it. Before implementing any AI marketing programs, marketing teams must coordinate with data management teams and other lines of business to establish processes for data cleansing and data maintenance. When doing so, consider the seven essential data dimensions:
• Completeness
• Consistency
• Relevance
• Transparency
• Accuracy
• Representativeness
Create an Integrated AI Marketing Strategy
Most digital marketers find their AI marketing tools are especially effective when integrated with their existing marketing strategy rather than used as a stand-alone tactic. AI marketing tools create opportunities to optimize steps in a marketing strategy that might currently be labor-intensive, such as data analysis, or have a risk of inaccuracy, such as attribution.
By incorporating AI to fill in these “blind spots,” digital marketers can not only leverage these powerful forward-thinking methods to take their marketing to the next level but also build on the foundation of effective digital marketing methods they’ve used in the past.
How to Select Selecting an AI Marketing Platform
Selecting the right platform or platforms is a crucial step in getting an AI marketing program off the ground. Marketers should be discerning in identifying the gaps the platform is trying to fill and select solutions based on capabilities. This will revolve around the goal marketers are trying to achieve. For example, speed, and productivity goals will require different functionality than tools used to improve overall customer satisfaction with AI.
One thing to keep in mind when selecting a tool is the level of visibility you will need regarding why an AI marketing platform made a certain decision. Depending on the algorithm in use, digital marketing teams may get a clear report on why a certain decision was made and which data influenced the decision. In contrast, algorithms working on a more advanced level with deep learning may not be able to give definitive reasoning.
What Are the Benefits of Leveraging AI in Marketing?
There are myriad use cases for incorporating AI marketing into your digital marketing programs, and each of these use cases yields different benefits. For example, AI marketing can be an asset in areas such as risk reduction, increased speed, greater customer satisfaction, increased revenue, and more. Benefits may be quantifiable (number of sales) or not quantifiable (user satisfaction). There are a few overarching benefits that can be applied across AI marketing use cases:
Increased Campaign ROI
If leveraged correctly, marketers can use AI marketing to transform their entire marketing program by extracting the most valuable insights from their datasets and acting on them in real-time. AI platforms can make fast decisions on how to allocate funds across media channels best or analyze the most effective ad placements to engage customers more consistently, getting the most value out of campaigns.
Better Customer Relationships & Real-Time Personalization
AI marketing can help you deliver personalized messages to customers at appropriate points in the consumer lifecycle. It can also help digital marketers identify at-risk customers and target them with information that will get them to re-engage with the brand.
Enhanced Marketing Measurement
Many organizations have trouble keeping pace with all the data digital marketing campaigns produce, making it difficult to tie success back to specific campaigns. Dashboards that leverage AI marketing allow for a more comprehensive view of what is working so that it can be replicated across channels and budgets allocated accordingly.
Make Decisions Faster
AI marketing is able to conduct tactical data analysis faster than its human counterparts and use machine learning to come to fast conclusions based on campaign and customer context. This gives team members time to focus on strategic initiatives that can then inform AI-enabled campaigns. With AI marketing, digital marketers no longer have to wait until the end of a campaign to make decisions but can use real-time analytics to make better media choices.
What Are the Examples of AI in Marketing?
AI marketing is being used in digital marketing initiatives in a multitude of use cases across a broad array of industries. Industries leveraging AI marketing and its optimization capabilities include financial services, government, entertainment, healthcare, retail, and more. Each AI marketing use case offers different results, from customer retention improvements to campaign performance, enhanced customer experience, or greater efficiency in marketing operations.
There are numerous ways marketers can take advantage of AI marketing and machine learning to create a more comprehensive marketing plan. Consider the following:
1. Bidding on Programmatic Media Buys
A problem that marketing teams often encounter is deciding where to place advertisements and messaging. Marketing teams can create informed plans based on user preferences, but these teams are often not flexible or agile enough to alter the plan in real-time based on the latest consumer information. Digital marketers are using AI marketing to mitigate this challenge through programmatic advertising.
Programmatic platforms leverage machine learning to bid on ad space relevant to the target audience in real-time. The bid is informed by data such as interests, location, purchase history, buyer intent, and more. This enables digital marketing teams to leverage AI marketing to target the right channels at the correct time for a competitive price. Programmatic or media buying exemplifies how machine learning can increase marketing flexibility to meet customers as their needs and interests evolve.
2. Select the Right Message
Across channels, different consumers respond to different messages – some may resonate with an emotional appeal, some humor, others logic. Machine learning and AI marketing can track which messaging consumers have responded to and create a more complete user profile. From there, marketing teams can serve more customized messages to users based on their preferences.
For example, Netflix uses machine learning to understand the genres a certain user is interested in. It then customizes the artwork the user sees to match these interests. On the Netflix Tech Blog, the author explains how Netflix leverages AI tools and algorithms to determine which artwork will most entice a viewer to watch a certain title, saying:
“Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. Here, we might personalize this decision based on how much a member prefers different genres and themes. Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian."
Credit: Netflix Tech Blog
When AI marketing tools and machine learning are used, these platforms can gather valuable data on customers that allow marketing teams to increase conversion rates and improve the customer’s experience. Marketing teams can then analyze all of this data to create a more nuanced view of the customer, even considering additional factors such as if a user would have watched a title regardless of the image and how that plays into future messaging.
3. Granular Personalization
A highly granular level of personalization is expected by today’s consumers. Marketing messages should be informed by a user’s interests, purchase history, location, past brand interactions, and other data points. AI marketing helps marketing teams go beyond standard demographic data to learn about consumer preferences on a granular, individual level. This helps brands create curated experiences based on a customer’s unique tastes.
For example, Spotify uses AI marketing tools to create customized playlists based on what a customer has listened to in the past, current hits across genres, and which music is being talked about. It uses these datasets to create customized playlists for users and to create genre playlists based on artists that appear in conversations, articles, etc. This data has helped Spotify to become a top streaming service and emphasize customer experience through personalization.
Another trend based on AI-enabled personalization is atomic content. Here, AI learns customer preferences and pulls pieces from a content library to create a customized email or offer for a client featuring relevant images, videos, or articles.
4. Chatbots and Conversational Experiences
With the development of natural language processing through AI, chatbots are now being used to augment customer service agents. Customers with more basic queries can refer to chatbots, which will give immediate, accurate answers. They can leverage past questions and historical data to deliver personalized results. This gives time back to customer service agents to work on complicated requests requiring more human nuance.
5. Predictive Marketing Analytics
With so much data flowing into their organization every day, marketing teams are having a hard time actually deriving insights from it. AI marketing tools allow marketing teams to make the most of this data using predictive analytics, leveraging machine learning, algorithms, models, like B2B marketing attribution and datasets to predict future behavior. This can help digital marketing teams understand the types of products a consumer will be looking for and when – allowing them to position campaigns more accurately.
For example, Amazon uses predictive analytics to suggest products to consumers based on past purchases and behaviors, increasing conversions and customer satisfaction. AI marketing tools can also help marketing teams more accurately track marketing attribution, allowing teams to see which campaigns contributed most to ROI.
Credit: WooCommerce
6. Marketing Operations
Another key use case for AI marketing tools is to increase efficiency across various processes. AI can help to automate tactical processes such as the sorting of marketing data, answering common customer questions, and conducting security authorizations. This allows marketing teams more time to work on strategic and analytical work.
7. Dynamic Pricing
AI marketing can help make brands more competitive by enabling dynamic pricing. Generative AI and generative attribution platforms can suggest optimal prices for products in real time by evaluating huge quantities of historical and competitive data. This strategy has been especially effective in retail. It allows brands to adjust prices to reflect demand for certain products, boost sales, and edge out the competition.
What are the Generative AI Marketing Predictions and Trends?
While AI marketing tools are still largely new additions to the marketing space, they promise to only grow in popularity. There are a few generative AI trends marketers will see over the next few years and should begin to adapt to:
ChatGPT is Just the Beginning
According to Brian Burke, Research VP for Technology Innovation at Gartner, early foundation models like ChatGPT enhance creative work. However, by 2025, generative AI techniques are predicted to increase significantly. Currently, at zero, over 30% of new drugs and materials are expected to be systematically discovered using generative AI. This is just one example of the many industry applications for generative AI.
Generative AI is Growing
- According to a recent survey of Salesforce, despite the recent emergence of generative AI as a mainstream technology, 51% of the over 1,000 marketers are already utilizing it or engaging in experimental use at their workplaces.
- An additional 22% of respondents have expressed their intention to utilize generative AI in the near future. This projection suggests that nearly three-quarters of the surveyed marketers may already employ this technology.
- Additionally, 71% of marketers (7 out of 10) anticipate that generative AI will significantly eliminate mundane tasks and allow them to dedicate more time to strategic work.
Teams Will Scale Through AI
Marketing teams will be put under increased pressure to demonstrate marketing value and ROI to executive stakeholders. Teams will be able to leverage AI marketing solutions to drive these targets and better allocate funds towards successful campaigns, as well as provide the marketing metrics that demonstrate the value of campaigns.
Generative AI in Increasing Productivity, Efficiency, and Innovation
According to Gartner, generative AI has the amazing potential to automate numerous tasks and greatly enhance productivity. It also has the power to lower costs and create new opportunities for growth. It is projected that by 2026, over 80% of enterprises will utilize generative AI APIs and models or implement GenAI-powered applications in their production environments. This is a significant increase from the less than 5% seen in 2023.
Breaking Down AI Marketing
No matter how large or small your marketing team is, AI marketing tools and machine learning programs can help increase productivity, marketing ROI, and efficiency while processing those mountains of data your team might not have time to wrestle with otherwise.
Even if your first foray into AI marketing is something as small as using a machine learning program to help write your email subject lines for your next digital marketing campaign, every little step towards an AI-powered future can help keep your brand ahead of the competition and crushing revenue goals all year long.
Commonly Asked Questions About AI in Marketing
Here are some commonly asked questions about AI in marketers every marketer should know about.
Is AI Replacing Marketers?
AI is not replacing marketers. AI can automate some tasks and provide insights but still needs human input, creativity, and decision-making. Marketers are essential for crafting strategies, understanding emotions and preferences, and building connections. AI is a recommendation tool, but marketers have the expertise to know which recommendations are practical for their business. AI enhances marketers' efforts instead of replacing them.
Is AI in Marketing Only for Big Businesses?
AI in marketing is not only for big businesses. Small and medium-sized businesses can also utilize AI tools and platforms, such as generative AI and generative attribution. These tools include AI-powered chatbots for customer support and analytics platforms for data analysis and understanding customer behavior.
Is AI in Marketing Ethical?
Ethics matter in AI, particularly in marketing, where personal data and privacy are at stake. Marketers must prioritize transparency and consent when gathering and utilizing customer information. They should also be cautious of biased algorithms that could unintentionally discriminate against specific groups. Responsible and ethical AI usage fosters trust and cultivates strong customer relationships.
Can AI Completely Automate Marketing Campaigns?
AI can automate marketing campaigns, like data analysis and content creation. However, it cannot entirely replace human involvement. Marketers must still contribute creatively, strategize, and make decisions using AI's insights. AI can enhance efficiency, but human expertise and intuition are crucial for successful marketing campaigns. Humans have the ability to evaluate if the recommendations that are being activated are actually working to help businesses.
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