How artificial intelligence is transforming marketing

Of all the 21st-century technological advances, Artificial Intelligence (AI) is the latest, most impactful disruptor. It holds immense potential for manufacturing, pharmaceuticals, healthcare, agriculture, logistics, and digital marketing. In fact, many technology experts believe it is ushering in a Fourth Industrial Revolution. Other emerging disruptive business technologies often integrate with AI: including the Internet of Things, blockchain, and big data analytics.

Speculation about the long-term future of artificial Intelligence in society is at a fever pitch, receiving unprecedented attention in the news and on social media. Luminaries such as Elon Musk and the late Stephen Hawking have expressed deep concern about AI’s exponential growth with the possibility that it will eventually become self-aware. They believe that if AI development is left unchecked, it could easily surpass human control within decades from now and create its own agenda one perhaps antithetical to the value of human life. That said, we are now witnessing fierce, heavily invested competition between Google and Microsoft to advance artificial intelligence without apparent concern for its long-term dangers.

AI has already revolutionized marketing.

However, this article is about how AI is currently impacting marketing, not some hypothetical future. AI has already had a transformative effect on marketing, with the most successful companies now using it strategically to expand their customer base. And it has been helping leading-edge organizations capture integrated data about customers and products across all channels These data are then used to better understand end-customer experience, providing visibility across all functional areas. Most organizations have adopted some of its features, often without understanding that Google Analytics and other common, readily available internet AI-based tools are already at work ‘under the hood’ to help expand their customer outreach.
“Studies have shown that an astonishing 91.5% of leading businesses invest in artificial intelligence regularly. Another forecast made by Gartner predicted that customer satisfaction will increase by 25% with the help of artificial intelligence by 2023 in the majority of organizations that choose to adopt it. (1)

Machine Learning

AI’s primary subcomponent, Machine Learning (ML), has played a crucial role in big data analytics (which anticipates and provides guided experiences to meet customer expectations). Leveraging AI and predictive analytics (2) is the key to offering customer experiences that build positive brand recognition and secure customers for life.

ML includes machines that have the potential defining AI capability of communicating like human beings. In the longer term ML aims to give machines increasing ability to learn a task without pre-existing code.
AI in 2023 most typically still refers to software that helps us to perform one particular job such as identifying where to place advertising to maximize efficiency or how to personalize an email to increase the likelihood of receiving a response The most frequently used search engine advertising solutions include email marketing platforms, e-commerce solutions, and tools designed to assist with content creation Again, they all provide functionality in performing repeated work, but most cannot yet ‘think’ outside their code and in that regard are only precursors to real AI. (3)

Nonetheless, present uses have proven invaluable, Research shows organizations that have a lot of data and a need for sophisticated personalization–and intelligent automation of their operations–have been doing this basic kind of AI for the last decade. This encompasses healthcare and financial services – though doing it within the operations of their business, not within marketing and sales.

Machine learning continually improves all aspects of marketing

By contrast, CMOs and their teams rely on AI and machine learning to iteratively test and improve their marketing campaigns and strategies. This requires a new level of data quality which isn’t possible using conventional platforms The most popular use of AI and machine learning in organizations is delivering personalization at scale across all digital channels. There’s also increasing adoption of predictive analytics based on machine learning to fine-tune models to improve up-sell and cross-sell results. (4)
There is such a vast amount of data that platforms like Google and Amazon handle that is impossible to analyze by humans. Also, an artificially intelligent system stores multiple information about large numbers of people captured within multiple machines from multiple sources. All of this appears in the system asynchronously or simultaneously.

AI-enabled systems perceive the environment and take action, accordingly, remembering the situations that may come up again. With the help of historical data, AI can predict future data trends, alerting marketers to proactively take action. (5)

According to a recent study (6), 87% of companies that have adopted AI were using it to improve email marketing. 61% of marketers were also planning to use artificial intelligence in sales forecasting.
Using AI tools like robotic analytics, CRM, and social data, a marketer can quickly boost the ROI of marketing initiatives. Thanks to the increasing amount of data gathered from customers, businesses’ marketing strategies have become more data-driven. (7)

Task automation is foundational to AI

Task automation: These applications perform repetitive, structured tasks that require relatively low levels of intelligence. They’re designed to follow a set of rules or execute a predetermined sequence of operations based on a given input, However, they can’t handle complex problems such as nuanced customer requests.

Machine learning: ML employs algorithms that learn how to use large quantities of data to make relatively complex predictions and decisions. Such models can recognize images, decipher text, segment customers, and anticipate how customers will respond to various initiatives. Machine learning already drives programmatic buying in online advertising, e-commerce recommendation engines, and sales direction models in customer relationship management (CRM) systems. It and its more sophisticated variant, deep learning, are the most exciting technologies in AI. That said, it’s important to clarify that most existing ML applications still just perform narrow tasks and need to be ‘trained’ using voluminous amounts of data.

Other AI differentiators

Stand-alone applications: Stand-alone applications are best understood as clearly demarcated, or isolated, AI programs. They’re separate from the primary channels through which customers learn about, buy, or get support for using a company’s offerings- -or the channels employees use to market, sell, or service those offerings.
One example is the color-discovery app created by Behr, the paint company. Using IBM Watson’s natural language processing and Tone Analyzer capabilities, the application delivers several personalized Behr paint-color recommendations that are based on the mood consumers desire for their space. Customers use the app to short-list two or three colors for the room they intend to paint. (8)

Integrated applications: Embedded within existing systems, Integrated AI applications are often less visible than stand-alone ones to the customers, marketers, and salespeople who use them. For example, machine learning that makes split-second decisions about which digital ads to offer users is built into platforms that handle the entire process of buying and placing ads. Data scientists and programmers are now formulating general-purpose learning algorithms that help machines learn more than just one specific task.(9)
Developers of Customer Relations Management (CRM) systems increasingly build machine-learning capabilities into their products. AI may present recommendations to a person faced with a choice—for example, suggesting a movie to a consumer or a minor project modification to a marketing executive. Human decision-making, by contrast, is typically reserved for the most consequential decisions such as whether to continue a campaign or approve an expensive TV ad. Clearly, firms benefit from moving to more-automated decisions whenever possible.

Challenges and risks: Implementing even the simplest AI applications can present challenges. Stand-alone task automation AI, despite its relative lack of technical sophistication, can still be difficult to configure for specific workflows. It requires companies to proactively acquire applicable AI skills. Bringing any kind of AI into a workflow demands careful integration of human and machine tasks so that the AI augments human skills seamlessly. For example, while many organizations use rule-based chatbots to automate customer service, less-capable bots can irritate customers. So, it may be better to have such bots assist human agents or advisers rather than interact with customers directly.

AI helps analyze current market trends

Digital marketers can adopt AI technology to find the most attractive business growth opportunities for brand expansion. Innovative AI tools can scan a vast quantity of data to derive and analyze current market trends. This helps marketers to create strategies that help to uncover new ideas for innovative marketing campaigns.
While most marketers are increasingly comfortable regularly using AI tools, they’re often executed in an ad-hoc manner. Many marketing departments still lack a coordinated, strategy-focused approach to implementing major projects. And many are lagging when it comes to fostering an AI-friendly, data-first culture as well. This includes developing competencies and requisite skills. (10)

AI and ad placement

Facebook and Google are the biggest online advertising platforms. They both offer AI tools that work by combining audience segmentation with predictive analytics. Segmentation splits customers into groups according to demographic characteristics – gender, age, income level, interests–and an infinity of other potential variables. In short, predictive analytics calculates which groups a particular product or service is most likely to appeal to. (11)

Facebook, Google, and all of the other platforms that offer advertising functions allow businesses to target thousands of potential customers with multiple versions of advertising materials. This is critical in measuring and assessing their relative effectiveness with different demographic groups. AI-driven advertising tools are most effective when used as part of a coordinated AI marketing strategy By comparison, with traditional methods of advertising such as television, newspapers, and magazines, it’s very difficult to attribute sales growth to any specific advertising content, or venue,

Content Marketing

“Content is king” has been accepted wisdom in marketing departments since the twentieth century, the dawn of Web 2.0, and the rise of user-generated content platforms, especially social media. AI content marketing is a term that describes the inclusion of AI and machine learning programs into content tools. “Content marketing tools can help with content planning, creation, distribution, analysis, and reporting. AI helps improve the quality of content marketing.(12)

Email marketing

A large number of AI-powered tools help with email marketing, “AI-based email marketing is a form of machine learning. In an email program, It’s the set of rules and processes that allows programs to analyze hundreds or thousands of inboxes to create better, more personalized content for subscribers.(13)

Challenges of AI marketing

The marketing manager’s challenges in implementing AI technology for their marketing campaigns are not to be underestimated. While the potential rewards of AI marketing are revolutionary, there are real obstacles to overcome. From securing buy-in from skeptical executives and team members to maintaining brand quality amidst the allure of generative AI, marketing leaders must navigate a range of concerns. Additionally, the lack of AI skills among employees poses a significant hurdle, requiring training and education to fully leverage the power of automated campaigns and content creation. Furthermore, ensuring compliance with privacy regulations and effectively prioritizing AI initiatives and solutions add further complexity to the marketing manager’s role. Despite these challenges, with a strategic approach and a solid case for implementation, the transformative potential of AI marketing can be realized.

While the potential rewards of AI marketing are revolutionary, there are real challenges. If you’re planning on adding additional AI software to your marketing, be aware of these common pitfalls:

• Securing buy-In: Some executives, managers, and team members will be all in on AI. Others, not so much. Depending on your industry, you might have to fight an uphill battle just to gain acceptance for a new AI solution or feature. So, make sure you come prepared with a solid case for implementing new marketing AI.
• Maintaining brand quality: With everyone excited about the seemingly magical potential of generative AI, (self-learning) content creators neglect off-brand elements, like outlier designs. As you begin to include generative AI in your marketing, make sure the AI you’re using isn’t negatively impacting your brand.
• Lack of employee AI skills: AI offers great potential to increase productivity, but you’ll need marketers with AI skills to set up your automated campaigns, as well as to edit and implement content. For marketing leaders, a lack of training and education was cited as the top challenge to adopting AI.
• Privacy and data utilization: Browsers, phone manufacturers, and governments are cracking down on the use of third-party data, which means your AI will have to comply with all data regulations in the regions you operate in.
  Prioritizing AI initiatives and solutions: Only 29% of marketing leaders feel confident in their ability to evaluate AI marketing technology. This deficit is a major barrier to AI adoption. To stay focused, prioritize the biggest problems your marketing department and customers face before incorporating AI enhancements.(14)

The near future of AI marketing

Most marketing departments outside of large companies won’t have dedicated, specialist data scientists. As a company goes through the ongoing process of developing a data-and-AI-literate culture, it must enable people who are already experts in their particular field to gain training in AI skills.
The rise of Generative (learning) AI within software platforms
Many commonly used marketing tools are adding generative (learning) AI capabilities. For example, the content collaboration platform StoryChief lets you use generative AI to write content briefs. And the SEO optimization platform Frase offers AI for drafting blogs. Expect to see hundreds of different marketing platforms adding AI into the mix. It will be up to you and your marketing team to determine which features are worth your time. (15)

We can expect to see research AI get much more sophisticated. AI tools make researching and strategizing much easier and faster. So, we can expect to see more AI tools dedicated to customer research and marketing strategy.

Emerging roles and responsibilities

In the near future, new AI-related marketing team roles will emerge. For example, there might be new positions for AI Marketer, AI Marketing Manager, AI Content Editor, etc.
Some AI marketing tasks don’t require a whole new hire, but there will be an augmenting of team marketing competencies to include specific AI-related skills.
Thirty-two percent of marketers and agency professionals were using AI to create ads, including digital banners, social media posts, and digital out-of-home ads, according to a recent study by Advertiser Perceptions. (16)

• AI and Customer Data Platforms are being combined to drive greater personalization at scale.
• High-performing marketing teams were 25% more likely to increase their use of AI in 2022.
• Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy.
• ML algorithms can correlate location-specific sentiment for a given product or brand and a given product’s regional availability. Having this insight alone can save the retail industry up to $50B a year in obsolete inventory.
• Minimizing inventory risk and fine-tuning product demand forecasts are ideal use cases for AI source: ai can help retailers understand the consumer, phys.org. January 14, 2019.
• Forty-one percent of marketers say that AI and machine learning make their greatest contributions to accelerating revenue growth and improving performance. Marketers say that getting more actionable insights from marketing data (40%) and creating personalized consumer experiences at scale (38%) round out the top three uses today. The study also found that most marketers, 77%, have less than a quarter of all marketing tasks intelligently automated and 18% say they haven’t intelligently automated any tasks at all. Source: Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.

Use of artificial intelligence in pricing management

Pricing involves factoring in multiple aspects in the finalization of price. Real-time price variation based on fluctuating demand adds to the complexity of the pricing task. Artificial intelligence-based multiarmed bandit algorithms can dynamically adjust prices in real-time scenarios (Misra et al., 2019). In a frequently changing pricing scenario like an e-commerce portal, machine learning algorithms can quickly adjust the price points to match the competitor’s price (Bauer & Jannach, 2018).

Use of artificial intelligence in place management

Product access and product availability are essential components of a marketing mix for heightened customer satisfaction. Product distribution relies on networked relationships, logistics, inventory management, warehousing, and transportation problems–which are largely mechanical and repetitive in nature. Artificial intelligence is the ideal solution in the case of place management by offering robots for packaging, drones for delivery, and IoT for order tracking and order refilling (Huang & Rust, 2020). Standardization and mechanization of the distribution process add convenience to both suppliers and customers. Besides utility in distribution management, AI also offers customer engagement opportunities in a service context.

Use of artificial intelligence in the marketing company

The best marketing agencies like OWDT (marketing and web design Houston company)  are constantly updating their strategies to incorporate artificial intelligence (AI) as it has the potential to transform the industry. AI can analyze vast amounts of consumer data to provide insights into customer behavior and preferences, allowing marketers to personalize their messaging and offerings. It can also automate tasks such as ad targeting and optimization, freeing up marketers to focus on more strategic initiatives. Additionally, AI-powered chatbots and virtual assistants can improve customer service and engagement. As AI technology continues to advance, marketing agencies that embrace it will have a significant competitive advantage in delivering effective and efficient campaigns that drive results.

There are, of course, challenges in adapting to AI marketing. But it’s important to understand and act upon the many advantages that it provides.

Summary of AI-related marketing improvements

• AI accelerates and improves business and customer interaction.
• AI improves ROI.
• AI advances revenue growth and improves performance.
• AI generates more actionable insights from marketing data.
• AI creates personalized customer experience to scale.
• AI reduces the time spent on repetitive tasks.
• AI drives down costs and improves efficiency.
• AI provides personalized content to users based on their search history
• AI and predicts customer needs and behaviors with greater accuracy.
• AI shortens the sales cycle.
• AI increases operational excellence and optimizes the user experience.
• AI offers continuous customer services with AI chatbots and Live Chat.
• AI helps brands cut costs and increase revenues.
• AI targets the right audience

Sources

[1] contentbot.ai/blog/news/is-ai-the-future-of-ecommerce-heres-what-we-know.

[2] Predictive Analytics - an overview | ScienceDirect Topics

[3] forbes.com/sites/bernardmarr/2022/09/09/artificial-intelligence-and-the-future-of-marketing

[4] snowflake.com/guides/prediction-analytics-or-predictive-analytics

[5] research.aimultiple.com/ai-stats/#marketing

[6] https://research.aimultiple.com/ai-stats/#marketing

[7] /venturebeat.com/ai/why-ai-is-the-differentiator-in-todays-experience-market/

[8] researchgate.net/publication/349635531_Enhancing_Marketing_Strategies_and_Analytics_Through_Artificial_Intelligence

[9] analyticsinsight.net/market-trend-analysis-of-artificial-intelligence-at-a-glance

[10] marketingaiinstitute.com/blog/ai-in-advertising

[11] rockcontent.com/blog/ai-content-marketing

[12] googleadservices.com

[13] imeanmarketing.com/blog/ai-marketing-statistics

[14] storychief.io/blog/ai-content-marketing-storychief-vs-chatgpt

[15] advertiserperceptions.com

[16] businessoffashion.com/opinions/retail/the-cost-of-dead-inventory-retails-dirty-little-secret/