Artificial intelligence (AI) is transforming the marketing industry, and companies that leverage AI in their marketing strategies can gain a significant competitive advantage. In this blog post, we will discuss how to design an AI marketing strategy, including its benefits, challenges, and best practices.

Benefits of AI Marketing

Artificial intelligence (AI) is revolutionizing the marketing industry, strengthening its key activities, like comprehending customer requirements, aligning them with the right products and services, and convincing individuals to make purchases(1). It does this by leveraging sophisticated algorithms and data analysis tools that give companies an unprecedented understanding of their customers and their needs.

AI has the capacity to compile and process vast amounts of data in real-time, enabling marketers to gain a deeper understanding of customer behavior. It can map out customer preferences, identify trends, and predict future behaviors with impressive accuracy. This level of understanding can help businesses tailor their offerings to meet specific customer needs and wants, resulting in higher customer satisfaction and increased sales(2).

Not only does AI help in understanding the customers, but it also learns from customer behavior. The more data AI has access to, the more it can learn and the better it can predict customer behavior. This adaptive learning and analysis of data are what makes AI so powerful. It provides insights that help businesses achieve their goals, from improving product designs to formulating more targeted marketing campaigns(2).

Moreover, AI can significantly influence the creation of more robust and effective marketing strategies. It allows for data-driven decision-making, a methodology that results in more precise and effective strategies. AI can crunch big data sets and generate insights that human analysts might overlook, thereby enabling marketers to create more precise and personalized strategies that resonate better with their target audiences.

In addition to enhancing strategy formation, AI is also revolutionizing the customer journey. It offers tools that can identify key touchpoints, predict customer actions, and even automate responses. This can lead to a more streamlined and personalized customer journey, thereby enhancing customer experience and building stronger customer relationships(3).

AI is also changing how businesses attract, engage, and convert customers. AI-powered tools like chatbots, personalization algorithms, and predictive analytics can help businesses connect with their customers on a more personal level, nurture these relationships, and ultimately convert them into loyal customers(3).

By combining human creativity with AI's analytical power, businesses can maximize their marketing potential, provide an enhanced customer experience, and ensure their longevity in a competitive marketplace. Thus, the integration of AI into marketing strategies isn't merely an added advantage; it's becoming a necessity for businesses looking to thrive in the digital era.

Challenges of AI Marketing

AI marketing, while offering numerous advantages, also brings with it a series of complex issues. One of the most prominent hurdles is the question of content quality. Content generated through AI can sometimes fall short in comparison to content crafted by humans, specifically in areas of creativity, subtlety, and emotional intelligence.

Humans, with their inherent creativity and ability to think out of the box, can formulate content that’s not only unique but also engaging, nuanced, and tailored to resonate with a particular audience. They can inject humor, elicit emotions, and create a narrative arc that can be quite challenging for AI to replicate. AI, on the other hand, while extremely efficient, is limited to what it has been trained on and may often churn out content that lacks originality or feels formulaic, reducing the chance of audience engagement(2).

Additionally, AI content can sometimes lack the delicate touch or sensitivity to cultural nuances that a human writer might possess. Humans have the ability to perceive and understand the nuances of different languages, dialects, and cultures, which can be quite tricky for AI to replicate. Moreover, humans can use this understanding to adapt their writing style to a specific context or audience, something AI is not particularly adept at yet.

The second significant challenge with AI marketing revolves around the need for high-quality data. The efficiency of AI algorithms depends heavily on the quality and quantity of the data fed into them. They require vast amounts of data to learn, adapt, and enhance their performance. However, obtaining such large volumes of accurate, relevant, and unbiased data can be a tough task(4).

If the data used to train the AI is incomplete or inaccurate, it can lead to the generation of unreliable results. Furthermore, any bias inherent in the data can be amplified by AI, leading to skewed or discriminatory outcomes. This can pose a serious issue, especially when AI is used for critical decisions like customer segmentation or personalized marketing.

The task of managing and processing these massive volumes of data can be incredibly resource-intensive. It requires a significant investment in data infrastructure, as well as skilled data scientists and engineers to clean, manage, and analyze the data. This need for specialized skills and resources can pose a significant barrier for many businesses, particularly small and medium-sized enterprises.

Despite these challenges, the potential of AI in marketing is undeniable. However, businesses need to tread with caution, ensuring that they balance the efficiency and scale offered by AI with the creativity, emotional intelligence, and cultural sensitivity offered by humans. They must also invest in obtaining high-quality, unbiased data and the necessary infrastructure and skills to leverage it effectively.

Best Practices for AI Marketing

To design an effective AI marketing strategy, companies should follow these best practices:

1. Establish Goals: The first crucial step in designing an AI-enabled marketing strategy is to define clear, quantifiable objectives. These goals could range from increasing website traffic or enhancing lead generation to amplifying sales. By establishing these objectives upfront, you can focus your efforts and resources more effectively, and ensure that your AI tools are tuned to drive these specific outcomes. For instance, if your aim is to boost web traffic, your strategy might involve AI-powered SEO optimization or content generation tools. In terms of SEO, incorporating relevant keywords and phrases can improve your website's visibility, thus helping potential customers find you more easily.

2. Collect High-Quality Data: The success of your AI marketing strategy hinges on the quality of your data. You need to ensure that your data is not only complete and accurate, but also unbiased. Garnering data from a variety of sources can provide you with a more holistic view of your customers. This could involve anything from website analytics and social media engagement metrics to customer feedback and buying patterns(1). For SEO, remember that relevant, regularly updated, and reliable data can help your site rank higher in search engine results, thus driving more organic traffic to your site.(1).

3. Choose the Right AI Tools: The choice of AI tools should be closely aligned with your set goals and available data. There's an array of AI marketing tools at your disposal - from natural language processing and machine learning to sentiment analysis. For instance, if you aim to enhance customer service, an AI chatbot could be a great choice. If understanding customer sentiment is a priority, then sentiment analysis tools would be a smart investment. When writing your content, consider how these tools can enhance your website's SEO. For instance, natural language processing can help identify relevant keywords to include in your content.

4. Integrate AI with Human Expertise: A winning AI marketing strategy doesn't aim to replace human expertise but rather augment it. While AI can analyze vast amounts of data and identify trends, humans provide the creativity, empathy, and strategic thinking that AI currently cannot replicate. For example, while AI can generate content based on keywords for SEO optimization, a human can add the storytelling aspect, emotional appeal, and creative twists that make the content truly engaging.(1).

5. Monitor and Evaluate Performance: To ensure the ongoing effectiveness of your AI marketing strategy, continuous monitoring and evaluation are vital. Make use of relevant metrics such as click-through rates, conversion rates, and customer satisfaction to measure the success of your strategy(6). These metrics can also inform your SEO strategy - high click-through rates often correlate with higher rankings in search engine results. Periodic evaluation allows for fine-tuning of your strategy, enabling you to keep up with evolving customer preferences, market trends, and technological advancements.

Adopting a Phased Approach 

We hold the conviction that the most substantial benefits will be reaped by marketers who take on comprehensive machine-learning applications, though elementary rule-based and task-automation systems can elevate highly regulated processes, with promising prospects for business returns. Yet, it's essential to note that task automation is increasingly blended with machine learning— extracting pivotal data from interactions, making more intricate decisions, and tailoring communications—a blend that spans quadrants. Independently operating applications remain valid where integration poses challenges or is unfeasible, but their benefits do have boundaries. Hence, we encourage marketers to gradually shift towards incorporating AI into their existing marketing systems as opposed to sticking with independent applications. This inclination is also supported by many companies; in Deloitte's 2020 survey, 74% of global AI executives concurred that "AI will be assimilated into all enterprise applications within three years."

The Initiation Process 

For enterprises with minimal exposure to AI, a suitable starting point is the development or acquisition of simple rule-based applications. Many firms adopt a "start-small-grow-big" tactic, initiating with a stand-alone non-customer-facing task-automation application, like one that assists human customer service agents. 

Once businesses develop foundational AI competencies and amass a wealth of customer and market data, they can initiate a shift from task automation towards machine learning. Stitch Fix's clothing-selection AI serves as a prime example. This tool aids its stylists in customizing offers for customers based on their self-declared style preferences, their retention and return of items, and their feedback. The data models grew even more potent when customers were asked to select from Style Shuffle images, thereby creating a rich source of fresh data. 

Marketers should continually seek out new data sources—including internal transactions, external vendors, and even prospective acquisitions—since most AI applications, especially machine learning, require enormous quantities of top-tier data. Take the example of charter jet company XO, which utilized a machine-learning-based pricing model to boost its EBITDA by 5%. The critical element was accessing external data sources about the availability of private jets and demand-influencing factors like significant events, macroeconomic factors, seasonal trends, and weather conditions. The data used by XO is open to the public, but seeking proprietary data whenever feasible is recommended, as models based on public data could be replicated by rivals.

As firms grow more proficient in applying AI to marketing, many completely automate certain decision-making types, entirely excluding human involvement. This method is vital for repetitive, high-frequency decisions like those needed for programmatic ad purchasing. In other areas, AI might simply offer suggestions to a person making a decision—such as recommending a film to a consumer or a strategy to a marketing executive. Human decision-making is typically saved for the most significant issues, like deciding whether to persist with a campaign or approve a high-cost TV advertisement.

Enterprises should strive for greater automation in decision-making whenever feasible. We firmly believe that this is where the most substantial gains from marketing AI will be seen.

Sources:

1. Harvard Business Review

2. HubSpot Blog

3. Social Media Strategies Summit

4. Sprout Social

5. Influencer Marketing Hub

6. Marketing Evolution