As marketers struggle to understand an ever-increasing volume and velocity of data, AI tools offer solutions. But in order to remain innovative marketers must balance AI capabilities with human capabilities when using such artificial intelligence marketing solutions.
Without core capabilities, teams may become unequipped to work quickly and innovatively – leading to costly consequences such as failed customer expectations.
Artificial Intelligence (AI)
AI in marketing is something most of us have experienced without even realising it; chances are, you have received personalized recommendations from online retailers or experienced assistance when calling customer service, or seen sponsored posts that aligned perfectly with your interests on social media.
Effective AI-powered marketing platforms help marketers gather invaluable information about their target audience to develop more targeted digital marketing campaigns, including sorting through large amounts of data that would otherwise be impossible for humans to sort manually.
AI can provide PR and communications teams with invaluable insights, helping to monitor online discussions about their brand to detect any potential issues and track consumer sentiment – especially during times of crisis when customers can quickly boycott brands. Levity uses an AI system to monitor and moderate social comments, using this data to ensure their reputation remains undamaged – an essential tool for keeping ahead of competitors while protecting customer bases.
Machine Learning (ML)
Machine Learning (ML) is an AI that uses large data sets to recognize patterns and predict behavior, from images and texts, through automating customer segment creation or personalizing messages, all the way to helping marketers make data-driven decisions and measuring marketing ROI. ML offers tremendous value to marketers by helping them make data-driven decisions while measuring marketing ROI.
Contrasting with traditional ad-targeting algorithms that rely on clicks as feedback, machine learning (ML) algorithms take many factors into consideration when making recommendations and predictions. For instance, charter jet firm XO utilizes machine learning techniques to predict demand for its services by tapping into external sources that may include macroeconomy factors, seasonal activity levels, weather data etc.
As machine learning involves access to sensitive customer data, marketers must take care to protect privacy and comply with applicable laws. They should establish ethics and privacy review boards comprised of legal professionals as well as marketing specialists to assess any ML projects undertaken.
Natural Language Processing (NLP)
With AI at our fingertips, nearly every industry will have an opportunity to harness its vast advancements. One particularly transformative application of AI is Natural Language Processing (NLP), providing data insights previously unavailable through traditional means.
NLP (Natural Language Processing) integrates computer science with linguistics to transform unstructured data into meaningful information that supports analysis and decision-making. NLP offers many capabilities, including voice recognition, text classification, sentiment analysis, image captioning and summary generation.
NLP technology powers an increasing variety of marketing tools that make engaging customers via messaging apps and websites simpler for businesses. Chatbots powered by NLP are one such example that make engaging customers easier; chatbots are an example that uses NLP for personalized customer service as well as automating high volume or repetitive tasks. NLP tools also help companies understand consumer attitudes by analyzing social media posts such as posts from Facebook or Twitter accounts to analyze sentiment analysis data gathered by social listening tools; some even generate images or videos based on descriptive text input from clients.
Generative AI
Generative AI is an exciting new technology with great potential to change marketing. CMOs should encourage their teams to experiment with it and identify applications, while pre-trained foundation models with unparalleled adaptability should also be integrated into workflows to further optimize existing processes.
Marketers using AI should employ this strategy to deliver more tailored experiences and content for customers while saving both time and money. They should closely monitor the performance of generative AI programs to ensure they meet expectations.
While generative AI can produce engaging and shareable content, its quality depends on the model used and data fed into it. Generative AI often struggles to produce high-quality long-form text when trained using content scraped from websites like Reddit, Twitter or Stack Overflow as examples; also programs must be retrained periodically due to rapid technological change.
