Guides
Clear answers to one question, from choosing tools to the metrics that matter, for turning customer, business and market data into decisions.
All 24 guides
The Growth Data Your AI Agent Needs Doesn’t Exist Yet
Why AI agents can’t find the judgments growth decisions depend on, and how to create them once as data every team can use.
Choose a Growth Data Platform That Turns Signals Into Action
Compare five buying criteria for turning continuously enriched customer and business data into decisions and agents that act.
Connect ChatGPT or Claude to Your Customer Conversations
Three ways to set it up, and which questions each one answers reliably: lookups, counts, judgments or trends.
Turn Recurring Questions Into Agents That Act
When a dashboard is enough, when to set up an agent instead, and how answers on churn, deals and demand reach the right owner every week.
Move QA From a 2% Sample to Every Conversation
Let AI score each conversation against your rubric, and put reviewers on calibration, coaching and root cause.
Turn Buyer Intelligence Into Pipeline and Win Rate
Read buyer conversations next to CRM, win/loss, competitor and market data to find emerging demand, fix positioning that’s losing ground and measure the result.
Customer Insights Tools for Support, Compared (2026)
Rippit, Enterpret, Chattermill, SentiSum, helpdesk AI and a DIY build, matched to what you need conversations to become.
Find the Real Reasons Customers Contact Support
Classify conversations instead of trusting tags, mine the “Other” bucket, and rank causes by revenue touched.
Health Scores vs. Conversation Signals for Churn
What each one sees, how to backtest conversation signals, and how to show both beside the account.
Measure What Your AI Agent Actually Resolves
Find the bot conversations that weren’t truly resolved, including customers who came back on another channel, and see which accounts need attention.
Hear What Customers Want Without Sending a Survey
Pull the voice of the customer from calls, chats and reviews, and test each request for repeat, spread, growth and weight.
Zendesk QA or a QA Agent on All Your Conversations?
Both cover every ticket now. Decide by scope: whether QA is the whole job, or one use of the same data.
Test Any Feedback Tool for Coverage Before You Switch
Three coverage checks, seven more trial questions and a worksheet for comparing Enterpret with the alternatives.
SentiSum Alternatives: Tags on the Ticket or Data for Every Team?
Decide where your conversation analysis should live, with one test question you couldn’t answer last month.
Catch the Wrong Answers Your AI Agent Gives Customers
Five checkable hallucination types, the signals that find them and the source behind each, with the accounts, revenue and compliance exposure they touch.
The Churn Signals Hiding in Tickets and Calls
Five signal families, a severity-and-persistence score, and what to do when a champion leaves.
What Breaks When You Export Tickets to ChatGPT
Capacity, then consistency, then traceability: where a CSV export stops working as volume grows.
Why AI Assistants Sample, and When It Changes the Answer
A sample shows what a pattern looks like, not how common it is. The math, and the questions that survive it.
Find the Complaints Your Tags Miss
Read every ticket, call and AI-agent chat against five complaint signals, confirm them in billing and account data, and let a trained reviewer decide.
Build a Scorecard for Your AI Support Agent
Score intent, grounding, policy, effort and handoff, then see what bot failures do to retention.
Compliance QA or Compliance Monitoring: Which Do You Need?
One checks defined criteria, the other watches for emerging risk. How to design each, and where coverage fits.
Watch Every Fintech Support Conversation for Compliance Risk
AI covers every call, chat and ticket and flags exceptions with evidence; your compliance team keeps the judgment.
Why Containment Overstates Your Bot’s Success
Contained isn’t resolved. Five metrics that show whether customers actually got an answer.
How Many Reviews Does It Take to Catch a 1% Issue?
The math behind QA samples: 299 random reviews to see a 1% issue once, and 1,521 to measure how often it happens.
See what you can build
Every guide here ends in something you can run. Start from a ready-made agent, or describe your own and Rippit builds it.
