kliqbot

Better data in. Better decisions out.

Stop telling Google Ads that junk calls are conversions.

Google optimizes toward the outcomes it is told to treat as success.

Weak outcomes can push a campaign in the wrong direction.

Kliqbot classifies what happened. Weak outcomes stay out of conversion data. Booked appointments can become stronger conversion signals. Campaign actions come from accumulated evidence — not from treating every ring as a win.

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Signal-quality pipeline

Illustrative signal flow

Raw campaign activity is classified before conversion signals are used. Short calls under 30 seconds are excluded. Repeat callers are deduplicated. Spam and tire-kicker calls are excluded. Genuine leads become useful lead signals. Booked appointments become higher-value conversions. Classified evidence feeds campaign actions such as negative-keyword exclusions, keyword pruning, search-term promotion, segment adjustments, and bidding-stage progression.

Campaign evidence → possible actions

  • Negative-keyword action
  • Keyword pruning
  • Search-term promotion
  • Segment adjustment
  • Bidding-stage progression

Classified evidence feeds an action layer. Not every call triggers every action — patterns accumulate, then specific changes are made.

Clean the feedback loop

A 12-second wrong number is not the same outcome as a booked job.

Bad calls can push a campaign in the wrong direction. When every ring is labeled a conversion, the campaign cannot tell waste from demand.

Calls under 30 seconds do not count as conversions. Repeat callers are deduplicated. Spam and tire-kicker calls stay out of conversion data. Booked appointments can be valued more strongly than a generic call.

Feedback distortion → classified picture

Illustrative comparison

Before

Unclassified signal

Every call looks like a conversion.

The label says “success” even when the call was spam, a duplicate, or the wrong service.

  • 12 sec wrong number

    Call duration ignored

    Conversion

  • Repeat caller

    Caller history ignored

    Conversion

  • Spam inquiry

    Call intent ignored

    Conversion

  • Wrong-service inquiry

    Service mismatch ignored

    Conversion

  • Ready-to-book caller

    Buying intent ignored

    Conversion

What the campaign learns

Every ring = a successful outcome

After

Classified signal

The campaign sees what actually happened.

Noise is removed, genuine demand is retained, and booked work gets the strongest signal.

  • 12 sec wrong numberExcluded

    Under 30 seconds

  • Repeat callerDeduplicated

    Same caller window

  • Spam inquiryExcluded

    Classified spam

  • Wrong-service inquiryExcluded

    Negative-keyword evidence when applicable

  • Ready-to-book callerLead

    Useful conversion input

  • Booked appointmentHigher-value conversion

    Stronger business outcome

What the campaign learns

Waste ≠ lead ≠ booked appointment

Result
Short calls filteredRepeat calls deduplicatedJunk calls classifiedBooked appointments valued more strongly

Signal-weight ladder

Relative strength — not fixed multipliers

  1. Genuine lead

    Baseline

    Baseline useful signal after junk is filtered.

    Counted as a conversion when the call is a real prospect.

  2. Booked appointment

    Higher-value

    Reported as a separate, higher-value outcome.

    Stronger than a generic call when booking is detected.

  3. Job value when supported

    Estimated from available conversation evidence.

    Only when the call provides enough information — not invented.

  4. Customer value inputs

    Average customer value and purchase frequency can affect conversion values.

    Owner-supplied inputs shape how outcomes are valued over time.

Give stronger outcomes more weight

A booked appointment should not look the same as a generic call.

After junk is filtered, remaining outcomes still differ. Kliqbot can report a genuine lead as a useful conversion, and a booked appointment as a separate, stronger signal when that outcome is detected.

When conversation evidence supports it, job value can be estimated. Owner-supplied average customer value and purchase frequency can further shape conversion values. Not every call carries enough information for every layer — only supported signals move up the ladder.

Conversion delivery integrity

Privacy-safe matching improves the connection between lead outcomes and the ad interactions that produced them.

  1. Ad interaction

  2. Privacy-safe match

  3. Conversion upload

  4. Delivery confirmed

If upload fails

Upload failedRetry

Failed conversion uploads are retried automatically rather than silently disappearing from the feedback loop.

Turn evidence into campaign action

Cleaner signals become specific changes — not vague “AI optimization.”

Kliqbot mines patterns from campaign evidence, then acts where the evidence supports a change: exclude waste, pause weak keywords, promote converting terms, adjust segments inside guardrails, and mature bidding strategy when enough reliable data exists.

Operating loop

  1. Observe
  2. Classify
  3. Act
  4. Measure again

Evidence-to-action ledger

Example decision structure

  • Evidence

    Similar irrelevant phrases recur across search terms.

    Search-term mining

    Action

    Turn the pattern into negative-keyword exclusions.

    Control

    Waste-reduction action

  • Evidence

    A keyword continues spending without useful outcomes.

    Keyword pruning

    Action

    Pause the keyword when the applicable evidence threshold is reached.

    Control

    Waste reduction

  • Evidence

    A search term repeatedly produces useful outcomes.

    Search-term promotion

    Action

    Promote it into a dedicated keyword with clearer control.

    Control

    Campaign action inside applicable rules

  • Evidence

    Useful outcomes differ by geography, time, device, or other supported segment.

    Segment adjustment

    Action

    Adjust bids inside defined guardrails.

    Control

    Guardrailed

  • Evidence

    Enough reliable conversion and value data has accumulated.

    Bidding-stage progression

    Action

    Traffic collection → lead-cost optimization → value-based optimization.

    Control

    Progression based on sufficient data

ObserveClassifyActMeasure again

Launch → forget → increase the budget

Bidding-stage progression depends on sufficient reliable data — not a fixed calendar.

Automation with a receipt

Optimization should never mean surrendering control of the money.

Safe waste-reduction can run automatically. Routine bid adjustments stay inside owner-set guardrails. Budget increases and other spend-increasing actions wait for a person. The campaign can keep working without becoming a black box.

Action-risk hierarchy

  1. Automatic

    Automatic

    Safe waste reduction and read-only actions

    • · Adding negative exclusions
    • · Pausing qualifying waste
    • · Syncing or reading performance information

  2. Inside guardrails

    Guardrailed

    Routine adjustments inside owner-set limits

    • · Applicable bid adjustments within your limits

  3. Approval required

    Human approval

    Budget increases and other spend-increasing actions

    • · Budget increases
    • · Other spend-increasing changes

Decision receipt

Example decision structure

Action

Add negative-keyword pattern

Why
Repeated irrelevant service searches were detected.
Before
Eligible to match
After
Excluded from future matching
Estimated cost impact
Calculated from account evidence when available
Status
ExecutedVerified

Controlled action flow

  1. Propose

    Proposed

  2. Approve

    Awaiting approval

  3. Execute

    Executes after approval

  4. Verify

    Verified

Stale proposals expire. Manual edits are not silently overridden.

Budget increase proposed

Status: awaiting owner approval

Spend-increasing actions do not silently advance.

  • Clean the conversion data.
  • Report stronger outcomes.
  • Remove repeated waste.
  • Change the campaign from evidence.
  • Keep control of the spend.
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Command starts at $397/month. Google Ads spend is separate and paid directly to Google.

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