Creating personalization and loyalty through data and AI
AI is getting smarter about everyone
Moving from predictive to prescriptive AI
While predictive models can anticipate outcomes, prescriptive AI takes it a step further by recommending actions to prevent undesired outcomes. For example, businesses can predict customer churn and tailor responses to retain at-risk customers. Prescriptive AI transforms customer experience by providing valuable recommendations that drive engagement, loyalty and revenue.
How do you make the leap from predictive to prescriptive AI? AI and machine learning can be leveraged for explanatory purposes to answer questions like:
- What are the drivers that are converting my customers?
- What are the drivers that are retaining my customers?
- How can I recommend certain products?
For example, customer churn. You can build a model without much effort - and there’s even pre-built models inside Azure ML - that will predict which customers are at risk so you can tailor responses to those individuals. Being able to predict churn is not nearly as valuable as being able to prevent it.
Achieving a 360-degree view of the customer
Industry examples of AI
AI in RetailThe use of bots powered by natural language processing (NLP) has gained momentum. Customers are becoming more receptive to leveraging AI-based solutions as they offer increasingly efficient and personalized customer service experiences. Bots can handle routine queries, and the human element is introduced when necessary, resulting in enhanced customer satisfaction.
AI in AgricultureAI assists sales teams in making data-driven predictions for seed purchases based on factors such as weather, season and competitor activities. By leveraging AI, agricultural businesses can streamline sales processes and enhance decision-making, ultimately maximizing productivity and profitability.
AI-powered chatbots are being adopted to improve patient interactions. Research has shown that patients feel more comfortable sharing information with chatbots than with live physicians during intake processes. This preference for AI-based interactions presents scalability advantages for healthcare providers and enables exponential growth in personalized care.
AI in Healthcare
Integrating AI into existing workflows and processes is a complex task. Language-based AI solutions enable seamless integration and continuous learning, facilitating effective communication and optimization within manufacturing environments.
AI in Manufacturing
Real-world applications for Generative AI
Examples include leveraging sophisticated chatbots for enhanced customer experiences, summarizing financial reports, automating claims processing, and even generating personalized stories and recipes in a personal context.
Applications of Generative AI are expanding far beyond chatbots. By fine-tuning base models with proprietary data, organizations can derive unique value and provide domain-specific answers. This integration of corporate data with the power of large language models offers a novel way to unlock enterprise data and generate tailored insights. Companies that quickly embrace this approach are likely to gain a competitive edge by leveraging the best of the internet and their own proprietary data.
AI can be daunting - so start backwards
By holistically analyzing these three essential considerations, organizations can establish a strong foundation for their AI journey:
1. Determine the problem AI will solve
2. Select the appropriate AI type
3. Decide how to leverage the data collected for empowering experiences