According to the official customer case of Kouzi, Hefu Laomian integrates review analysis into the workflow, and the agent identifies review emotions and keywords such as dishes and services, and outputs the results in JSON format. The page describes it as an implemented business application and notes that the case data is provided by the customer. This article has not verified the specific model, the complete set of comments and the extent of operational improvement.
After the comments become labels, the store still needs to understand what the customer is talking about. The following is an independent analysis by ChinaOPC. The example sentences are all illustrative constructions and do not come from Hefu Lo Mein customer records, nor are they undisclosed implementation details of the project.
A comment can contain both positive comments and pending issues.
For example, "I like the noodles, but the waiting time is longer than expected." This includes affirmation of the dishes and feedback on the waiting experience. If only one overall positive and negative label is retained, the operator may not be able to see the parts that need to be further understood. You can first split the expression into different items, and then retain the customer's original words, the objects involved, and the information to be confirmed.
"The waiter was very patient and the seats were a bit crowded" also does not fit into the service category alone. The seating experience may be related to the arrival time, number of people or space arrangement, which needs to be discussed separately. The purpose of classification is to help find issues worth understanding; the system should not infer that there is a certain cause for employee attitude, dish quality, or store management based on just a short review.
Labels also need to accommodate situations where information is insufficient. Customers who only write "this time was average" can be left as unclear experience feedback, and should not automatically conclude that a certain dish is not delicious. If the operator wants to know more, he should ask through the appropriate service channel instead of letting the model write the reasons for the customer.
Before comparing quantities, first check if they are the same thing.
The same review may appear repeatedly in different exported files, and a single meal may generate multiple responses. The small team can help the store clarify whether it is counting the number of reviews, feedback items or identifiable dining events, and retain the source correspondence. The three calibers cannot be replaced at will, nor can repeated texts be counted as multiple independent customers.
The user composition, display methods and collection coverage of different platforms may also be different. If a new comment source is added this week, the number of negative feedback will increase accordingly, and it cannot be immediately determined that the operating situation has deteriorated. You can first explain which channels and time periods are covered in this issue, and then observe changes within the same range, leaving the store manager the necessary room for explanation.
Review processing should start from the export or interface that the store has the right to use, and be limited to the scope of business needs. The identity of the reviewer should not be guessed, and the analysis results should not be used to create positive reviews or suppress reasonable feedback. When displaying reports publicly, you can prioritize the use of aggregated and de-identified examples to prevent irrelevant information from being repeatedly disseminated along with comments.
Leave the analysis to someone who can plan the next steps
A result can differentiate between specific issues that require immediate review, repeated feedback that warrants ongoing observation, and general expressions that cannot be located at the moment. For example, feedback related to waiting for meals can be further understood by the store based on existing meal records and on-duty status; if there is only a comment itself, it should explain what information is missing, rather than automatically generating a complete conclusion on responsibility.
A small team is trying out a review analysis service. They can sort out a small number of approved samples around the issues that a store really wants to know about in the near future, so that the store manager can judge whether the classification is helpful. The deliverables can be a list of questions and a discussion record based on the original text; whether subsequent actions are taken and whether the actions bring improvement requires continuing to collect corresponding evidence.
This case provides clues to the entry of catering reviews into the AI workflow, but not enough material has been disclosed for ChinaOPC to audit labeling effects and operating income. Hefu Laomian is a chain brand, and this article does not identify it as OPC. What is suitable for small teams to study is how to help stores clearly understand customer expressions and allow managers to arrange follow-up work based on the basis.
Extended reading: [How AI organizes product data to determine fields] () and [How small merchants clearly deliver content services] (), compare the different tasks of data collection, problem analysis, and external content services. Related reading: How to determine fields when sorting product information using AI; How to clearly deliver content services for small businesses.