15 APPLICATIONS FOR AI AND MACHINE LEARNING IN FINANCIAL MARKETING.

Updated: Sep 23, 2019

Credit by: The Financial Marketing.



AI and machine learning are making the customer experience more personalized and contextual than ever before. Banks and credit unions are using advanced technology to make websites, emails, digital advertising, social media and other content more efficient and effective. This is increasing marketing ROI as well as customer satisfaction.


The greatest potential for AI in marketing is around the opportunity to deliver personalization and relevance at scale. As consumers engage with their bank and more transaction and behavioral insight are collected, the consumer expects interactions with their bank to be more contextual and personalized.


It doesn’t do any good if the talk about AI and machine learning in marketing is just theory, hype or if the technology can’t be implemented at organizations of all sizes. To help navigate the capabilities of AI and behavioral marketing across the customer lifecycle, Smart Insights provides a great inforgraphic identifying fifteen artificial intelligence techniques that organizations of all sizes can implement.


Stage I: Reach


Reach is the initial stage of the buyer’s journey. The key is to attract more visitors and provide an engaging experience that will lead to a purchase.


1. Smart Content Curation: This stage is about showing visitors content relevant to them based on what others like the prospect have bought in the past. In short, this can be a form of recommendation engine that includes products, offers and content.


2. Programmatic Media Buying: This relates to the use of propensity models to more effectively target ads to the most relevant customers. AI can help by determining the best (and worst) sites to be used for ads.


3. AI Generated Content: AI content writing programs can select elements from a dataset and structure a ‘human sounding’ article that is personalized to a specific prospect. For banks and credit unions, AI writers can assist with quarterly earnings reports and market data.


4. Voice Search: To improve reach, voice technology driven by AI is about utilizing the technology developed by the major players (Google, Amazon, Apple) to help increase organic search traffic using digital personal assistants.



Stage II: Act


The second stage of the consumer journey is intended to draw the consumer in and to make them aware of your products and services.


5. Propensity Modeling: Propensity modeling uses large amounts of historical data to make predictions about the real world. Machine learning at this stage helps to direct consumers to the right messages and locations on you website as well as to generate outbound personalized content.


6. Ad Targeting: Propensity models can process vast amounts of historical data to determine ads that perform best on specific people and at specific stages in the buying process. This allows for more effective ad placement and content than traditional methods.


7. Predictive Analytics: Using propensity models can help determine the likelihood of a given customer to convert, predicting what price a consumer is likely to convert, or which customers are most likely to make repeat purchases. The key here is accurate data.