7 common conversation analytics mistakes and how to fix them

Short answer

Conversation analytics projects rarely fail because of technology. They usually deliver nothing because of seven organisational and methodological mistakes: a vague question, free labels, poor data, unchecked results, treating an incomplete period as complete, insights with no owner, and reading correlation as cause.

Below, for each mistake, is how it shows up, the early sign that gives it away and the fix. The list works as a checklist before a pilot and once a quarter afterwards.

1. A vague starting question

The project starts with "let's gather customer insights". Nobody says which decision will change. Many fields get built, many charts come out, and none of them serves a specific leader.

1. 1Early signIn the report meeting people say "interesting", but no task is written down.
2. 2FixTie every field to a decision question. Switch off fields with no question.

2. Letting AI write free labels

AI writes a topic in its own words for each conversation. This month "delivery delay", next month "courier was late", then "late order". Each is counted separately and no trend appears.

1. 1Early signThe report has hundreds of small categories, the largest under 5%.
2. 2FixBuild a closed value list, keep "other", and manually add what repeats there to the list.

3. Poor or mixed data

Test conversations, staff internal notes, automatic notifications and one-message conversations get into the analysis. In call transcripts speakers get mixed up and the agent's words are read as the customer's view.

1. 1Early signResults contain phrases a customer would not say, or bot sentences.
2. 2FixExclude tests and internal notes, set a minimum number of customer messages, fill fields only from the customer's words.

4. Unchecked results

The values AI picks are never checked by hand. If a field's definition is vague, AI consistently picks wrong, and the error flows into every week's report.

1. 1Early signWhen you open the evidence conversation, the value often does not fit.
2. 2FixEach week check 30–50 results per main field, record errors, fix the definition.

5. Treating an incomplete period as complete

Analysis runs with a delay, or some conversations fail. In the report, yesterday's number looks lower than earlier days and a leader concludes "enquiries are down".

1. 1Early signThe last point on every trend chart always drops.
2. 2FixShow the coverage rate in every report and flag incomplete periods separately.

6. Insights with no owner

The analytics team sends the objection split; sales thinks it is marketing's job, marketing thinks it is sales'. The number stays the same week after week because nobody acts on it.

1. 1Early signThe same problem tops three reports in a row and no decision is recorded.
2. 2FixEvery field has one owner; under each block of the report, write the owner's name and a decision line.

7. Reading correlation as cause

Price objections went up and sales went down, so sales fell because of price. But perhaps a new campaign brought an audience that asks about price but is not ready to buy.

1. 1Early signA decision rests on one chart and no alternative explanation is discussed.
2. 2FixWrite at least one alternative explanation for every hypothesis and test it with channel, segment or CRM data.

How the mistakes reinforce each other

Each mistake may look harmless on its own, but together they create the most dangerous situation: free labels (2) go unchecked (4), an incomplete period is treated as complete (5) and the result is read as a cause (7). Such a report leads to a confident wrong decision. That is why the two most important fixes are a closed value list and regular human checks — the rest builds on them.

Which mistake to fix first

You do not need to fix all seven at once. Order matters, because some fixes are preconditions for others. First define the question and the owner (1 and 6): without them nobody knows whom the other fixes serve. Then clean the data and replace free labels with a closed list (3 and 2); this may mean re-analysing old conversations. After that set up regular checks and a coverage indicator (4 and 5). Cause-and-effect discipline (7) comes last, once the numbers are reliable — debating alternative explanations on weak data is wasted time.

A quarterly checklist

• Is every active field tied to a decision question and an owner?
• Do all fields use a closed value list, and is the share of "other" tracked?
• Are test conversations and internal notes excluded from analysis?
• Was every main field checked by hand in the last quarter?
• Is the coverage rate visible in reports?
• How many decisions based on analytics were recorded last quarter?
• Was an alternative explanation tested behind every decision?

Illustrative example

This is an illustrative example. A travel company has run conversation analytics for three months and leadership is unhappy: "nothing changed". The checklist shows only two of 14 fields have an owner, the topic field uses free labels and has produced more than 300 different values, and nobody has checked the results.

The fix takes three weeks: fields are cut to five, the topic field moves to a 12-value closed list, old conversations are re-analysed and every field gets an owner. In the next quarter the first decision is recorded: include the transfer service price in the package.

How Vexvon prevents these mistakes

In Vexvon some mistakes are closed off by design: AI picks only from the company's closed list and cannot create free labels; free-text fields are never counted; test conversations and internal notes are not analysed; the report shows the share of the period analysed; and frequently repeated phrases that fell into "other" appear in a separate list so they can be added as new values. Assigning owners, checking results and thinking through alternative explanations remain human work. More: Vexvon analytics.

Next step

Fill in the quarterly checklist this week and start with the section that has the most "no" answers. If you are just starting, a pilot plan prevents the first mistake, and analysing all conversations explains the checking model. We can audit your own project together in a demo.