AI-Driven Marketing Strategies

Artificial Intelligence is reshaping personal lives and the business landscape. I recently accepted an interim advisory and mentor position with a venture capital-backed startup that utilizes AI to drive revenue growth through bespoke go-to-market strategies. Witnessing the progress in AI-driven applications and business models, from niche, customized solutions to broader applications for entire organizational functions, has been remarkable.

A recent McKinsey study, “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value”, underscores this growth. Their latest survey indicates that the embrace of comprehensive AI solutions, particularly generative AI, has seen a significant uptick between 2023 and 2024. AI adoption, which plateaued at around 50% for the last six years, has surged to 72%, while usage of generative AI has soared from 33% to 65%. Moreover, half of these companies now implement AI across two or more business areas.

Perhaps not a surprise, sales and marketing lead the way in adopting generative AI technologies, with reported usage rates doubling. Among the top applications are content development to support marketing strategies and tailoring personalized marketing campaigns. This piqued my interest about further potential applications of AI in the realm of marketing.

A useful way to start exploring the potential of AI for marketing is to become familiar with the AI technology landscape. This is constantly evolving and may not reflect all AI technologies, but according to a BDO article, “How AI Contributes to Marketing”, there are four common AI technologies that marketers should know:

• Machine Learning analyzes data and predicts outcomes based on algorithms and statistical models. This technology helps marketers create customized campaigns, segment customers, and understand buying behavior. For example, a marketer can use Machine Learning to optimize the timing, frequency, and content of their email campaigns based on the responses of their subscribers.
• Natural Language Processing emulates the way humans use language and communicate. This technology assists marketers in deriving insights into customer sentiments, emotions, and traits from various online sources like social media posts, blog entries, and product reviews. For example, a marketer can use NLP to analyze the feedback of their customers on social media and adjust their marketing strategy accordingly.
• Computer Vision analyzes visual content from images and videos to derive important data like brand logo recognition, customer emotional responses, and can produce tailored content. For example, a marketer can use Computer Vision to detect the presence of their logo in user-generated content and measure the impact of their branding efforts.
• Neural Networks utilize advanced deep learning algorithms to identify intricate patterns in human behavior, enhancing customer segmentation for automated marketing initiatives and refined forecasting. For example, a marketer can use Neural Networks to segment their customers based on their purchase history, preferences, and demographics and create personalized offers and recommendations for each segment.

As the technology progresses, AI is set to revolutionize our approach to developing marketing strategies and executing them. The question remains, in what applications or instances can AI really show its strengths in increasing marketing productivity, reducing the burden on teams, speeding up time to market, and elevating ROI? Although the potential is limited by the strength of current tools and the marketer's inventiveness, my recommendation is that these are the areas where AI stands to offer significant advantages at present:

• Market analysis : Collecting, processing, and analyzing large volumes of data from various sources to gain insights into market trends, customer behavior, and competitor strategies. AI can help marketers to identify patterns, opportunities, and threats in the market and make data-driven decisions.
• Customer segmentation: Grouping customers into different categories based on their characteristics, needs, and preferences and tailoring marketing campaigns accordingly. AI can help marketers to segment customers more accurately and effectively, and deliver personalized messages and offers that resonate with each segment.
• Product planning: Designing and testing new products or features based on customer feedback, demand, and preferences and optimizing the product development process. AI can help marketers to generate new ideas, test hypotheses, and validate assumptions, and improve the quality and usability of the products.
• Pricing strategies: Setting optimal prices for products or services based on market conditions, customer willingness to pay, and competitor pricing. AI can help marketers to dynamically adjust prices based on supply and demand, and optimize the profit margin and customer satisfaction.
• Content generation: Creating engaging and relevant content for the target audience using natural language generation and computer vision techniques. AI can help marketers to produce high-quality content that matches the tone, style, and voice of the brand and the audience, and generate visual content that captures attention and emotion.
• Personalized advertising: Delivering personalized and targeted ads to potential customers based on their online behavior, interests, and preferences and increasing conversion rates. AI can help marketers to create and display ads that are relevant, timely, and appealing to each individual customer, and optimize the ad performance and budget allocation.
• Automation: Automating various tasks and processes such as email marketing, social media management, lead generation, and customer service and saving time and resources. AI can help marketers to automate the repetitive and tedious tasks, and free up more time and energy for creative and strategic work.

Implementing an AI marketing approach does carry its share of risks and challenges. As highlighted by McKinsey, growing concerns like data inaccuracy and intellectual property violations are prominent in the use of generative AI. However, the displacement of the workforce by AI tools has lessened, reinforcing the notion that AI is a companion to human skill rather than a substitute. Actively managing the risk of data inaccuracy is increasingly prioritized over last year. Around one-fourth of surveyed respondents report negative impacts from the imprecision of generative AI. This underlines the necessity for businesses utilizing AI strategies to integrate AI risk compliance into their frameworks to guarantee adherence to regulations and responsible advancement of generative AI applications. Moreover, for applications that interact with customers, it's crucial to establish data privacy measures to safeguard any collected information in accordance with privacy legislation.?

While writing this article on AI-driven marketing strategies, my research led me to an insightful piece by Harvard Business Review, “How to Design an AI Marketing Strategy”. Despite being from 2021 and possibly outdated given the rapid progression of AI technology, the publication offers a solid framework for integrating AI into a company's marketing plans. This framework considers two key dimensions: the complexity and intelligence level necessary for the application and whether the solution operates independently or as part of an integrated system. The recommendation is that while marketing departments will likely benefit most from sophisticated, fully integrated machine learning applications, a gradual adoption beginning with simple automation could be more practical. Ideally, companies should evolve towards more intricate tasks as they become more familiar with AI, amass more customer and marketing data, and develop stronger AI skillsets.?

This seems like a solid recommendation and good starting point.? What have you experienced in using AI marketing tools in your role or deploying an AI marketing strategy in your company?

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