Incrementality Testing & Measurement

Controlled experiments measuring true causal impact of marketing revealing 20-40% of attributed sales are non-incremental and redirecting budget accordingly.

Business Outcome
time reduction in the testing cycle
Complexity:
Medium
Time to Value:
3-6 months

Why This Matters

What It Is

Controlled experiments measuring true causal impact of marketing revealing 20-40% of attributed sales are non-incremental and redirecting budget accordingly.

Current State vs Future State Comparison

Current State

(Traditional)
  1. Marketing assumes all platform-reported conversions are incremental (wouldn't have happened without ad).
  2. No testing to validate whether ads truly caused sales or just took credit.
  3. Budget allocated based on attribution data that conflates correlation with causation.
  4. Retargeting gets huge budget despite low incrementality (users would buy anyway).
  5. No measurement of cannibalizing effect between channels.

Characteristics

  • Adjust’s InSight
  • Measured
  • Rockerbox
  • Google Optimize
  • Salesforce
  • Excel/Google Sheets
  • Statistical Software (R, Python)

Pain Points

  • Audience contamination leading to inaccurate results.
  • Complexity requiring advanced statistical knowledge.
  • High time and cost associated with running controlled experiments.
  • Challenges in integrating data from multiple channels.
  • Difficulty in ensuring true isolation of control groups.
  • Limited frequency of tests due to resource constraints.
  • Confusion between attribution models and incrementality.
  • Resource-intensive process requiring cross-departmental coordination.

Future State

(Agentic)
  1. Experiment Design Agent creates incrementality tests: geo-holdout (advertise in test markets, hold out control markets), PSA (Public Service Announcement) method (replace ads with PSAs), audience holdout (exclude random control group from targeting).
  2. Test Execution Agent runs experiments with statistical rigor.
  3. Analysis Agent measures incremental lift (sales in test vs control) and calculates true incremental ROAS.
  4. Channel Comparison reveals which channels drive highest incrementality.
  5. Budget Optimization redirects spend from low-incrementality to high-incrementality channels.

Characteristics

  • Sales data by geography and audience segment
  • Ad exposure data (who saw ads, who didn't)
  • Experimental test and control group definitions
  • Historical sales patterns for baseline comparison
  • Channel spend and impression data
  • Statistical power and sample size calculations

Benefits

  • 20-40% budget reallocation from non-incremental to incremental channels
  • Geo-holdout tests prove causal impact with 95%+ confidence
  • Reveals retargeting incrementality often 30-50% (not 100% as attribution implies)
  • Branded search incrementality testing prevents over-investment
  • Channel interaction effects measured (e.g., TV + digital synergy)
  • CFO-credible ROI measurement based on controlled experiments not correlation

Is This Right for You?

50% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

  • Applicable across multiple industries
  • Moderate expected business value
  • Time to value: 3-6 months
  • (Score based on general applicability - set preferences for personalized matching)

You might benefit from Incrementality Testing & Measurement if:

  • You're experiencing: Audience contamination leading to inaccurate results.
  • You're experiencing: Complexity requiring advanced statistical knowledge.
  • You're experiencing: High time and cost associated with running controlled experiments.

This may not be right for you if:

  • Requires human oversight for critical decision points - not fully autonomous

Related Functions

Metadata

Function ID
function-incrementality-testing-measurement