Introduction
Generative AI has emerged as a disruptive force in transforming customer-facing functions, including marketing, sales, commerce, and customer service, accelerating the shift toward personalized and intelligent customer experience (CX). These functions are being reshaped through understanding customer preferences more accurately by combing through and analyzing vast data sets, enhancing interactions through real-time insights, tailoring recommendations, content, products, and experiences, and automating tasks and processes. This research byte covers how generative AI can transform CX by enhancing personalization, the potential of generative AI across the CX landscape, and the need to break down data silos to unlock the full potential of the technology.
Generative AI in Action: Opportunities across the CX Landscape
Generative AI has the potential to create a high impact across key customer-facing functions, including marketing, sales, commerce, and customer service.
The following are key use cases and applications of generative AI:
Fig 1: Generative AI use cases across customer service, marketing, sales, and digital commerce services
- Marketing: Generative AI presents a great opportunity for marketers to develop customized marketing campaigns and automate content creation.
- Sales: Generative AI is playing a key role in sales enablement, wherein it is being used for personalizing content such as sales brochures, automating processes such as lead scoring, sales forecasting, sales coaching, and streamlining sales operations and pricing strategies.
- Commerce: In commerce, generative AI is being used to personalize product searches and recommendations, create unique product descriptions, and analyze customer feedback at a scale.
- Customer Service: Generative AI is rapidly transforming the customer service space through self-service automation, personalization of interactions, and agent enablement.
Generative AI significantly improves revenue operations (RevOps), which is defined as the integration of sales, marketing, and customer service functions to drive process optimization and revenue enablement.
Generative AI: A Game-changer for Personalization
Personalization is core to CX and results in improving sales conversion, delivering a better return on marketing and advertising spending, and enhancing the ROI of CX initiatives. Generative AI models can quickly analyze vast customer data sets, both historical and real time, and combine human prompts to deliver outputs (recommendations, content, and so on) tailored to suit individual preferences and requirements.
Generative AI enhances personalization by:
- Creating dynamic and predictive customer segmentation and targeting.
Segmentation paves the way for personalization. Generative AI algorithms analyze vast amounts of customer data, such as purchase history, browsing behavior, demographics, and customer data, leading to the creation of dynamic customer segments that get updated in real time. This can be used to develop better predictive models for predicting customer churn and forecasting demand. For instance, predicting the next customer order and generating a personalized marketing email.
- Automating content creation and creating personalized content at scale.
Content is key to CX. With minimal human intervention, generative AI helps create personalized content across various categories, including text, images, and videos. Generative AI does this by leveraging natural language processing algorithms, prompts, and large language models to automate and optimize content creation based on past and real-time customer data, for example, product descriptions, email campaigns, and social media blog posts.
An example is Spotify, which uses generative AI (OpenAI) to analyze user listening patterns and preferences to let music editors provide listeners with insightful facts about the music, artists, and genres and create personalized playlists based on listening habits. The company is further exploring creating podcast summaries and audio ads by leveraging generative AI.
Key Considerations for Enterprises for Adopting Generative AI in CX
- Overcoming the data integration challenge:
A rapid increase in customer interactions across multiple channels and touchpoints is leading to the creation of enormous amounts of customer data for enterprises. However, creating a 360-degree view of the customer acts as a major roadblock. Without proper data integration, quality, and privacy checks, generative AI might misinterpret customer queries, produce inaccurate responses, and lead to data breaches and unauthorized access. Here, the role of customer data platforms such as Oracle (Unity), Adobe (Real-Time CDP), and Twilio (Segment) becomes crucial to collect real-time data across channels, third-party sources, and CRM systems to create a unified customer profile. These platforms also help secure customer data through enhanced authentication and encryption, such as TLS 1.2 and Advanced Encryption Standard, and compliance with regulations such as the GDPR and the California Consumer Privacy Act.
- Building the implementation road map:
To support enterprise needs, the ecosystem is maturing fast, with large to small platform companies racing to offer generative AI-based tools and integrate the technology into their existing products. For instance, Adobe Firefly uses natural language processing for image generation and video editing. Through generative AI, Salesforce Einstein GPT enables the creation of personalized content across Salesforce cloud platforms, including Sales and Marketing. Enterprises must ensure that generative AI is well integrated into their existing CX and CRM systems to create real-time personalized experiences. With their diverse ecosystem partnerships in CX, service providers can support enterprises in identifying the right platforms and use cases and defining the implementation road map. They can accelerate adoption by leveraging prebuilt assets and workflows and selecting the right foundation models.
- Setting quantifiable and measurable goals:
It is crucial for enterprises to move quickly beyond proof of concepts and minimum viable products to full-fledged implementations. For this, a timeframe for experimentation must be defined, along with clear goals and metrics to measure the success of pilot projects. The goals could be to improve the conversion ratio, repurchase rate, mean time to resolution, or customer churn rate. This can be extended to measure the impact on key customer service metrics such as net promoter score, customer effort score, and customer satisfaction score through customer feedback measurement and analysis.
- Ensuring content quality and data access control:
Enterprises must ensure that the content and assets developed using generative AI are of the highest quality and comply with the copyright rules. Also, there is a need to reduce data exfiltration and leakage that requires companies to put strict access controls in place, allowing only authorized users to leverage generative AI for the purpose of content creation or improving CX processes and functions.
Conclusion
The use of generative AI in enhancing CX is becoming increasingly crucial to provide personalized services and streamline customer operations. However, integrating data, implementing AI, and measuring ROI are significant challenges that businesses face. To overcome these challenges, companies need to break down data silos, navigate complex vendor ecosystems, and develop a solid business case that focuses on desired outcomes. Collaborating with a strategic partner who can control costs, accelerate time to market, and bring in the right talent can help businesses adopt generative AI in CX more efficiently and reap the maximum benefits.
By Shwetank Saini, associate research director, Ashutosh Darmal, senior analyst, and Biswadeep Hazra, senior analyst, Avasant
