15 Ways AI Transforms Your Marketing Strategy

Artificial Intelligence (AI) is far from a novel concept in the marketing world. It’s become particularly interesting given the surge in technologies like ChatGPT. While the term AI is a broad umbrella, it has several sub-technologies with varying levels of maturity and applicability to different business sizes.

AI essentially tries to emulate human cognition—think voice and image recognition, machine learning, and semantic search. In our AI and Machine Learning guide, we spotlight 15 AI techniques that cater to businesses big and small. These techniques encompass three core tech categories—Machine Learning, Applied Propensity Models, and AI Applications.

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Three Types of AI Technology

1. Machine Learning Techniques: These use algorithms that ‘learn’ from past data and then create models for prediction.

2. Applied Propensity Models: These models, once created, are employed for predicting events like lead conversion.

3. AI Applications: These are broader AI tools that perform tasks typically requiring human cognition, such as customer support.

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The Customer Journey and RACE Framework

The applications differ in their relevance at various points in the customer journey. Therefore, we’ve organized them under the RACE (Reach, Act, Convert, Engage) framework. This framework helps marketers measure and prioritize omnichannel touchpoints across the customer journey.

Reach – Drawing in the Crowd

1. AI-Generated Content: AI can’t replace human creativity in opinion pieces or industry advice, but it’s exceptional for generating data-focused content, such as earnings reports.

2. Smart Content Curation: AI enhances visitor engagement by showcasing relevant content, effectively holding the potential customer’s interest.

3. Voice Search: This tech, usually developed by big players like Google, Amazon, and Apple, is a game-changer for SEO strategies.

4. Programmatic Media Buying: AI’s machine learning capabilities optimize ad placements, making programmatic ads more effective and safer.

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Act – Grabbing Visitor Attention

1. Propensity Modelling: Machine learning can analyze data to predict behaviors like customer conversion rates.

2. Predictive Analytics: This application uses propensity models to predict various customer behaviors, ensuring that your business decisions are data-driven.

3. Lead Scoring: Machine learning models can rank leads on their likelihood to convert, helping sales teams prioritize their efforts.

4. Ad Targeting: Machine learning refines ad targeting by continually optimizing a plethora of variables.

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Convert – Turning Interest Into Sales

1. Dynamic Pricing: Machine learning models predict which customers need an offer to convert, maximizing sales while protecting profit margins.

2. Web and App Personalization: Knowing where a customer is in the buying cycle allows for content optimization, enhancing the likelihood of conversion.

3. Chatbots: Despite the misconception that they’re only for big brands, chatbots are accessible through open platforms, making customer service more efficient.

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Engage – Fostering Customer Loyalty

1. Re-targeting: Machine learning helps in customizing content to re-engage customers, bringing them back to your website.

2. Predictive Customer Service: Machine learning algorithms predict which customers are at risk of churning, allowing for proactive outreach.

3. Marketing Automation: AI sifts through enormous data sets to identify optimal times for customer engagement, maximizing the impact of marketing automation systems.

4. 1:1 Dynamic Emails: Machine learning helps curate personalized emails, adjusting the content in real-time for maximum relevance.

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With the right implementation of AI within the RACE framework, you not only build a robust marketing funnel but also create a path for sustained business growth. Make the most of these AI applications to amplify your marketing impact today.