Customer Feedback and Sentiment Analysis
Automatically analyze customer feedback, identify trends, weak signals, and priority improvement opportunities.
A customer relations manager recompiles the same scattered verbatims every month, across tickets, satisfaction surveys and online reviews, to produce three summary slides.
We build pipelines that connect this feedback to a topic list agreed with the business, without reducing the customer’s voice to a single figure.
Thursday evening, before the monthly review
A customer relations manager pulls together the month’s verbatims: support tickets, satisfaction survey replies, online reviews, messages relayed by the sales team. Each source has its own format, its own scale, its own vocabulary: a score out of ten here, a simple thumbs-up there, free text elsewhere. Compiling it takes a full evening and mostly serves to produce three slides for management, before falling back into a spreadsheet until next month.
An aggregate score can hide what matters
A sentiment pilot produces a convincing dashboard fast: a line trending up, an overall score edging higher. We have seen these dashboards abandoned by the second month, because they say nothing about what actually changed for customers. A stable average can mask a fresh irritant rising within one segment while another improves elsewhere. Irony, sarcastic thanks, or a message that mixes a complaint with a compliment are also hard to reduce to a single figure. Without a path back to the original verbatim, nobody knows what to fix.
What needs to be in place before starting
The exercise is worth building only once channels are identified and stable, an owner is named to arbitrate the topic categories, and volume exceeds what a careful morning of reading can absorb. It also means accepting that a share of messages will stay classified with uncertainty, rather than trying to erase that grey zone with ever more rules. Below that threshold, a shared spreadsheet and a weekly re-read do the job just as well.
Sorting runs automatically, reading stays open
We set up a pipeline that collects feedback by channel, maps it to a topic list agreed with the business, and attaches a tone to each item. The output takes the form of a ranked list of recurring frictions, each linked to the messages that illustrate it. The customer relations manager decides which ones go to committee and which can wait another month; the model sets no priority on its own. When a message covers several topics, it is attached to each of them rather than forced into a single category, so the count of irritants isn’t skewed.