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Alex Li
32 min read

Measure Abandoned Cart Incrementality: Step-by-Step

Step-by-step guide to measuring abandoned cart incrementality with a user-level Klaviyo holdout—compute incremental recovered revenue, windows, and leadership visuals.

Measure Abandoned Cart Incrementality: Step-by-Step

Cart recovery emails often look amazing in platform dashboards, yet leadership still asks a fair question: how much of that revenue would have happened anyway? This guide gives you a pragmatic, defensible path to measure the true lift from your abandoned cart emails in Klaviyo using a persistent user-level holdout, then turn the results into a crisp executive readout.

Key takeaways

  • Use a user-level persistent holdout for 14–28 days so each eligible abandoner is either always treated or always withheld during the test window.

  • Set your north-star KPI to Incremental Recovered Revenue and back it with a small set of supporting metrics like recovered orders and conversion lift.

  • Define windows by the first qualifying abandon per user and analyze at 28 days with sensitivity checks at 7 and 35 days.

  • Enforce clean suppression across email, SMS, on-site prompts, and paid retargeting to avoid contamination.

  • Package results for leadership with three visuals: revenue per user with confidence intervals, a cumulative incremental curve, and a funnel snapshot.

Design the test for abandoned cart incrementality

Your objective is to isolate the incremental effect of email cart recovery on abandoners. The most credible design is a persistent user-level holdout: randomly assign 5–10% of eligible abandoners to never receive recovery emails for the duration of the test window, while everyone else follows your standard flow.

Why persistent holdouts? Because they measure cumulative, long-run impact without the interference of rapid on/off toggling. Both experimentation leaders emphasize this approach for trustworthy reads of aggregate impact over time, rather than stitching together many one-off tests. For background, see the perspective on holdouts from GrowthBook in R&D contexts and Eppo’s view on cumulative impact measurement in modern product growth.

Your north-star KPI is Incremental Recovered Revenue. In plain terms:

Incremental Recovered Revenue = Revenue_test − Revenue_holdout

Compute it over the same cohort, windows, and inclusion rules, net of returns where you can measure them. You’ll also track supporting KPIs like net lift in abandoner conversion rate, recovered orders, revenue per user, AOV, and a profit line that subtracts discounts and messaging costs.

According to Baymard’s multi-year synthesis, average cart abandonment hovers around 70% across industries, which underscores the high ceiling for recovery—but not all recovered revenue is incremental. The point of this design is to separate signal from the inevitable baseline of buyers who would have returned regardless.

  • External references: GrowthBook’s holdout overview and Eppo’s cumulative impact discussion offer methodological context; Baymard provides the abandonment baseline.

Set windows and cohorting rules

Anchor all analysis to the user’s first qualifying abandon during the test period and lock that user’s assignment for the window. Choose one primary window and two sensitivity reads.

Window

What it captures

When to use

7 days

Fast-cycle purchases, immediate recovery effects

Early read and sensitivity

28 days

Durable ecommerce effect without long tail noise

Primary reporting window

35 days

Late conversions and discount-driven stragglers

Sensitivity only

Rules to keep your math clean: treat only the first abandon per user during the test period; suppress subsequent abandons for measurement to avoid double counting. Attribute orders back to that first abandon if they fall within the active window and meet your attribution model settings. When you later share numbers with leadership, show the 28-day result first and footnote the 7- and 35-day reads to demonstrate stability.

Implement holdouts and suppression in Klaviyo

Follow these steps to enforce a clean, user-level persistent holdout and prevent cross-channel contamination:

  1. Create the holdout assignment. If available and appropriate, configure Klaviyo’s global holdout group with 5–10% of profiles and set your test dates. Alternatively, build deterministic assignment by writing a profile property such as holdout_group using a consistent hash on email or customer_id via the Profiles API. Deterministic assignment ensures flows and campaigns treat the same people consistently.

  2. Enforce in flows and campaigns. Add a flow-level split or filter that ends the journey for holdouts, and add the holdout segment to Don’t send to when building campaigns. Verify no overrides accidentally re-include holdout users.

  3. Define the eligible abandoner cohort. Use your ecommerce platform’s abandon events and dedupe to the first qualifying abandon per user during the test period.

  4. Suppress cross-channel recovery. Exclude the holdout segment from SMS, block on-site promos for those users, and upload a negative audience to paid retargeting platforms to avoid exposure that would blur your read on email.

  5. Document attribution settings. Record lookback windows and last-touch rules in Klaviyo’s attribution settings so your analysis is reproducible.

  6. Pre-launch balance checks. Confirm treatment and holdout are balanced on new vs returning, device, and top geos. If imbalance appears, revisit assignment.

  7. Monitoring plan. Track daily eligible vs sent counts, deliverability for the treated group, and any accidental exposure of holdouts across channels.

Helpful documentation you can reference as you configure: Klaviyo’s guide to global holdout groups, developer docs for the Profiles API and custom properties, and the overview of message attribution and lookback settings. For methodological grounding on why persistent holdouts beat short A/B toggles, see GrowthBook and Eppo.

Verify and monitor without bias

Before launch, smoke test suppressions by inserting a small batch of test profiles into both groups and proving that holdouts receive no recovery emails or SMS. On day one, validate counting logic by reconciling eligible abandoners, emails sent, and orders booked. Keep a simple audit log noting any promo spikes, deliverability swings, or inventory outages that could skew behavior. During the run, watch for sudden drops in open or click rate that suggest inboxing issues, and confirm holdout audience files remain active in paid platforms. After the window closes, lock the dataset and note any exceptions or leakages in a footnote for leadership.

Analyze the results and compute KPIs

Start with the core revenue join and cohorting logic. Join the first qualifying abandon to orders within the chosen window and compute metrics at the user level.

-- Example pattern: first abandon per user → orders within 28 days
  WITH first_abandon AS (
    SELECT
      user_id,
      MIN(abandon_ts) AS first_abandon_ts
    FROM raw_abandon_events
    WHERE test_period = TRUE
    GROUP BY 1
  ),
  orders_in_window AS (
    SELECT o.user_id,
           o.order_id,
           o.order_ts,
           o.net_revenue -- net of returns if available
    FROM raw_orders o
    JOIN first_abandon fa
      ON o.user_id = fa.user_id
     AND o.order_ts BETWEEN fa.first_abandon_ts AND fa.first_abandon_ts + INTERVAL '28 days'
  )
  SELECT
    u.assignment AS group,              -- 'test' or 'holdout'
    COUNT(DISTINCT u.user_id) AS users,
    COUNT(DISTINCT o.order_id) AS recovered_orders,
    SUM(COALESCE(o.net_revenue,0)) AS revenue,
    SUM(COALESCE(o.net_revenue,0)) / NULLIF(COUNT(DISTINCT u.user_id),0) AS revenue_per_user,
    COUNT(DISTINCT CASE WHEN o.order_id IS NOT NULL THEN u.user_id END) * 1.0 / COUNT(DISTINCT u.user_id) AS conversion_rate
  FROM eligible_abandoners u
  LEFT JOIN orders_in_window o
    ON o.user_id = u.user_id
  GROUP BY 1;

Compute Incremental Recovered Revenue and conversion lift from these aggregates. Then subtract direct costs to frame incremental profit.

IRR = Revenue_test − Revenue_holdout
  Profit = IRR − Discounts_redeemed − Messaging_platform_costs − Ops_time_cost
  Net conversion lift = (CR_test − CR_holdout) / CR_holdout

To plan power or to put error bars on your visuals, you can use standard large-sample approximations. For revenue per user differences, use a two-sample t-interval; for conversion rates, use a proportion lift interval. If you don’t have an internal calculator handy, Evan Miller’s sample size and confidence interval calculators are reliable for planning detectable effects and gauging uncertainty.

As context, Klaviyo’s public benchmarks show abandoned cart automations tend to drive high revenue per recipient relative to broadcast emails; your holdout test will reveal how much of that is truly incremental in your specific funnel and audience.

Report to leadership with three visuals

Your executive readout should fit on one page. Use these visuals and a short, no-jargon narrative that ties back to revenue and profit.

  • Bar chart of revenue per user with 95% confidence intervals. Headline the absolute difference and percentage lift for the 28-day window. Caption with cohort and attribution rules.

  • Cumulative incremental revenue curve over the window. Plot test minus holdout by day to show when lift emerges and whether it stabilizes. This helps spot novelty effects or late-stage discount pulls.

  • Funnel snapshot for abandoners. Show abandon → email sent → opened → clicked → ordered with net lift annotations at order and conversion stages. Keep it legible; the aim is directional understanding, not pixel-perfect tracking archaeology.

Narrative script you can adapt: “Among 142,308 eligible abandoners over 28 days, the treated group generated $1.82 more revenue per user than holdout, for $259k in incremental recovered revenue at a 95% CI of $210k to $308k. After discounts and messaging costs, incremental profit was $224k. The effect was evident by day 5 and stable thereafter. We recommend rolling the program globally and testing creative and timing next to push RPR.”

Troubleshooting when things go sideways

  • Holdout leakage into campaigns or flows. Re-audit exclusion segments and any flow overrides, quantify the exposure, annotate the analysis, and if leakage is material, rerun the window.

  • Underpowered test due to low volume. Increase holdout share, extend duration, or consider a geo or time-based holdout with careful seasonality matching inspired by large-scale geo experiments.

  • Identity mismatches and masked emails. Favor a stable customer_id spine and server-side order confirmations; treat masked emails conservatively and document limitations.

  • Deliverability shocks. Monitor OR and CTR vs baseline, annotate anomalies on the cumulative curve, and consider sensitivity reads that exclude the affected period.

Tools that can help

Disclosure: Attribuly is our product. For teams standardizing identity and server-side receipts while running holdouts, Attribuly supports Shopify and Klaviyo integrations that make cohorting and revenue joins more reliable. You can review the Shopify integration overview and the Klaviyo integration notes for how profile properties and server-side events can be leveraged in this workflow. Use these resources to keep identity consistent and ensure orders are captured even when client-side tracking drops.

  • Internal references: Shopify integration overview at Attribuly and Klaviyo integration overview at Attribuly.

References and further reading

  • Methodology and why holdouts matter: see GrowthBook’s overview of persistent holdouts and Eppo’s explanation of cumulative impact measurement for product and marketing experiments.

  • Baselines and benchmarks: Baymard’s cart abandonment rate aggregate offers context on expected abandonment, while Klaviyo’s abandoned cart benchmarks help interpret revenue per recipient versus true incrementality.

  • Planning and uncertainty: Evan Miller’s calculators are useful for sample size, detectable lift, and confidence interval planning when scoping your test.

Wrap up and next step

You now have a reproducible pattern to quantify abandoned cart incrementality for email, from test design to a board-ready readout. Stand up the holdout, track the first-abandon cohort with clean windows, and package the 28-day lift with confidence intervals and a simple profit roll-up—then iterate on creative and timing to compound the gains.

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