Artificial intelligence is often touted, or feared, for its potential to replace humans in marketing roles. However, as Harvard Business School Professor Karim Lakhani said, “AI [won’t] replace humans, but humans with AI [will] replace humans without AI.” Your human marketing team is great at connecting with your target audience, but it can be much more efficient when supercharged by AI tools and techniques.
According to Accenture, the productivity of businesses can be improved by 40% when utilizing AI. The best place to start adopting this innovative technology is to evaluate the latest trends of AI in advertising and marketing to understand what’s going on and what may be on the horizon.
#1 – Generative AI for Content Creation
By far the most profound AI trends in marketing are generative models. With GPT-3.5 and GPT-4, near-human-level chatbots and unique marketing content can be created. Stable Diffusion can generate realistic-looking images and photo edits.
Generative AI is great for many marketing tasks, such as automating the creation of social media posts, blog drafts, and email campaigns. AI tools like Jasper and Phrasee can also be used to improve human-generated content with optimizations and suggestions. Midjourney and DALLE2 are useful tools for generating images to use in marketing campaigns.
However, there are risks and challenges involved with generative AI for content creation. Fundamentally, AI isn’t human, and although great strides have been made for it to better understand us with natural language processing, it’s not quite there yet. Also, usually generative AI models are trained on general-purpose data and therefore they will not work well in cases with narrow specificity. So the outputs will still need to be refined by human marketers.
These people do not exist, the faces are generated by AI
As a result, it’s important to see AI as a tool and not a weapon of mass destruction in the market. It isn’t a replacement for human marketers, but it will enhance their capabilities. If you can come up with the right strategy that will reach your customers, it’s possible to use AI to build upon and automate parts of your content creation process to reach your audience more efficiently.
Relying on third-party generative AI models can pose a threat to information security as well. Many companies, like Apple and Samsung, have banned ChatGPT’s use by their employees for this reason, as their staff may unknowingly provide the model with confidential information that could hurt their company’s position or reputation.
Publicly available, free versions of generative AI models, such as Ghat GPT, can indeed use client-provided content to improve model performance. However, paid business versions of such solutions usually fall under security policies, allowing companies to protect their data and still benefit from generative AI. Anyway, you need to check this information before using any model.
#2 – AI in Predictive Marketing Analytics
Salesforce reports that 79% of customers expect consistency in their interactions across departments. However, 55% say that their interactions don’t feel like they’re interacting with the same company. AI can help your business process customer data more efficiently and create a big picture of their needs and, based on that, implement recommendations for different departments to interact with them.
Machine learning in marketing is great for processing large data sets and generating insights, predictions, and recommendations. This allows marketers to make data-driven decisions that catch pace with their customers’ behaviors and preferences.
AI is far more efficient at dealing with clickstream data, social media interactions, or transaction histories than humans. Artificial intelligence can process this data and extract meaningful insights from data that would be overwhelming to sort through by hand. Using this information, marketers can make predictions about future demand and user behavior, and choose appropriate messages and promotion channels accordingly.
Data readiness is a critical component of this process. AI tools are only as effective as the quality of the data they’re given. AI engineers have to thoroughly clean up the data to make it easier to work with to provide a successful analysis. In fact, data quality is often much more important than the AI model you’re using. An older AI model with better data quality will perform better than the most cutting-edge model powered by bad data.
#3 – AI Product Recommendations
If AI-driven data analytics provide information about the interests of your customers, giving them personalized product recommendations is the natural next step. Segmenting audiences is a classic strategy in marketing campaigns, but AI can take the process further.
To improve on this metric, personalizing the customer experience universally and automatically with AI may be a good strategy. Analyzing customer interactions, behaviors and interests can accumulate into effective product recommendations. Amazon sets the paradigm with its heavily personalized website. No two visits to the site are the same, especially between different customers. One customer will see a completely different set of products from another.
The next step could be to launch personalized advertising campaigns with recommended products to encourage repeat sales. Such an approach allows us to focus on the individual instead of a larger group, making it a very granular approach to segmentation. The more the system can understand the individual, the more effective it will be. Because of this, group campaigns will never be as personalized as individual ones.
#4 – Smart AI Search
In the same way that AI can generate personalized product recommendations, personalized search results are also an effective way of suggesting topics and results to your audience. Smart AI search works by:
- Understanding the context and meaning behind the question with Natural Language Processing and Understanding (NLP/U)
- Advanced indexing that can understand the content of documents in databases and on the web
- Content is categorized for greater comprehension and easier reference
- Machine learning algorithms allow the search engine to continuously improve over time to provide higher-quality results to questions
Intelligent search is being used by a number of businesses like IBM and Senseforth.ai. Senseforth utilizes this technology to improve on-site searches to provide better results for site visitors.
Voice search is another popular avenue for AI in marketing. Search engines are being revolutionized by technologies like local search optimization and personalized search experiences. Another approach is combining voice search with visual search. For example, Humane’s AI pin is able to identify objects with machine vision initiated by a natural language command. One of their examples shows a user of the pin asking, “Can I eat this?” The camera on the pin takes a picture of the object in front of them. Then, the AI system identifies the object and understands the context of the question to deliver an answer.
Of course, you don’t have to go that far with this trend. You can start with more simple options. For example, Domino’s Pizza has incorporated voice-enabled ordering functionality into its mobile app, allowing their customers to place orders with voice commands.
Another option that gained popularity in the last few years is voice-activated advertising. UK-based startup AdTonos launched YoursTruly, a solution that enables listeners to engage in real-time interaction with advertisers by utilizing voice commands. Listeners can communicate with the ads they hear through smart speakers. This empowers your target audience to engage with a brand’s call-to-action by requesting additional information about a product, initiating a direct order, or swiftly bypassing the ad altogether.
#5 – Demand Forecasting & Dynamic Pricing
One of the most powerful uses for artificial intelligence in marketing is demand forecasting and dynamic pricing. Analyzing world events, consumer interests, and other sources can allow AI systems to create strategic and competitive pricing models that are not only responsive but predictive.
By analyzing historical data, market trends, and other relevant factors, businesses can predict future demand and adjust their marketing strategy accordingly. Demand forecasting allows marketing and sales specialists to identify price elasticity and set prices that maximize revenue and profitability. Additionally, demand forecasting helps in planning effective promotional campaigns by identifying periods of high demand and aligning promotions with those periods.
However, demand forecasting is unique for every business and will depend on the types of items you carry and the businesses you serve. As a result, out-of-the-box forecasting solutions may not be as effective as custom-built AI models that can be tailored specifically to your business’s datasets. Based on our experience as AI and data science experts, three months is typically enough to get started forecasting with machine learning if the business is not subject to seasonality.
#6 – Chatbots and Virtual Assistants
At the crossroads of customer service, marketing, and AI are chatbots. Although never a true replacement for human customer service representatives who can empathize and connect more deeply with customers, advances in AI technology have made it much easier for chatbots to automate many processes. Some chatbots can entirely automate the customer journey from start to finish.
Chatbots can be built and taught to execute lead nurturing functions such as:
- Offering discounts to first-time visitors
- Suggesting newsletter subscription signups
- Providing relevant product information and suggestions
- Assisting with setting up appointments with humans
- Inviting them to join informational webinars
- Helping customers with FAQ
- Simplifying the process of lead qualification
The recent explosion of generative AI has resulted in a number of chatbot services appearing across many platforms and brands. The most notable are those on search engines like Microsoft Copilot (Bing Chat) and Google Bard. However, social media platforms are also taking part with Snapchat’s My AI and Meta AI for Instagram and Messenger. Meta’s AI has a number of different personalities to choose from to personalize user experiences.
If you haven’t yet incorporated a chatbot into your marketing strategy, now is the time. The available open source tools as well as the growing capabilities of AI will allow you to do this as quickly and cost-effectively as possible, especially since the chatbot market expects significant growth in the coming years. While the size of the chatbot market was estimated at $4.6 billion in 2022, it is expected to reach $32.4 billion by 2032.
#7 – Sentiment Analysis
Although AI doesn’t understand and empathize with people as well as we do, it can help you get the data you need to gauge how your audience feels about your brand. Sentiment analysis analyzes text to identify and categorize it as positive, negative, or neutral. This uses NLP techniques to assess the overall tone of voice, giving marketers insight into the writer’s opinions and feelings.
When applied to thousands of social media posts, AI can aggregate the sentiment of an audience quickly. This can help marketers understand whether a brand’s messaging aligns with customer perceptions and adjust their messaging if they aren’t quite hitting the mark.
However, there are some challenges to this approach. For example:
- Context is hard: AI isn’t human, and that means it can still have trouble understanding what’s really being said. It may be able to judge tone relatively well, but it won’t be very good at handling the nuance of sarcasm, irony, and some cultural references.
- Bias and fairness: AI is only as good as the data it’s trained on, and often that data is biased and can offer unfair results. Reducing bias and accounting for it with human review is essential.
To overcome these challenges, you need the involvement of experienced AI engineers. Below, you can see an example of NLP text analysis using a model developed by MobiDev engineers. Among other things, it allows you to determine the emotional tone of the text.
You can try this and other AI models on the MobiDev AI demo platform after submitting an access request here.
#8 – Combining Artificial Intelligence with Augmented Reality
One of the most important intersections in AI in marketing is with augmented reality. When focused on providing personalized and engaging content, AR and AI can work together to provide tailor-made experiences. When paired with AI technologies like product recommendations, virtual try-on solutions with AR can be even more effective. In addition, Apple’s Vision Pro Headset and the Meta Quest 3 are each offering more mixed reality experiences than ever before, each empowered with AI to provide more personal and engaging interactions.
In the near future, artificial intelligence may be able to aid in creating augmented reality experiences based on natural language commands. AI-integrated 3D development tools like Spline can already create shapes and objects in 3D environments using simple NLP commands. Eventually, that technology might evolve into a user being able to ask for unique and personalized AR experiences generated from scratch by AI.
Such an interactive experience significantly increases the attention of potential customers to the brand and can become a real competitive advantage for the business.
#9 – Marketing Routine Automation with AI
Humans are much better marketers than AI, but humans are even better at the job when enhanced by AI tools. AI can optimize their daily workflow to make marketing processes easier and more efficient. This can save time that can be better spent on personal wellness or other aspects of the business.
Some marketing tasks that AI can automate are:
- Customer research
- Customer journey mapping
- Keyword research and SEO
- Competitive analysis
- Data analysis and segmentation
Artificial intelligence in advertising can especially reduce a lot of headaches for marketers because it can help simplify the process of programmatic advertising. The data analysis capabilities of AI can help better ensure ROI with paid advertising when paired with creative marketers who understand their audience.
AI is also great at assisting with SEO optimization, especially technical SEO. Using AI tools to investigate technical issues with websites can reveal more complicated and nuanced issues that might be difficult for humans to understand without diving deep into HTML, CSS and JavaScript.
However, AI works best in synergy with the imaginations, experiences, and humanity of human marketers who can determine what works and what doesn’t.
#10 – Ensuring Data Privacy When Using AI in Marketing
Data privacy is a tricky thing to keep track of. Regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the US require businesses to look out for the freedom of privacy of their customers. Companies with AI marketing tools need to pay special attention to these regulations to ensure compliance.
Tracking cookies on websites is an important area of focus of these regulations. It can be difficult to understand during the web development process just how many cookies are being used to track customers visiting your website. However, automated tools like the Complianz WordPress plugin can make this process a lot easier. AI tools can dive deeper into understanding how tracking occurs with your services, write privacy policies, and ensure continued compliance with privacy regulations.
Industry standards are also important to pay attention to, like the Advertising Standards Authority (ADA) guidelines that govern artificial intelligence in advertising. This ensures that AI’s use in these applications is done ethically and transparently.
The Future of AI in Marketing
AI has enormous potential to enhance and augment human marketers to provide higher-quality content to their audiences and customers. In the future, markets will be more personalized than ever at an individual level, providing more engaging and unique experiences than ever before. With AI in the marketing industry set to reach a value of $107.4 billion by the end of 2028, there’s no doubt that this is the next frontier for competition, and businesses that can’t keep up will lose pace with their rivals.
However, finding a balance between privacy and personalization will be one of the greatest challenges of AI in marketing in the future. In addition, AI as an innovation is always associated with some uncertainties. Therefore, whether you are looking for innovation in your marketing department or working on a marketing startup product, it is worth cooperating with experienced AI engineers who know all the features of AI adoption.
If you’re ready to see what that future looks like and build those relationships with your customers with AI app development, contact us today.
