Marketing in AI Age: How Artificial Intelligence Influencing the Behaviour of People the Way They Think, Act, and Decide?
Powerful social marketing platforms, like Sprout, weave together sophisticated AI technologies under the hood to provide the insights you need to succeed. Capabilities such as semantic classification, named entity recognition and aspect based sentiment analysis help you get targeted insights specific to your industry, while natural language processing helps you optimize social content and improve customer engagement—all leading to greater competitive advantage and share of voice.
Let’s get to know these technologies better.
1. Machine learning
Machine learning (ML) uses statistical methods to analyze social data for high-precision insights around customer experience, audience sentiment and other marketing drivers. Once trained, ML models automatically complete text mining, topic extraction, aspect classification, semantic clustering and other tasks to provide results in seconds.
AI-ML models get smarter as they process more data over time and so upgrade automatically, which is perfect for scaling your business operations while minimizing future investment in your tech stack.
2. Natural language processing (NLP)
Natural language processing powers your AI marketing tool so it can semantically and contextually understand social listening data. It combines rules-based lexical and statistical methods, enabling you to scan a wide range of posts, messages, reviews or comments and extract critical information from it.
When NLP algorithms are coded for social listening, they can interpret the data even if it's splattered with colloquialisms, code switches, emojis, abbreviations, hashtags or spelling mistakes. Natural language generation (NLG) further enhances the tool’s capabilities to help you create high-performing copy for posts, customer responses and more.
This gives you access to a wider audience for outreach campaigns, stronger communication with existing customers and better returns on our investment in social.
3. Semantic search
Semantic search algorithms are critical in NLP because they help understand the intent of a phrase or lexical string without depending on keywords. These algorithms extract relevant keywords and categorize them into semantic clusters. This eliminates chances of duplicates in text mining, especially where sentiment analysis is concerned, for an accurate measure of customer experience or brand performance.
Knowing exactly how strong your brand is in relation to your competitors and monitoring it against your benchmarks can help you alter marketing and sales strategies to achieve long-term business goals.
4. Named entity recognition (NER) and neural networks
NER helps an AI platform identify named entities in big data. These entities could be important people, places or things such as CEOs, celebrities, locations, currencies, businesses and others. It can identify these entities even if they are misspelled. NER also is a key function in generating knowledge graphs because they establish a relationship between entities in order to derive context and insights from data.
Neural network (NN) algorithms, built to mimic how a human brain handles information, remember these interconnected data points and keep adding them to their knowledge database. This is what enables ML models to provide more precise results with time through deep learning.
Thus, you get to know why certain brands keep appearing in your social listening data, what new market trends are brewing, which influencers would be a great fit and many other insights that can help you strengthen your social marketing strategy.
5. Sentiment analysis
Sentiment analysis is the process of measuring customer sentiment from feedback data and can be instrumental in helping with reputation management. Sentiment analysis algorithms analyze social listening data including survey responses, online reviews and incoming messages, both in real-time and historically. They measure sentiment in every aspect that is extracted from the data and assign polarity scores in the range of -1 to +1. Neutral statements are counted as zero.
When analyzing social data where customers are talking about aspects of a business, sentiment analysis models consider the polarity score of each aspect. The scores are aggregated to provide an overall sentiment of the brand in terms of customer experience. This eventually gives you an idea of how well your business is performing.
With such insights available, you can grow your brand by evaluating and improvising social media content, shaping sales and marketing, improving brand management, better interpreting customer intent and so much more.
Our role is this phase is not very different from before, except that we can get certain work done faster and easier:
- Experiments and first steps of implementation: People are responsible for starting to discover and initiate experiments with AI tools and implementing basic AI features, such as automated email marketing or social media management.
- Evaluation and feedback: Evaluate benefits and results of AI-driven tasks and provide feedback to improve the accuracy and relevance of AI results.
- Individual training and development: Learning the basics of AI technology and developing a mindset open to technological change and innovation. Starting to develop an AI-first mindset.
Phase 2: Deepening
In this phase, the ad hoc individual use of the "AI explorers" shifts to using AI more intensively and consistently as a team. This includes sharing knowledge and collaborating on the best AI solutions for the team.
Marketers are beginning to automate certain routine tasks. While this is not yet a full application of AI, it is an important step in that direction. For example, tools introduced at this stage can help plan social media posts, automate email campaigns or generate simple reports. This reduces manual effort and increases efficiency, but human involvement is still needed for strategic decision-making and more complex tasks. It also gives the marketer more time to focus on strategic thinking and creative work.
What does our role look like:
- Team-functional collaboration: Active collaboration within the marketing team to perform more and more work with AI.
- Strategic decision-making: Based on AI-driven insights, they make decisions about marketing strategies and customer segmentation.
- Data collection and analysis: Employees work together to collect and analyze data, using AI tools to gain insights.
- Team-oriented training and development: all team members will learn the basics of AI technology and develop an AI-first mindset that is open to technological change and innovation.
- There is an ongoing need for training and adaptation to new AI-driven processes and tools.
Phase 3: Integrate
The third phase marks a significant shift where the entire organization becomes involved in integrating AI. Machine learning, a core aspect of AI, enables systems to learn independently from data and experiences. This increases AI's ability to assist in more complex decision-making processes.
This phase requires close collaboration between different departments to ensure that AI systems are well integrated into the organization.
What does our role look like:
- Cross-functional collaboration: All departments will work together to best integrate AI into the overall marketing and organizational strategy.
- Introduction of an AI steering committee: leaders from the various departments form an AI steering committee together to determine the direction of the organization with each other.
- System integration and management: Employees are responsible for integrating AI systems across the organization and managing the interaction between AI and human tasks.
- Advanced Analytics and Application: Employees will understand and apply advanced AI analytics to more complex marketing challenges.
- Organization-wide training and development: all colleagues will learn the basics of AI technology and develop an AI-first mindset that is open to technological change and innovation.
- There is an ongoing need for training and adaptation to new AI-driven processes and tools.
Stage 4: Autonomous
phase four, systems are becoming increasingly autonomous. LLMs can now take on complex tasks such as automating content creation, promotion, customer service via chatbots or generating personalized marketing campaigns. This phase emphasizes accountability for both processes and results, with AI helping to drive marketing initiatives based on real-time data and feedback.The autonomy achieved in this phase allows marketers to focus on the most strategic aspects of their work, while AI ensures operational efficiency and effectiveness.
What does our role look like:
- Supervision and control: Employees oversee autonomous AI systems, maintaining control over key decision-making processes.
- Creative and strategic input: Human creativity and strategic thinking remain essential, especially in developing marketing strategies that complement and reinforce AI recommendations.
- Innovation and Development: Driving innovation and developing new ways to leverage AI functionalities.
- Organization-wide training and development: There is an ongoing need for training and adaptation to new AI-driven processes and tools.
Phase 5: Optimize
In the final stage, AI systems, including LLMs, are fully integrated and play a central role in all marketing activities. Everyone in the organization bears responsibility for continuous improvement and innovation.
What does our role look like:
- Strategic leadership and innovation: Employees assume the role of strategic leadership, focusing on innovation and setting the long-term vision of the marketing strategy.
- AI collaboration and management: The focus is on collaborating with AI, managing advanced systems and ensuring that AI seamlessly aligns with business goals.
- Continuous improvement and adaptation: everyone is involved in continuous improvement and adaptation of AI systems to ensure maximum efficiency and effectiveness.
At each stage, our role evolves, from initiating and guiding AI implementations to strategically managing and innovating advanced AI systems
The advantages of AI that the marketing enjoying are the following
Improved efficiency
Fast and accurate customer data processing
Optimized marketing campaigns
AI system to automate tasks such as the following:
Lead scoring: If your business gets hundreds of new leads per day, prioritizing the leads who are close to converting is important. Automate your lead scoring with AI so your sales reps don’t have to guess which lead they must reach out to first.
Lead routing: Improve your speed to lead by automatically qualifying, sorting, and delegating leads among your sales reps. AI can improve your lead routing process using machine learning to analyze lead and sales performance data. As a result, you can accurately predict which leads are qualified and which among your sales rep can most likely nurture a relationship with a lead and close the deal.
Email send-out: You can use AI to create triggered emails and email drip campaigns to help you nurture your leads and improve your conversion rates. AI can also help you analyze your email engagement to improve your email’s open rate and click-through rate (CTR).
Answering site visitors’ inquiries with a chatbot: You can train AI chatbots to assist site visitors looking for information on your website. Some AI tools can also gather leads on your site and integrate with your customer relationship management (CRM) software.
AI helps marketers make data-backed strategies for the following tasks:
- Customer segmentation: Discover your customers’ purchasing habits or other behaviors using AI so that you can segment them accordingly. Use AI to process your lead and customer data, so you can look at patterns and decide the best way to segment them.
- Personalization: Did you know that 88% of marketers using AI say it has helped them personalize the customer journey across different channels? With the help of AI to analyze your customer’s interactions with your business, you can provide personalized experiences to your audience based on the products they’re interested in or how long they’ve been your customer.
- Improved ad targeting: AI can analyze large amounts of data, like your ad campaigns’ historical performance. Which customer segments clicked on and converted through an ad? What are the ad messages that resonate with each segment? AI can help you answer these questions to help you improve your ad targeting and CTRs.
When discussing the challenges of AI in marketing, it’s inevitable to bring up the topic of AI ethics. In this section, let’s go through the other disadvantages of the technology in marketing:
Data privacy
Concerns of biases and inaccurate content
Lack of creativity
Let’s discuss each one:
1. Data privacy
For AI to draw insights from your campaigns, it must collect and analyze large amounts of lead and customer data, thus raising data privacy and security concerns.
Because transparency fosters trust among your customers, it’s essential to disclose to your customers how you collect, process, and protect their data. Once you’ve communicated this, let them decide whether they want to continue sharing their data with you or not.
Take extra measures to safeguard the customer data you’ve collected to avoid data breaches.
2. Concerns of biases and inaccurate content
AI’s output is only as good as the data it’s trained on. If you train it with data of disproportionate representation, AI may produce inaccurate or biased predictions. It can also lead to unfairly targeted ads.
Pro tip: Implement algorithmic decision-making only in the right places and with the right data. Regularly audit your AI system and data to avoid bias.
3. Lack of creativity
Today’s AI technologies are narrow AI, a type of AI designed to perform specific tasks. They can effectively analyze data and automate tasks.
- Jasper AI (for copywriting)
- Lexica Art (for blog thumbnails)
- Surfer SEO (for SEO content writing)
- Notion AI (for productivity)
- Content at Scale (for generating SEO blog posts)
- Originality AI (for AI content detection)
- Writer.com (content writing for teams)
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