Artificial intelligence is transforming how marketers understand audiences, personalize experiences, optimize campaigns, and create content—but AI is only as effective as the data behind it. Success starts with a strong data foundation built on quality, integration, and security, giving organizations the accurate, connected insights needed to navigate increasingly complex buyer journeys across channels and devices. With clean, AI-ready data, marketers can uncover new audience opportunities, deliver more relevant customer experiences, scale creative testing, and make faster, smarter decisions. As consumer behavior, technology, and even organizational structures continue to evolve, the companies that pair human expertise with trusted data will be best positioned to drive meaningful engagement, improve performance, and stay ahead of the competition.
Introduction
Section I: AI enabling a more complex buyer journey
Section II: The importance of data hygiene in creating ai-ready data
Section III: The components of AI-ready data
Leveraging AI in creative: A Data Axle case study
Bonus section (multiverse, neural networks & org charts)
Conclusion
In the rapidly evolving landscape of digital marketing, the integration of proprietary modeling and artificial intelligence (AI) technology has become increasingly essential for businesses seeking to gain a competitive edge. With AI’s ability to analyze vast amounts of data and deliver actionable insights, companies can optimize their marketing strategies, enhance customer experiences, and achieve tangible results. In 2023, the global AI market was estimated at USD 196.63 billion and is projected to grow at a CAGR of 36.6% from 2024 to 2030, which means that your competitors are planning to get in the game.
However, many companies are struggling to use this powerful tool. “Many marketers are interested in introducing AI into their work processes, but they lack the data, tools, budget, and guidance to do so confidently with a proper plan in place,” said Andrew Frawley, Data Axle’s chief executive officer.
In a world where bad data costs U.S. companies about 3 trillion dollars per year, according to IBM, it’s clear that businesses need to focus on getting their data ready for the AI revolution, before they take the plunge into the great unknown. This whitepaper draws insights from experts in the industry and outlines our agency’s approach to developing innovative solutions to help marketers and advertisers find their footing. By leveraging advanced modeling techniques and AI capabilities, we empower businesses to navigate the dynamic marketplace effectively.
“Generative AI is one of the most powerful tools a marketer can have, and data takes it to new levels and builds connections that previously were unattainable. This enables clients to experiment with new creative, test it in interesting ways, and get better at delivering customers content that resonates. With marketers at the helm and AI bolstering their efforts, creative is better, faster, and more cost-effective.”
SECTION 1
Did you know that a study by Google found that 95% of customers will use more than one channel when making a purchasing decision? This preference for an omnichannel approach to marketing is relevant for both B2B and B2C marketers, with a Salesforce survey revealing that 72% of consumers and 89% of business buyers think brands should understand their unique needs and expectations across multiple channels. Juggling consumer preferences along with an omnichannel campaign is tough, but AI is here to help.
AI is fundamentally reshaping our understanding of the increasingly complex buyer journey. As consumers engage with brands across a multitude of channels and devices, AI-powered tools offer the ability to analyze vast amounts of data from these interactions, revealing nuanced behavior patterns, preferences, and decision-making processes. There are also more people involved in the buyer journey than ever before and this is especially true when it comes to B2B purchasing. In the past, marketers have assumed that buying groups are generally about 6 people. However, according to Forbes, the new reality is that marketers need to anticipate a buying committee of about 10-14 people or more.
This advanced analysis allows marketers to gain a more comprehensive view of the buyer journey, enabling them to predict future actions, anticipate needs, and personalize experiences with greater accuracy. By decoding the intricacies of customer behavior, AI empowers businesses to create more relevant and engaging interactions, ultimately driving better outcomes across the entire buyer journey. Let’s explore how.
At Alterian, our main use for AI is focused on helping the customer experience (CX) teams in businesses. These teams are working with vast amounts of data, and we need to help them wade through oceans of it in a short period of time. We don’t want these teams to get lost in the amount of data they have – the key is making your data actionable, and that’s where AI comes into play. Once brands are able to distill their data, they can pull insights from it to better understand customer journeys, friction points, and ultimately provide the experience the customer wants to see from them.
Much of the hype around AI is often looked at as customer-facing. People are talking about building better chatbots and things like that. But in reality, AI is a mix of terms and needs. Brands shouldn’t limit their use to customer-facing tools – there are a plethora of use cases that will help marketers meet their KPIs and businesses smash their goals.
One of the biggest benefits of AI comes from what it does behind the scenes to drive ROI and reduce operational and resource waste. Its ability to analyze and draw actionable insights out of large quantities of data and real-time interactions helps internal teams do their jobs better, faster, and more efficiently. This means businesses adopting customer-led practices become more effective, saving time and using fewer resources to achieve better results. Furthermore, those that leverage AI to enable their customer-led journeys benefit from its unique ability to deliver real-time individualized journeys that help customers achieve their goals more efficiently and are optimized to deliver on business objectives.
Creating exceptional customer experiences is at the heart of our agency’s approach to leveraging AI in marketing. In fact, 86% of buyers are willing to spend more money if they receive a better customer experience. We believe that AI should not only meet business goals but also enhance the overall customer journey. Our strategy focuses on three key areas:
SECTION 2
High-quality data is the backbone of effective AI models. Poor data quality can lead to inaccurate analysis, misguided strategies, and ultimately, unsuccessful marketing efforts. In fact, a recent study found that underperforming AI programs/models built using low-quality or inaccurate data cost companies up to 6% of annual revenue on average. Ensuring that data is accurate, complete, and consistent is paramount for deriving reliable insights and making informed decisions.
Data cleansing
Data cleansing involves identifying and correcting inaccuracies, duplications, and inconsistencies within the dataset. Regular data cleansing routines help maintain the integrity of the data, ensuring that AI algorithms can process it effectively to deliver precise and actionable insights.
Data integration
Integrating data from various sources is essential for creating a comprehensive view of customer behavior and interactions. Proper data integration ensures that all relevant information is available for analysis, allowing AI to uncover deeper insights and make more accurate predictions.
Data security
Protecting customer data is crucial for maintaining trust and complying with regulations. Implementing robust data security measures, such as encryption and access controls, ensures that sensitive information is safeguarded against breaches and unauthorized access. This not only protects customers but also enhances the credibility and reliability of the data used in AI models.
Part of the challenge with powering AI and ML and LLM models is that you need broad datasets that are analytic-ready. It’s not hard to have a broad dataset – there’s data all over the place. But is it in a format where you can run models against it? That’s what Snowflake Marketplace offers. Data is in an analytic format so that it’s ready to start running queries and training models.
SECTION 3
A strong and strategic data foundation is crucial for successful AI implementation. Our agency emphasizes the importance of AI-ready data, which involves:
DATA QUALITY Ensuring data accuracy, completeness, and consistency is paramount. High-quality data is the backbone of effective AI models and enables accurate analysis and insights.
DATA INTEGRATION Integrating data from various sources provides a holistic view of customer behavior and interactions. This comprehensive dataset allows AI algorithms to generate more precise and actionable insights.
DATA SECURITY Protecting customer data is a top priority. We implement robust data security measures to ensure compliance with regulations and build trust with customers.
Recent research has found that AI-driven audience targeting has demonstrated a 30% increase in ad performance and a 25% reduction in costs. AI-generated audiences leverage machine learning algorithms to identify new customer segments that may not be immediately apparent. By analyzing patterns and correlations in the data, AI uncovers hidden opportunities for targeting and engagement.
“Audience segmentation isn’t what it once was. There are nuances that have, historically, been difficult to identify. We now have the data and technology to break the barriers that used to exist and can now make more intelligent decisions based on a more holistic look at who our audiences are and what they want.”
In our creative team, the integration of generative AI into our workflow has revolutionized our performance capabilities. By leveraging the strengths of our talented content strategists and copywriters, we enhance their value through the strategic application of generative AI. This case study explores how clean data serves as the cornerstone of our AI strategy, enabling us to maintain a deep strategic core and brand consistency while driving performance through rigorous testing and audience-specific versioning.
Our approach begins with the data. We utilize the same audience and performance data that our team relies on, ensuring that our AI models are trained with the same insights and learnings. This foundational step is crucial to maintaining the sentiment and strategic depth that our team values. By embedding these elements into our custom models, we ensure that the AI outputs align with our established standards and expectations.
Maintaining brand integrity is paramount. We receive extensive brand documentation from our clients, and just as our team is trained on these standards, our AI models are similarly trained. This includes core brand pillars, tone of voice, and other critical elements. By integrating these standards into our models, we guarantee that our AI-generated content remains true to the brand’s voice and is consistent across all channels.
Our commitment to aligning content with different audience segments is central to our strategy. Generative AI aids in defining these audiences and understanding how to effectively communicate with them. By creating nuanced content that resonates with specific segments, we enhance our ability to drive engagement and conversion. This alignment is based on the detailed audience data we extract, ensuring that each piece of content is tailored to meet the unique needs and preferences of our target demographics.
Testing is a vital component of driving performance, and generative AI allows us to scale this process significantly. With the capability to generate numerous test variants rapidly, we can continuously keep new variants in the market. This constant cycle of testing, learning, and iterating feeds valuable insights back into both our team and our AI tools, fostering an environment of continuous improvement and optimization.
The “Marketing Multiverse” is an important new concept in the industry. The proliferation of remote work technology has led to the lines between professional and personal lives, as well as physical and digital identities, becoming increasingly blurred. This evolving landscape, often referred to as the “marketing multiverse,” presents both challenges and opportunities for marketers, particularly in B2B and B2C spaces.
Throughout the day, individuals effortlessly transition between their professional and personal personas, often within the same hour. A B2B decision-maker might start their morning reviewing industry reports on LinkedIn, only to switch to browsing consumer products on Instagram during a lunch break. In the same vein, physical and digital channels are seamlessly integrated—attending a virtual meeting while walking through a brick-and-mortar store or engaging in online networking during a conference.
This fluidity demands a new approach to marketing. For advertisers, it’s no longer enough to separate strategies into rigid B2B or B2C silos. Instead, the focus should be on understanding the context in which the audience is operating at any given moment and delivering personalized content that resonates across their various personas and channels. AI and data analytics play a crucial role here, helping to track these transitions and tailor messages that align with the audience’s current mindset and environment.
As the marketing multiverse continues to expand, businesses must embrace this blend of identities and channels. Success lies in creating cohesive, omnichannel experiences that recognize the dynamic nature of today’s consumers—whether they are engaging with your brand in a professional capacity, a personal one, or somewhere in between. By doing so, brands can forge deeper connections, enhance engagement, and drive more meaningful outcomes.
Let’s talk about another disruption in the way we work. This concept suggests a shift in organizational structure and management, driven by the adoption of advanced AI and machine learning technologies, specifically neural networks. Traditional org charts represent a hierarchical structure that can be slow to adapt and limited in its ability to foster cross-functional collaboration. In the rapidly evolving marketing metaverse, such rigidity is a liability. The shift toward AI and data-driven strategies necessitates a more flexible and interconnected organizational model.
Traditional organizational charts (org charts):
Emergence of neural nets in organizations:
In marketing
The integration of metaverse technologies will likely revolutionize how brands interact with consumers, making experiences more immersive and personalized. Marketers need to stay updated with these technologies to effectively leverage their potential.
In organizational structures
The shift toward neural network-inspired structures suggests a move toward more agile, responsive, and innovation-driven organizations. Leaders will need to embrace flexibility, foster a culture of continuous learning, and leverage AI to stay competitive.
In conclusion, building a strong and strategic data foundation is essential for leveraging AI-powered advertising and marketing. By integrating proprietary modeling and AI technology, businesses can gain a competitive edge, optimize their marketing strategies, and deliver exceptional customer experiences. Our agency’s approach, grounded in insights from recent Forrester presentations, emphasizes the importance of AI in reshaping the buyer journey, enhancing customer experiences, ensuring data readiness, targeting digital audiences, and driving creative innovation. Embrace AI to unlock new opportunities and achieve tangible results in today’s dynamic marketplace.