Preparation of data for the neural network
Before you upload reviews into the dialog box with an AI assistant, they need to be systematized. It is enough to upload data from your CRM or personal account of the marketplace to a simple table. Collect the review text, assessment and date into one file. Clean up data from system debris and customer personal information. If the database is huge, divide it into parts of 50-100 reviews. This will help the model process information without losing context and provide the most accurate analysis results.
Creating a prompt for tone analysis
In order for a neural network not only to read texts, but to produce structured analytics, it needs clear instructions. Give models a specific role, such as an experienced support analyst. Entrust her with three categories of feedback: positive, neutral and negative. Be sure to specify the format of the data output. Ask the model to identify the main reasons for customer discontent in the negative group. This approach will immediately highlight weaknesses in your product or service.
Automatic tagging and grouping of topics
Customers often complain of similar problems: long delivery, marriage, or poor packaging. Assign neural networks to automatically assign thematic tags to each review. Instead of reading for a long time, you will get statistics. For example, the model will determine that 40% of the negative is related to the speed of couriers, and 10% is related to the quality of service. This will allow you to make quick management decisions based on real numbers rather than intuitive guesses.
Search for hidden patterns in reviews
Sometimes in the mass of reviews hidden unobvious ideas to improve the business. Ask the AI assistant to make a list of suggestions and wishes that customers mention in passing. These can be requests for new features, changes in the configuration or ideas to expand the range. The neural network will carefully collect these bits of information and present it as a structured list. You will get a ready-made guide to product development without expensive marketing research.
Generation of response templates to reviews
The final step is to create answers for customers. Write a prompt that will make the neural network compose polite templates for each review category. For negativity, ask the model to suggest specific ways to solve the problem, avoiding standard unsubscriptions. For positive comments, let the AI generate sincere gratitude with an easy call to re-buy. This will unload your managers and take audience loyalty to the next level.