Through months of continuous communication and comprehensive consideration, a leading independent brand automaker in North China fully recognized the digital construction capabilities and best practices of Futong Auto Intelligence's Voice of Customer (VOC), and finally chose Futong VOC as the digital management support platform for comprehensively managing customer voices and practicing user-centered management within the group. Relying on AI + application scenarios, Futong Auto Intelligence VOC helps automakers gain insights and improve customer satisfaction and service efficiency, driving the continuous improvement and innovation of various businesses.

Full-cycle data integration of customer experience

Based on the full-cycle journey of customers, customer data is connected from the client side to the business side, and ultimately intelligently empowered in different stages of the entire business chain of the automaker.

• Internal data of the automaker, including 400 hotlines, APPs, official websites/online customer service, etc.

• External data of the automaker, including vertical media, complaint websites, social platforms, etc.

• Cooperative data of the automaker, including channel survey data and cooperation with external institutions, etc.

Establish an exclusive index system and model algorithm

Futong Auto Intelligence VOC customizes an exclusive fusion label system for this automaker, and uses artificial intelligence technologies such as NLP and DL to intelligently extract and analyze customer viewpoints and emotional characteristics in scenarios from customer voice data.

Sample

• Customer viewpoint extraction

Based on the semantic context, extract customer viewpoints from the source data without distorting the original voices of customers. For example, in the sample, the start-stop function failed after driving 6,900 kilometers.

• Customer sentiment tendency characteristics

Extract the real sentiment tendencies of customers in combination with the original voice scenarios, such as positive, negative, neutral, inquiry, and suggestion.

• Label system mapping to achieve end-to-end business output

Product # Quality problem -> Warranty, after-sales and other departments; Service # Customer experience -> Customer operation and other departments.

AI insight into explicit and implicit customer needs

By recognizing the voices of customers from various channels of this automaker, mining the key concerns of customers, helping this automaker understand customers' attitudes towards products or services in a timely manner, and analyzing customers' core concerns, fully identify the current feelings during the product usage process, and assist business departments in understanding customers' explicit and implicit needs.

Comprehensively manage customer experience

The analysis of customer experience includes emotion and intention analysis.

Identify the hotspots of positive and negative emotions in customers' comments and messages on various vehicle models to assist business departments in understanding potential high-quality customers.

• Customer intention analysis

By collecting customer voice data from various channels and timely insight into customers' actual feelings during the process of product and service provision, including complaints, inquiries and suggestions, timely identify potential operational events.

All-round product analysis and insight

Through the exclusive quality label system constructed for this automaker, multi-channel data sources are integrated to monitor product quality, and summarize typical faults so that the business department can observe the common problems of related vehicle models more intuitively. At the same time, detailed data query is provided to accurately locate specific vehicle models and specific product failure problems, so that the business department can make improvements more quickly. The platform aims to help this automaker achieve the digital product innovation path from trend discovery -> value mining -> decision evaluation.

Multi-dimensional risk identification, early warning and closed-loop management

With the help of the Futong Auto Intelligence VOC platform, potential operational event risks such as product quality and high-frequency complaint customers are identified and mined. Through risk event identification and early warning, push processing and effect display, a PDCA closed-loop solution process for risk issues within this automaker is formed, helping business departments understand current potential risks in a timely manner and prepare countermeasures to prevent the occurrence of brand reputation risks.