The growth in demand for hybrid services (physical and digital) continues to increase and behind it, there are consumers who demand more customized attention and a frictionless experience at all touch points. Many of them have found creative ways to adapt to the circumstances and with the help of technology manage to navigate uncertainty in new ways. If companies want to retain and strengthen their customer network, they must do the same.
Smart applications and machine learning-enabling artificial intelligence (AI) that is both explainable and scalable-maximize access to information around customer needs, behaviors and data to hyper-personalize offerings, reinvent the marketplace and allow services to be tailored to their individual circumstances. However, today, companies experience technical challenges in implementation, often lack an end-to-end AI strategy, and lack a user-centered approach.
To fight these and other challenges in AI adoption, IBM recommends three practices to help companies develop better customer retention strategies:
- Use an agile and people-centric approach to better understand customer needs. Agile methodologies (such as IBM Design Thinking) aim to focus on user needs by conducting multiple brainstorming sessions with customers at the beginning of a project, which allows aligning AI with key user issues and desires. Prototyping and iteration of these ideas should follow before formulating solutions. When it comes to customer retention, a smarter system is needed to help prioritize which customers need attention and immediate notification when a customer is at high risk of leaving. To truly embrace AI, it is key to have systems that can be trusted, i.e., produce explainable AI results.
- Apply the AI Ladder model to create end-to-end processes for AI applications. This model provides organizations with an understanding of where they are on their AI journey, as well as a framework to help them determine where they need to focus by providing five key areas to consider: 1. how to become modernized so their data is ready for an AI and hybrid cloud world; 2. how to make data simple and accessible; 3. how to create a business-ready analytics platform; 4. how to build and scale AI with transparency and confidence; and, ultimately, 5. how to put AI to work across the organization.
- Reduce complexity and increase repeatable AI processes. According to IBM’s AI Adoption Index, 80% of companies said they are using or plan to implement some form of automation software over the next 12 months, which will enable them to transform their processes both efficiently and profitably. To accelerate the implementation of AI across the company, rather than adopting tailored, non-replicable approaches, it is more efficient to create a single platform to deploy all AI applications, standardize processes and strengthen business outcomes. For example, IBM Cloud Pak for Data offers a wide range of services, including AutoAI to automate the model building approach and Watson Studio to enable ethical and explainable AI.
The battle for consumer trust is taking place on several fronts, from making AI decisions understandable and explainable, to reassuring consumers that their personal data is protected from cyber attacks. Only transparent, reliable and effective AI will drive business growth and strengthen customer retention and acquisition strategies.