Finding the right bid is usually trial and error. Bid too low, miss good traffic. Bid too high, overpay for conversions. This case study shows how one buyer used Smart Rules to systematically test bid levels and find their optimal CPM.

The Problem

A media buyer running popunder campaigns faced a common challenge:

Unclear optimal bid - Was $1.00 CPM too high? Too low?
Manual testing was slow - Changing bids, waiting for data, comparing results
Inconsistent conditions - Traffic quality varied day-to-day, making comparisons unreliable
Time-consuming monitoring - Had to check stats constantly to evaluate tests

They needed a systematic way to test bid levels under controlled conditions.

What They Did

Step 1: Created Test Structure

Instead of one campaign, they created three identical campaigns:

Campaign A: $0.80 CPM (conservative)
Campaign B: $1.00 CPM (baseline)
Campaign C: $1.20 CPM (aggressive)

Same targeting, same creatives, same landing page - only bid differed.

Step 2: Set Up Smart Rules for Monitoring

They configured Smart Rules to automatically track performance:

Rule 1: Pause if CPA exceeds target

IF cost_per_conversion > $5.00
AND conversions > 10
THEN pause campaign

This stopped overspending campaigns automatically.

Rule 2: Alert on performance milestones

IF conversions >= 50
THEN send notification

Notified when statistically significant data accumulated.

Rule 3: Scale winners automatically

IF cost_per_conversion < $3.00
AND conversions > 25
THEN increase budget by 50%

Winning bid levels got more budget without manual intervention.

Step 3: Ran the Test

All three campaigns ran simultaneously for two weeks:

Equal starting budgets
Same time period (controlled for day-of-week effects)
Smart Rules handled monitoring

Step 4: Analyzed Results

After sufficient data accumulated:

Campaign	Bid	Impressions	Conversions	CPA
A (Conservative)	$0.80	45,000	38	$4.21
B (Baseline)	$1.00	62,000	71	$3.52
C (Aggressive)	$1.20	78,000	82	$4.56

The Result

The data revealed clear insights:

$0.80 was too low - Missed quality inventory, lower conversion rate
$1.00 was optimal - Best balance of volume and efficiency
$1.20 was diminishing returns - More impressions but worse ROI

The Smart Rules had already scaled Campaign B budget by 50% during the test, recognizing it as the winner before manual analysis.

Key Takeaways

Simultaneous Testing Eliminates Variables

Running tests in parallel controls for market fluctuations. Sequential testing (week 1 at $0.80, week 2 at $1.00) introduces confounding variables.

Automation Removes Emotion

Smart Rules made objective decisions based on data. No second-guessing, no "lets give it more time" on losing variants.

Statistical Significance Matters

Waiting for 50+ conversions per variant before drawing conclusions avoided false signals from small samples.

The Optimal Bid Isnt Always Intuitive

Higher bids dont always mean better results. The $1.20 bid got more traffic but worse quality (or faced more competition for worse inventory).

Implementing This Approach

Test Design

Choose 3-4 bid levels to test
Space them meaningfully (20-30% apart)
Create identical campaigns except for bid
Set equal budgets

Smart Rules Setup

Pause rule: Stop losers before they waste budget
Alert rule: Know when data is sufficient
Scale rule: Automatically amplify winners

Analysis Framework

Compare CPA (primary metric)
Check conversion volume (sufficient scale?)
Calculate ROI if revenue data available
Consider traffic quality indicators

When to Use This Approach

Bid testing is valuable when:

Entering a new market or vertical
Launching campaigns on new traffic sources
Market conditions have changed significantly
Current performance has plateaued
You want to verify assumptions about optimal bids

Let Smart Rules do the monitoring while you focus on strategy. Data-driven bid optimization beats guesswork every time.

Case Study: A/B Testing Bid Strategies with Smart Rules Automation