The evolving landscape of customer feedback management: Generative AI (genAI) is making inroads in customer experience (CX) management, but its impact on voice-of-the-customer (VoC) programs is more evolutionary than revolutionary.
- Despite initial excitement, only one-third of CX professionals currently utilize genAI for VoC and CX measurement, according to Forrester data.
- The slow adoption rate suggests that companies not yet incorporating genAI into their VoC programs are not necessarily falling behind.
- CX professionals are advised to approach genAI integration cautiously, considering its value-add before implementation.
Challenges hindering rapid genAI adoption: Several factors contribute to the measured pace of genAI integration in customer feedback management.
- Text mining techniques, particularly hybrid AI combining knowledge-based and machine learning approaches, remain effective for analyzing unstructured feedback.
- GenAI cannot fully replace traditional analytics methods like prescriptive and predictive analysis, which are crucial for informed business decisions.
- Many VoC programs struggle with siloed and disorganized unstructured data, making it challenging to leverage genAI effectively.
- Customer-facing genAI applications pose risks to trust and transparency, with 78% of US online adults expecting companies to disclose genAI use in interactions.
Balancing innovation and caution: While customer-facing genAI applications offer exciting possibilities, they also come with significant risks that CX professionals must navigate carefully.
- CX teams should evaluate the specific value-add of genAI features within their existing programs before implementation.
- For example, genAI-drafted surveys may provide limited benefit to well-established VoC programs but could be highly valuable for less mature initiatives.
- Customer feedback management (CFM) solution providers are increasingly incorporating genAI features, requiring CX professionals to stay informed about available options.
The role of traditional analytics: GenAI’s limitations in certain areas underscore the continued importance of established analytical methods in CX management.
- Prescriptive and predictive analyses remain essential for making data-driven business decisions.
- The nondeterministic nature of genAI can pose risks when used as the sole basis for critical business choices.
- A balanced approach combining traditional analytics with genAI capabilities may yield the most comprehensive insights.
Data management challenges: The effective use of genAI in VoC programs is often hampered by existing data infrastructure issues.
- Many organizations struggle with siloed and disorganized unstructured data from various customer feedback channels.
- Preparing this data for genAI analysis requires significant effort in organization and establishing proper data connections.
- Addressing these data management challenges is crucial for maximizing the potential of genAI in customer feedback analysis.
Customer trust and transparency: The use of genAI in customer-facing applications raises important considerations about maintaining trust and meeting customer expectations.
- A significant majority (78%) of US online adults expect companies to disclose when they use genAI in customer interactions.
- Only 30% of consumers say they would trust information provided by genAI, highlighting the importance of transparency in its application.
- CX professionals must carefully balance the benefits of genAI with the potential risks to customer trust and loyalty.
Looking ahead: Strategic integration of genAI: As the customer feedback management landscape continues to evolve, CX professionals must develop strategies for thoughtful genAI integration.
- Regular assessment of available genAI features from CFM solution providers will be crucial for staying competitive.
- CX teams should prioritize use cases where genAI can provide significant value without compromising customer trust or data integrity.
- Continuous monitoring of customer attitudes towards genAI will help inform responsible implementation strategies.
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