How to read what aVtonomus recommends

Do not start from tables and numbers. Start from what aVtonomus suggests doing.

See what aVtonomus finds

What we found

People search for a specific service

Why it matters

The site has no separate page

What to do

Add a page and a clear step

What a useful recommendation is made of

  • What we found

    The opening or the problem itself. For example: people already search for one of your products separately.

  • What this means for you

    Why the finding deserves attention at all.

  • What to do

    One main action.

  • What it can give

    The most important block. It answers: why should I do this?

  • Why we decided this

    The basis: demand, competitors, other available signals and facts about your business.

What the confidence level means

  • High: the basis is strong and there are few important unknowns.
  • Medium: the idea looks reasonable, but the decision depends on extra information.
  • Low: this is a hypothesis that is better to check first.

What we found

People search for a specific service

Why it matters

The site has no separate page

What to do

Add a page and a clear step

How the difference looks

Opportunity

There is already interest in a separate product.

Now

A weak result would sound like: “Query — 60. Create a URL.”

What to do

A useful result: make the offer more visible so people who already need the product can find you more easily.

If a recommendation is confusing

  1. 01

    If aVtonomus asks a question

    Answer it. Sometimes one business fact matters more than another thousand queries.

  2. 02

    If the recommendation does not fit you

    Say so. For example: “We do not want to develop this direction.” The system should take the business reality into account.

Common questions

Where should I start reading the result?

From what it suggests doing. Numbers are evidence, not the first screen.

What if confidence is medium or low?

First clarify the unknown or check the hypothesis. Do not treat a guess as a fact.

Want to see this on your own data?

The first analysis shows a few important recommendations, not a huge table.

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