Your customers are telling you what is wrong.
Every day. In calls, emails, reviews and chats. And nobody has time to read any of it.
You already have the answers.
Your systems already keep all of it. Calls are recorded and turned into text. Emails stay in the mailbox. Reviews arrive. Chat logs are saved. This part works, and it has worked for years.
Then it sits there. Reading all of it is nobody’s job. So what you know about your customers comes from the few pieces someone had time to read — and from the people who complained loudest.
- You do not know why people really contact you. Only what the last angry customer said.
- Your team promises to check things and reply. Nobody counts those promises.
- The same questions come again and again. The answers are not in your system.
- Some customers do not come back. You do not know why.
One company decided to listen.
They began with the hardest channel: the telephone. Seven thousand calls a year, recorded, written down, and never read. The same method reads email, chat and reviews — text is text.
They did not want a dashboard. They wanted three answers. Three questions any business can ask its own customers:
Why do people call us? What do we promise them? What can we not answer? Everything else was decoration. They had one condition about data. We agreed in the first meeting.
No pilot project. No workshop.
We tested a small sample first, so we knew the method worked. Then we put the whole proposal on one page.
They asked what we would find. We said: we do not know yet. That is why we should look. They agreed.
Eight months of calls, read completely.
463 hours. Three answers they did not have before.
51%
Most callers were customers they already had.
More than half of all calls came from people who had already bought. They were asking what happens next. New enquiries were one call in seven. The phone line was not winning business. It was reassuring people who had already chosen them.
11,632
Promises to call back.
Agents promised to check something and reply. Eleven thousand times in eight months. Only one call in eight was finished while the customer was still on the phone. Nobody was counting these promises.
3,463
The company did not have the answer.
An agent looked for information — the pet rules, the local contact, who approves a refund — and the company did not have it written down anywhere. This is not a people problem. It is a missing information problem.
Other numbers tell you how your team works. This one tells you what to fix.
Three answers. Three things to do.
A finding you cannot act on is only interesting. Each of these becomes work.
Answer people before they ask.
If most people want to know what happens next, put that answer where they look first. The same customers, fewer messages, less waiting.
Keep every promise you make.
Every promise to check something and reply is now a row in a database. You can count them, give them to someone, and close them. Nothing depends on memory.
Write down what you keep failing to answer.
The missing answers become a list, ordered by how often each one costs you a reply. Write each answer down once and that question stops coming back.
Example
One conversation in. One row out.
This is what every call, email or review would become. The examples below come from one industry and one channel. The fields are the same for any.
Villa Elysion · Hypatia Andronikos Guests smell gas. They left the house.
- Priority
- P1 · Critical
- Send to
- Product team — now
- Topic
- Safety
- Mood
- worried
- Risk
- high
- Escalate
- yes
- Result
- Escalated
- They asked
- Is the house safe tonight?
- Can we move to another house?
- We promised
- Call the local team now
- Reply to the guests within one hour
- Missing
- No emergency contact for this region after office hours.
Villa Arcadia · Cassandra Doulos Building work next door. The family wants to leave.
- Priority
- P1 · Critical
- Send to
- Product team — now
- Topic
- House condition
- Mood
- angry
- Risk
- high
- Escalate
- yes
- Result
- Follow-up promised
- They asked
- Will we get money back?
- Can we move to another house?
- We promised
- Ask the owner about the building work
- Offer another house today
- Missing
- No record of building work near this house.
Casa Hyperion · Theron Kallias No internet for two days after a storm.
- Priority
- P2 · High
- Send to
- Product team
- Topic
- Internet
- Mood
- frustrated
- Risk
- medium
- Escalate
- yes
- Result
- Escalated
- They asked
- When will the internet work?
- Can we get part of the money back?
- We promised
- Open a case with the owner
- Tell the guests directly
- Missing
- Agent could not see repair status for the house.
Villa Kalliope · Penelope Stavros Can they bring a dog? They travel in three weeks.
- Priority
- P3 · Medium
- Send to
- Customer service
- Topic
- Booking change
- Mood
- calm
- Risk
- low
- Escalate
- no
- Result
- Follow-up promised
- They asked
- Does this house allow dogs?
- Is there an extra cost?
- We promised
- Ask the owner about pets
- Send confirmation by email today
- Missing
- Pet rules are not saved for each house.
Villa Thalassa · Ariadne Nikolaou An invoice for a booking that is already paid.
- Priority
- P2 · High
- Send to
- Customer service
- Topic
- Invoice
- Mood
- confused
- Risk
- medium
- Escalate
- yes
- Result
- Follow-up promised
- They asked
- What is this amount for?
- Did you receive my payment?
- We promised
- Check the invoice with finance
- Call back after checking
- Missing
- Agent could not see the payment status for this booking.
All names from Greek mythology.
Pick a conversation, or paste your own. Personal details are removed in your browser before anything is sent. Nothing you type is stored.
What are your customers telling you?
Your answers are different from theirs. But they are already written down somewhere — a recording, a mailbox, a review page — and nobody has read them yet.
We will tell you what they are.
AngryRobot Labs · Conversation intelligence
Common questions
- Can this be done without our customer data leaving our systems?
- Yes. Personal details are removed on your own infrastructure, before any analysis begins. What gets analysed contains no names, no contact details and no reference numbers. Understanding why someone contacted you, how the conversation went and what was promised does not require knowing who was speaking.
- How do we know the anonymisation actually worked?
- Because it is measured against your own records, and you see the result before the work goes ahead. We test the process on a large sample of your real data, report how much came back completely clean, and withhold anything that does not pass the check. That number is yours to review, and the work does not proceed until you are satisfied with it.
- What does this find that our own quality reviews do not?
- Patterns that only exist across everything. Reviewing a sample tells you how individual people performed. Reading everything tells you what your customers are collectively asking for, what your business keeps promising them, and which questions your own records cannot answer. Those are different questions, and a sample cannot answer them at any size.
- How can a business actually use this?
- Three kinds of work. Deflection: if most people ask the same question about what happens after they buy, publishing that answer where they look first removes the contact without removing the service. Accountability: every promise to check something and reply becomes a record that can be counted, assigned and closed, so following up stops depending on someone's memory. Correction: every occasion where your team could not find information you should have documented becomes a list, ordered by how often each gap costs you a reply. Write each answer down once and that question stops coming back. Anything that cannot be turned into one of those three is interesting rather than useful.
- What is the single most useful thing it finds?
- The questions your own systems cannot answer. Every other measure describes how your team is performing, which is useful but not directly actionable. A ranked list of what your own records fail to answer is a work queue, and it is built from conversations you have already paid to record.
- Does this only work for phone calls?
- No. It works wherever your customers speak or write to you: calls, emails, chat transcripts, reviews, support tickets. Recorded conversations are usually the hardest starting point, because they have to be turned into text first. Anything already written is more straightforward.
- What do we need to provide, and how long does it take?
- Access to your existing records and someone who can answer questions about your business. No new software, no change to how your team works, nothing to install. We agree the scope and the price in writing before anything starts. There is no pilot phase and no discovery retainer.
- 7,011
- Calls analysed
- 463
- Hours of conversation
- 99.8%
- Clean in the anonymisation check
Anonymised on your infrastructure · Every record read · Results in your database