Measuring Push Incremental Lift in GetResponse Effectively

You need clear answers about how a push campaign moves the needle. This intro shows how to frame tests, avoid attribution traps, and link results to revenue and goals. It draws on methods marketers now favor as cookies fade and privacy rules tighten.

Start by defining incrementality: the extra outcomes your team drives beyond baseline behavior. Use exposed and control groups inside your audience to isolate true impact and protect against cross-campaign contamination.

Good tests match users, set exclusions, and track conversion, average order value, and revenue per user. That approach makes attribution less assumption-driven and more evidence-based.

Apply sound design and clear KPIs so you can decide whether to scale, pause, or re-target. With the right platform steps and data discipline, you’ll turn experiments into confident strategies that grow long-term return.

Key Takeaways

• Define incrementality as the extra outcome beyond baseline behavior.
• Use control vs. exposed designs to isolate the campaign impact.
• Track conversion, average order value, and revenue per user.
• Set send exclusions to prevent overlapping treatments.
• Translate test results into budget and optimization decisions.

What Incremental Lift Means for Push in Today’s Marketing Landscape

Incrementality answers the tough question: did your notification cause the sale or would the customer have bought anyway?

Incrementality measures the net change in outcomes caused by a campaign once other factors are held constant. It contrasts observed conversions with the counterfactual — what would have happened without the treatment. That difference is the core signal you need to judge true impact and revenue effects.

With third-party cookie deprecation and stricter privacy rules, tracking across advertising channels has become fragmented. That makes randomized control designs using first-party audiences more valuable than ever.

Industry examples support this. RevX highlights how attribution models often misattribute branded search clicks. Goodway Group recommends control vs. exposed tests, geo holdouts, and ghost bidding as practical, privacy‑durable methods to isolate causal impact.

• Design experiments so you compare comparable users who did or did not receive a notification.
• Focus on outcomes — conversions, average order value, and revenue deltas — rather than click counts.
• Allow enough time to capture delayed purchase behavior and normalize for seasonality.

Attribution vs. Incrementality: Understanding the Difference Before You Test

You must separate bookkeeping heuristics from scientific tests to know what a campaign truly produces.

Attribution models allocate credit by rule — last-touch, multi-touch, or a weighted scheme. Those rules can inflate reported ROAS when a final click or branded search takes credit for earlier influence.

By contrast, incrementality uses a controlled comparison to prove causality. A properly randomized exposed and control group shows the true additional revenue and conversions your campaign delivered.

Where last-touch and multi-touch fall short for ROAS

Last-touch often over-credits the final interaction. Multi-touch can mirror assumptions, not evidence. That skews decisions across marketing channels and harms budget allocation.

How lift isolates a channel’s unique contribution

Compare an exposed group to a matched control, apply exclusions, and align timing. The difference in conversion rate or revenue per user equals the causal effect.

• If exposed = 6% and control = 4%, the 2-point difference is the incremental effect.
• Multiply that delta by average order value to estimate incremental revenue.
• Use randomization and statistical checks to prove causality, not click logs alone.

Align stakeholders: treat attribution as bookkeeping and incrementality as the evidence standard for ROAS and channel decisions.

Core Test Design Concepts: Control, Exposed, and Holdouts

A robust test design starts with how you split and protect your control customers. Define a control group inside your platform audience that will be withheld from notifications so you can observe baseline behavior.

Randomize assignments to balance known and unknown factors across groups. Apply send exclusions so the same users are not exposed by overlapping campaigns. Consistent eligibility rules, frequency caps, and holdout windows keep comparisons fair over time.

• Choose KPIs that reflect business value: conversion rate, revenue per user, and average order value.
• Size groups for statistical power; larger control groups and longer measurement windows reduce noise.
• Log exposures, sends, opens, conversions, and revenue at the user level for robust analysis.

Design Element	Why it matters	Practical step
Control group	Establishes baseline behavior for fair comparison	Withhold notifications from a randomized subset of customers
Exclusions	Prevents cross‑campaign contamination	Suppress users in overlapping campaigns and apply frequency caps
KPIs	Shows value mix: response rate vs. average value	Track conversion, revenue per user, and AOV over equal time windows

Document decisions up front—split rules, holdout duration, stop conditions, and logging format. Use Bayesian or Monte Carlo checks if you need extra confidence before scaling.

Measuring Push Incremental Lift in GetResponse

Define a clear business objective and align the audience and timeframe to match how customers actually convert. Pick one primary goal—purchases or revenue per user—and set an attribution window that covers typical purchase delays.

Set your goal, audience, and timeframe

Choose an audience that represents your customer base and size control and exposed splits for statistical power. Use about 8–12% holdout as a practical starting point per RevX guidance.

Create exposed vs. control and apply send exclusions

Randomize users into a control group and an exposed group inside the platform. Apply exclusions and frequency caps so each user is treated only once during the test window.

Run the campaign consistently and collect clean data

Keep creative, timing, and eligibility identical across groups. Log sends, deliveries, opens, clicks, conversions, and revenue at the user level to ensure clean measurement data.

Calculate lift and interpret results for budget decisions

Normalize outcomes by group size and time. Compare conversion rate and revenue per user between groups to estimate incrementality, then subtract campaign delivery costs to find incremental profit.

• Report per-user and absolute figures so stakeholders see scale and efficiency.
• Use credibility checks (for example, Bayesian probability) before scaling budget.

Test Method Options You Can Adapt to Push

Pick a method that balances statistical rigor with operational simplicity for your next campaign test. Below are three practical designs you can adapt to notifications and quick to implement with user-level data.

Control vs. exposed lift study

Classic and reliable. Randomize users into a control group that receives no notification and an exposed group that gets your campaign under identical rules.

This isolates the causal difference in conversion and revenue per user while keeping creative and timing constant.

Geo holdout concepts

For regional brands, match similar markets and activate notifications in one region while holding out another.

Compare the difference in KPI trends to quantify market-level incrementality and avoid cross-region contamination.

Ghost-bidding analogs: last-moment withholds

Simulate a no-cost control by withholding the message at send time from a random subset of eligibles.

This creates an in‑flight control without extra media or ad spend and is useful when you need rapid tests.

Practical checklist

• Keep group definitions stable across time for valid comparisons.
• Validate baseline trends before launch to reduce bias.
• Ensure sample sizes are adequate for each method and track costs so small lifts translate to profit.

Method	When to use	Key advantage
Control vs. exposed	General purpose tests with user-level logs	Clean causal estimate per user
Geo holdout	Regional campaigns or limited markets	Market-level impact without individual randomization
Last-moment withhold	Fast iteration and low media cost situations	Cost-efficient control created at send time

Metrics and Math: From Conversion Uplift to Incremental Revenue

A clear math framework turns raw campaign data into actionable revenue estimates.

Start by separating two drivers: how many customers acted and how much each order was worth. That gives you the signal and the size of the effect.

Response rate and average value as twin pillars

Calculate response rate as conversions divided by total group size. Compute average value as revenue per converting customer. Together, they show whether gains come from more customers or larger orders.

Lift formula basics and normalizing groups over time

Compare per-user revenue in exposed and control groups, then scale by group size to find total incremental revenue.

Normalize all results to the same measurement window to avoid seasonality or latency bias. Align lookbacks with your buying cycle for fair comparison.

Credibility checks to reduce false positives

Use Bayesian probability-of-superiority or Monte Carlo checks and require at least a 90% threshold before declaring a win. Track variance and confidence intervals with your point estimates.

• Segment new vs. returning customers to spot concentration and diminishing returns.
• Attribute costs so incremental profit and ROAS are realistic.
• Log exposures, timestamps, and order values at the user level for reproducible analysis.

Metric	How to compute	Why it matters	Practical tip
Response rate	Conversions / group size	Shows share of customers who drove outcomes	Use the same window for all groups
Average value	Revenue / number of converters	Reveals whether order size changed	Segment by cohort to detect shifts
Per-user incremental revenue	(Exposed per-user) − (Control per-user)	Direct measure of campaign contribution	Multiply by exposed population for total impact
Credibility	Bayesian probability or confidence intervals	Reduces false positives and noisy decisions	Require ≥90% probability before scaling

For a practical workflow and automation options, see this automation guide to align campaign rules and logging with your measurement plan.

Ensuring Validity: Clean Experiments and Statistical Rigor

Keep tests pure: prevent overlapping treatments so each customer has one clear experience. Enforce exclusions early so users do not appear in multiple campaigns. Overlap creates many small permutations and biases your control groups.

Use exclusions to limit cross-campaign contamination

Apply strict suppression rules so every user maps to a single campaign path. Optimove warns that overlapping campaigns give unrepresentative samples and invalid comparisons.

Document exclusion logic and freeze other campaigns for the measurement time where possible.

Sample size, duration, and seasonality controls

Size your groups for statistical power and allow enough time to capture delayed customer behavior. Short windows inflate noise; long windows reduce responsiveness.

Align test and control timelines to avoid promotions or holidays that distort results. Monitor pre-test baselines and re-randomize if trends diverge.

Interpreting inconclusive or negative results

Treat non‑credible positive or negative outcomes as inconclusive. Iterate on hypotheses rather than forcing a decision from weak signals.

If negative results are credible, inspect frequency, timing, and creative. Then run revised tests before scaling back to campaigns that failed.

• Protect the test: freeze audience changes and annotate unavoidable shifts.
• Avoid peeking: limit interim checks to prevent bias; commit to minimum sample/time.
• Report clearly: share confidence levels, limitations, and the exact measurement logic so others can reproduce results.

Risk	Mitigation	Why it matters
Cross-campaign contamination	Strict exclusions and suppression lists	Preserves representative control groups
Seasonal distortion	Align timelines and avoid peak promotions	Prevents biased comparisons
Underpowered tests	Increase group size or lengthen time	Reduces false positives and noisy outcomes

Cross-Channel Reality: Reading Lift in a Multi-Channel Journey

Cross-channel exposure changes how users respond; a campaign that follows video or display can behave very differently than one that runs alone.

Goodway Group found that combining programmatic video with display can drive as much as a 7x revenue increase per exposed user. That shows media sequencing matters.

RevX warns that simple attribution rules often over- or under-credit channels. Use representative controls so you isolate the marginal effect each channel delivers.

Optimove shows you can value overlapping campaigns by comparing “both” versus “only” groups. Create matched control groups for users exposed to single channels and to combinations.

• Test sequences: run media first, then the campaign, to see amplification or substitution.
• Credit marginal effects: use cross-channel incrementality logic to avoid double counting revenue.
• Separate cohorts: analyze acquisition and retention customers separately—results often differ.

Report per-user revenue by group, use user-level data to trace high-performing journeys, and turn findings into simple budget rules your team can apply.

From Insights to Action: Optimization, Budget Shifts, and ROAS

Translate your experiment findings into clear rules for scaling, pausing, or reallocating spend.

When to scale and when to reallocate to other channels

Scale when credible incrementality clears your ROAS and profit thresholds. Use per-user and per-customer incremental revenue to rank segments where a campaign drives the most sales value.

Reallocate when results are neutral or negative versus alternate marketing efforts. RevX’s guidance favors moving budget away from assumed wins and toward proven impact.

Creative, timing, and audience strategies informed by test results

Optimize creative based on what moved the needle: higher response rates require different messaging than bigger order values. Adjust cadence and dayparts where curves show peak user response.

• Prioritize targets and users with the highest revenue per cost.
• Double down on groups that show sustained positive lift; exclude segments that cannibalize organic sales.
• Build a testing roadmap: offer depth, urgency, and personalization as staged hypotheses.

Governance tip: require a credibility threshold before scaling and share outcomes with finance so your budget rules reflect true business contribution, not clicks alone.

Conclusion

Turn test outcomes into action: use clear rules to move budget and optimize campaigns so your marketing drives profit, not just activity.

Focus on control-based tests and representative groups to separate observed revenue from true incremental revenue. Pair that evidence with clean exclusions, adequate timing, and solid user-level data.

Attribution helps bookkeeping, but incrementality and credible lift give you the defensible signal for acquisition and retention choices. Share concise summaries with stakeholders so customers and business leaders see the real effect on sales.

Operationalize what worked, retire approaches that did not, and schedule the next test. That cycle will improve results, protect revenue, and make your platform experiments a repeatable growth lever for marketing.

FAQ

What does “incremental lift” mean for push notifications and why does it matter now?

Incremental lift measures the real additional value a push campaign generates compared to what would have happened without it. With cookie loss and tighter privacy rules, direct attribution is harder. Lift studies give you a clearer view of a channel’s unique contribution to conversions and revenue, helping you allocate budget and optimize ROAS.

How is incrementality different from standard attribution like last-touch or multi-touch?

Attribution models assign credit based on user touchpoints, which often double-count or miss influence across channels. Incrementality isolates the causal effect by comparing exposed users to a control group. That tells you what actions and revenue are truly driven by the push campaign rather than coincident user behavior.

How should I define a control group for push audiences on GetResponse?

Create a randomized holdout from your target audience that receives no push sends during the test window. Exclude users who are in active experiments or targeted by overlapping campaigns to avoid contamination. Ensure the control mirrors exposed segments on key demographics and past behavior.

What exclusions and randomization rules prevent campaign overlap and bias?

Use strict send-exclusion lists for users in other active experiments, recent converters, and those targeted by concurrent channels. Randomize assignment at the user ID level, not by device or cookie, to keep groups independent. Lock assignments for the test duration to avoid crossover.

Which KPIs should I choose for a push lift test?

Pick KPIs tied to business goals: conversion rate, incremental revenue, average order value, and lifetime value when possible. Include engagement metrics like open or click rate as secondary signals, but prioritize revenue or conversions for budget decisions.

What are the practical steps to run a push lift study in GetResponse?

Set a clear goal, define the audience and timeframe, and split users into exposed and control cohorts. Apply send exclusions, run the campaign consistently across the test window, and collect clean, de-duplicated data. After the test, compute difference-in-differences to estimate net effect and statistical significance.

How do I calculate lift and normalize results across groups?

Calculate the conversion or revenue rate for exposed and control groups, subtract control from exposed, and express the result as a percentage of the control. Normalize for group size and time—use per-user metrics or per-period rates to compare results fairly.

What sample size and duration are needed for credible results?

Sample size depends on expected effect size and baseline conversion rate. Small effects require larger samples and longer duration. Aim to capture normal weekly seasonality and avoid holiday spikes. Use power calculations up front to set minimum sample and run-time thresholds.

How do I interpret inconclusive or negative test outcomes?

Inconclusive results mean you don’t have enough evidence to confirm a causal effect—either increase sample size or extend the test. Negative lift indicates the campaign reduced desired outcomes; pause or analyze creative, timing, audience overlap, and measurement errors before scaling.

What test designs can I adapt for push beyond simple control vs. exposed?

Consider geo holdouts for regional rollouts, staggered starts to manage media pacing, or ghost-bid analogs where you intentionally withhold sends for a subset to measure last-moment effects. Each design trades off operational complexity, contamination risk, and statistical power.

Which credibility checks reduce false positives in lift studies?

Run pre-test balance checks on key metrics, verify no pre-period differences, test for cross-contamination, and apply significance thresholds and confidence intervals. Also replicate tests across segments or time windows to confirm repeatability.

How do multi-channel journeys affect how I read push test results?

Users interact across channels, so a push may assist conversions later attributed elsewhere. Lift measures net impact regardless of credit assignment, but you should also run cross-channel experiments or use sequence-aware analyses to understand interaction effects and inform channel strategy.

When should I scale push and when reallocate budget to other channels?

Scale when tests show positive, statistically significant incremental revenue and a favorable cost-per-acquisition relative to alternatives. Reallocate when lift is negligible or negative, or when other channels show higher incremental ROAS. Use test results to prioritize channels by marginal return.

What creative, timing, and audience strategies should I test after an experiment?

Use lift results to iterate: A/B test creatives and call-to-action, vary send windows and cadence, and refine audience segments by recency, frequency, or value. Focus on audience strategies that increase conversion probability while controlling for fatigue and overlap.

How do I ensure data cleanliness and avoid attribution leakage during tests?

De-duplicate events, align event windows across platforms, maintain consistent user IDs, and enforce exclusion rules for concurrent campaigns. Audit tracking implementation and use server-side logs to validate client-side signals for a single source of truth.

What math should I use to translate conversion uplift into incremental revenue?

Multiply the per-user incremental conversion rate by average order value and the exposed population size to estimate incremental revenue. Adjust for returns and refunds, and annualize if you aim to estimate longer-term value like LTV.

How often should I run lift tests to keep marketing decisions data-driven?

Run frequent, focused tests when you change creative, audience, or budget levels—monthly or quarterly depending on traffic. Maintain a cadence that balances learning velocity with statistical rigor and avoids overwhelming users with tests.