I’ve spent years nudging teams away from reactive content creation and toward using the richest source of customer truth most companies already have: support transcripts. If you’re running a help desk — whether that’s Zendesk, Intercom, Freshdesk, or a custom chat queue — those conversations are full of repeat questions, feature gaps and phrasing that tells you how customers think. Turned into a simple NLP workflow, they become a measurable idea pipeline for product content, search-focused articles, onboarding flows and even product roadmap signals.
Why support transcripts beat other idea sources
Marketing teams rely on keyword tools, competitive research and social listening. Those are useful, but support transcripts are different. They contain:
So the question I ask is simple: how do we turn hundreds or thousands of chat and ticket logs into measurable content ideas that we can test and track? Below is a pragmatic, low-friction NLP workflow I’ve implemented with teams of different sizes.
Workflow overview — from raw transcript to measurable content
The workflow has five stages. I keep it intentionally simple so it fits inside a sprint or a monthly content planning cycle:
Collect and anonymize
Export your chat/ticket history. Most platforms let you pull CSV or JSON exports. If you’re using Zendesk or Intercom, you can automate this with their APIs. Two non-negotiables here:
Preprocess and normalize
Raw conversation text is messy. Minimal preprocessing makes NLP much more accurate. My go-to steps:
I usually use Python + spaCy or Hugging Face tokenizers for this stage. If you don’t want code, some no-code platforms (e.g., MonkeyLearn, AssemblyAI) handle these steps with simple pipelines.
Extract intents, topics and sentiment
This is where the magic happens. I combine a few lightweight NLP techniques so results are interpretable and actionable:
For many teams I’ve worked with, combining a pretrained model (OpenAI/GPT embeddings or Hugging Face sentence-transformers) plus a cheap clustering algorithm (KMeans or HDBSCAN) gives excellent topic groups with minimal labeling.
Cluster, enrich and prioritise
Once you have intents and topics, cluster transcripts into coherent groups and enrich them by metadata.
In practice I use a simple prioritisation formula: Priority = Frequency_rank + 2*Frustration_rank + Revenue_rank. Weight frustration higher because reducing costly escalations is often the best ROI.
Map clusters to content with measurable KPIs
Not every insight should become a blog post. Match the cluster to the right content format and define a metric to measure impact.
Example mapping table
| Cluster | Suggested Content | Primary KPI | Implementation Effort |
|---|---|---|---|
| “Email not syncing” | KB article + 90s video | Tickets/month for “email” intent | Low |
| “Billing charge confusion” | Billing FAQ + UI tooltip | CSAT and billing dispute rate | Medium |
| “Setup for enterprise SSO” | Detailed guide + template support script | Time-to-onboard and enterprise churn | High |
Make it measurable — sample tracking plan
Define dashboards before you publish content. Track both content-specific and downstream support metrics:
I like to set an initial hypothesis for each content piece, for example: “Publishing a 2-minute explainer will reduce 'email not syncing' tickets by 25% in 60 days.” Then we measure and iterate.
Practical tips and tooling
Some practical choices that make this easier in real teams:
Real-world win
At one startup I worked with, a single 5-minute video on a setup flow eliminated a recurring ticket cluster that accounted for 18% of inbound chats and had below-average CSAT. We created the asset, added an in-app CTA on the setup page and linked it from the KB. Within two months tickets for that intent dropped by 42% and NPS in onboarding rose by 6 points. The content paid for itself in saved support hours and happier customers.
Turning transcripts into content ideas doesn’t require a datascience team. It requires a repeatable process, sensible NLP tools, and a focus on measurable outcomes. Once you start measuring the support impact of content, the investment in content planning becomes clearer: fewer repetitive tickets, faster onboarding, and content that actually reduces friction for real users.
