DigitalTransformationTalk: Closing the engagement divide in the age of AI driven customer expectations 

Views on news 

AI is fundamentally changing not only individual processes but also the relationship between companies and customers. Analytical AI reveals customer needs that were previously hidden. Generative AI enables personalized communication on a scale that was previously not economically feasible. Predictive models shift the focus from reaction to anticipation. The CAS in Strategic Customer Management offered by the Institute for Communication and Marketing at Lucerne University of Applied Sciences and Arts combines customer-centric strategy, CX management, CRM, and data-driven transformation – with AI and data not treated as isolated modules, but rather as a methodological underpinning throughout the curriculum. The focus is on what leaders and professionals in customer management truly need: an integrated understanding and the expertise to drive change within their own organizations. While most businesses are aware of what good customer relations look like, the operational reality is often different with fragmented data and different functions using different metrics.  

 

The need for new customer engagement metrics 

While legacy digital metrics such as click-through rates still matter, it should also be asked whether the customer relationship has been moved forward measured by metrics such as retention, repeat purchase, customer lifetime value and loyalty. Personalisation, which is key to good customer outcomes, has two different aspects – while the customer is searching, it must offer a broad view and offer the customer a wide variety of options, while towards the end of the customer journey, it must be laser-sharp and very specific. There is a paradox, though, about personalisation – while customers expect it, they are reluctant to share their personal data that could enable it.  

 

To ensure that customer relations create real business value is easier in sectors that generate real-time data than in others and measuring outcomes can require specific techniques and metrics in businesses where customer life cycles are radically different. Many companies implement AI on top of fragmented customer data and fail to get better outcomes. In contrast, unifying customer view even between B2B and B2C lines of the business means that signals that surface in the latter can inform marketing strategies for the former. Today, conversational AI can deliver the highest value along the customer journey. But AI’s pattern recognition capabilities can also be leveraged extensively to see early sign of changing lead and lag measures. However, over-automation and over-personalisation can become serious issues. In FMCG, AI is making great strides in building customers’ psychological profiles, highlighting regional differences.   

 

The panel’s advice 

• Unified data can give AI the context necessary for scaling.  
• Oftentimes, to deploy AI, you must re-design the complete workflow to achieve good outcomes.   
• Don’t rely solely on AI-driven outcomes.  
• Pick one high-value customer journey you’re already operating, map out what data, teams or systems create frictions there and find ways how AI can remove those.