Potential AI and Machine Learning Use Cases in the Financial...

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Potential AI and Machine Learning Use Cases in the Financial Marketing Sector

By CFO Tech Outlook | Tuesday, August 11, 2020

Machine learning and AI have enabled financial marketers to connect activity and behavioral inputs such as transaction history, website inquires, social media interactions with consumer-centric outputs

Fremont, CA: AI and machine learning make the customer experience more tailored and relatable than before. Banks and credit unions use advanced technology to make websites, emails, digital advertising, social media, and other content more efficient and effective, increasing marketing ROI and customer satisfaction.

Data can be analyzed through machine learning with the help of advanced algorithms to better understand the intent of the inputs and offer tailored and real-time messages.

Smart Content Curation:

This shows visitors the content relevant to them based on what other visitors have bought in the past. It is a recommendation engine that includes products, content, and offers.

Programmatic Media Buying:

It uses propensity models to effectively target ads to relevant customers with the help of AI by establishing the best and worse sites for ads.

AI-Generated Content:

AI content writing programs can choose features from a dataset and structure an article customized to a specific prospect. AI writers can help with quarterly earnings reports and market data for banks and credit unions.

Voice Search:

AI-driven voice technology utilizes technology developed by major players to increase organic search traffic using digital personal assistants.

Propensity Modeling:

The propensity model uses a large amount of historical data to predict about the real world. ML also helps direct consumers to the right message and locations on the website and generates outbound personalized content.

Ad Targeting:

Top 10 Artificial Intelligence Solution Companies - 2019By processing large amounts of historical data with a propensity model, it can decide which ad will perform best on the target customer and at specific stages in the buying process for a more effective ad placement and content.

Predictive Analytics:

Propensity models also help decide the likelihood of a customer to convert, predict pricing, or which customer will likely repeat the purchase.

Dynamic Pricing:

Machine learning is used to make special offers only for those prospects likely to need them to convert. It maximizes profit by increasing sales without reducing a lot of profit margin.

Chatbots:

AI-driven chatbots mimic human intelligence, interpret consumer inquiries, and complete orders.

Predictive Customer Service:

Predictive learning is used to find out which customers will likely become dormant or leave. With this knowledge, businesses can reach out to these customers with offers, prompts, and assistance to avoid churning.

See also: Top Machine Learning Companies

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