20 Ways Artificial Intelligence & Machine Learning Transform Marketing - Great Learning
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20 Ways Artificial Intelligence & Machine Learning Transform Marketing

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Marketing initiatives and practices are becoming highly data-driven in today’s scenario and this is mainly due to the radical digital transformation that has swept across every major industry. With a huge amount of data being generated every second, it becomes necessary to invest in advanced technologies that can efficiently manage, analyze, and reap maximum insights out of this data, namely Artificial Intelligence & Machine Learning.  

Apart from analyzing and decoding data, Artificial Intelligence & Machine Learning have a wide range of use cases and applications that can immensely help marketers communicate and advertise better. According to a recent survey by QuanticMind, 97% of industry experts believe that AI will play a massive role in the future of digital marketing.

Here are 20 ways in which AI and Machine Learning can transform marketing:

Buyer Persona

Every customer has specific traits or characteristics that define their buying habits. Creating a buyer persona enables marketers to market the right product or service to a customer. AI can study millions of users based on their data and create precise personas for each customer based on their buying patterns

Customer Experience and Engagement

Salesforce’s 2018 report stated that 52% of consumers can change their brand preference if they do not receive a personalized customer experience. AI can be used to send personalized messages to each customer at scale based on their buyer personas. This increases digital customer engagement and subsequently conversions, hence revenue.

Content Generation

Marketers can use AI to both curate content as well as create it. AI can curate content automatically by scanning the internet. Based on a customer’s specific interest, it can perform activities such as generating personalized emails automatically at scale. With the help of natural language processing and adequate amounts of data, AI can also drive quality content production.

Audience Segmentation

While planning marketing campaigns, marketers need to properly identify and segment users based on different criteria. With AI, marketers can target granular segments of customers. AI algorithms can track large groups of buyer personas and consumer characteristics to efficiently group them into granular segments for targeting.

Product Recommendations

Based on a customer’s online activity, marketers are using AI to recommend or suggest similar or new products. Companies such as Netflix use AI-based predictive analytics to recommend movies and TV shows to its subscribers. E-tailers like Amazon are using this feature to cross-sell and up-sell products to their consumers.

Product Pricing

Most businesses have a dynamic pricing model that depends on consumer demand for a product or service. AI can take dynamic pricing to a whole new level. Using machine learning to analyze consumer data patterns such as their receptiveness to offers, buying propensity, etc., the most precise price can be determined for each product. This helps companies to calculate the exact amount of discount needed for a sale.

Product Categorization

E-commerce companies who source products from various sellers find it difficult to correctly categorize them. Machine learning can help retailers tag and categorize products more efficiently. For example, a product such as a shoe might have been given different names by sellers in different languages. AI-based algorithms can accurately analyze and identify the right category of the product.

Lead Sourcing

The traditional ways of generating data take a lot of time for marketers as they need to obtain, review, and classify data before targeting potential leads. As marketing becomes more data-driven, marketers are using AI and ML to analyze and make sense of the data. AI can capture a large dataset and identify the best B2B targets for marketing initiatives. It can effectively increase the quality of the sourced leads in lesser time.

Account Selection

Sales executives spend most of their time doing monotonous repetitive tasks to find potential leads. By incorporating AI in various databases and B2B applications, these mundane tasks can be easily automated. AI can further help in identifying ideal prospects by analyzing large data pools of existing and new customers. This will lead to better results through higher conversions.

Lead Scoring

As AI helps in identifying ideal leads to target, it also helps in maintaining lead scoring. Using advanced algorithms to analyze the readiness to purchase, a specific score can be given to each lead. This enables departments like marketing and sales to prioritize leads and strategize sales process.

Sales Forecasting

By analyzing past sales data and industry trends, AI can efficiently help in forecasting sales for businesses. This helps marketers to make informed business decisions and predict their short-term and long-term performance.

Programmatic Ad Targeting

AI can make programmatic ad bidding and audience targeting more efficient. Machine learning algorithms can determine factors such as the probability of a user to click on an ad, the best time to serve an ad, the type of ad that can resonate well with a user type, etc. AI can also be used in ad bidding strategies for various customer segments.

Website Search Engine

In 2015, Google introduced its machine learning algorithm called RankBrain that improved the search quality and experience for its users. Many companies soon followed suit by implementing AI into their search engines. AI can determine the link between various products, find relevant search results, and suggest similar items. This can help consumers in discovering products even if they aren’t sure of what they are looking for.

Search Engine Optimization (SEO)

SEO analysts take a lot of time in identifying keywords, generating competitor insights, analysing market trends, creating backlinks, and so much more. AI can help in automating these tedious tasks and make them more efficient while generating marketing content. With AI handling SEO, marketers can focus more on improving customer engagement and conversions.

Social Monitoring and Sentiment Analysis

Thanks to advances in natural language processing, marketers can now analyze brand presence by listening to the conversations around their brands on social media. Using AI-based sentiment analysis on social conversations, they can now understand how consumers perceive their brand. This helps them in identifying potential issues and fixing them immediately before they become widespread.

Speech Recognition

The Google Assistant and Amazon’s Alexa are AI-based voice assistants that have been very successful in interpreting user requests. With natural language processing, these AI assistants can successfully decode a user’s voice request and respond with the correct reply or action. Companies can thus use this feature in their marketing applications.

Chatbots and Conversational AI

AI Chatbots provide a convenient way for companies to interact with customers and respond to their queries at any given time. Conversational AIs are used as customer service representatives to address user concerns. Trainline, a UK travel company, recently released an AI-based voice app with 12 levels of conversation depth. The app provides accurate information to commuters and can improve itself through machine learning algorithms.

Email Automation

Email marketing has been used to target and nurture leads who dropped their email addresses through website forms. AI-powered email automation can enable marketers to address each customer concerning their specific needs. Rather than sending random mass emails, marketers can use AI to improve email campaign performance via contextually personalized messages.

Automated Image Recognition

Image recognition is a common AI application nowadays thanks to companies like Google and Microsoft who have incorporated it in their search engines.  Apps like “Google Photos” and “Photos” (iOS) have this feature in-built where they could automatically recognize people from pictures. This AI-driven feature can be used by marketers to track physical store visits by users and link them to their customer profiles. This enables them to send personalized push notification messages to customers in real-time when they are actually in the store.

Cyber Security

Most online businesses are susceptible to security breaches. Advanced security systems are already being used that can instantly alert companies of fraud and other security breaches. Coupling AI-based intelligent algorithms with these systems can drastically improve threat detection, risk assessment, and breach remediation.

Just as AI has the potential to transform industries, marketing also stands to see massive process improvements and better results through the strategic application of Artifical Intelligence. If this is what the present looks like, we’re sure that what the future promises would be even more exciting. If you are a marketer and are looking to understand how to use AI in your job, take a look at our AI & ML program here.

 

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