Bandit vs Classic A/B for Homepage Hero Rotation—How to Choose (2025)

If your homepage hero is the front door to your business, your rotation strategy is the doorman: it decides who gets in first, how long they linger, and whether they spend. In 2025, most teams face the same fork in the road: keep a classic fixed-split A/B (or A/B/n) test to learn a defensible winner—or switch to a Multi-Armed Bandit (MAB) to adapt allocation on the fly and capture more value during the test.

This guide is built for eCommerce and subscription teams deciding how to rotate hero banners or hero modules. We compare bandits versus classic A/B across traffic allocation, statistical guarantees, speed to learn vs. speed to earn, non-stationarity/seasonality, novelty effects, metrics (CTR vs conversion vs revenue/session), implementation complexity, governance, and org maturity—then give scenario-based recommendations.

Quick definitions in the hero context

• Classic A/B (fixed split): You assign traffic evenly (e.g., 50/50 or 33/33/33) for the duration, avoid peeking until your stopping rule is met, and make an auditable decision with confidence intervals. Sequential testing engines can support early stopping while controlling error rates.
• Multi-Armed Bandit (adaptive): You reallocate traffic in near real time toward variants that appear to be performing better. Thompson Sampling and epsilon-greedy are common; platforms differ in how they handle exploration floors and update cadence. For example, Adobe’s Auto-Allocate begins dynamic rounds only after each experience crosses thresholds (about 1,000 visitors and 50 conversions), then maintains a random-exploration floor and updates hourly, reverting if confidence intervals overlap again, per the 2025 Adobe Experience League description of automated traffic allocation.

According to Optimizely’s support docs (2024–2025), their MAB implementations pair Thompson Sampling for Bernoulli metrics with epsilon-greedy for numeric ones and emphasize that bandit modes focus on maximizing lift rather than producing classic “statistical significance” readouts—useful nuance when your goal is speed to earn rather than inference (see the Optimizely Support articles on the history of Stats Accelerator and on maximizing lift with MAB optimizations).

The trade-off that matters: learn vs. earn

There is a fundamental tension between minimizing regret (earning more during the test by sending less traffic to losers) and maximizing statistical power (learning precisely), formalized in a 2023 Management Science paper that lays out a Pareto frontier where improving one dimension degrades the other, see the INFORMS Management Science 2023 analysis of regret vs. power.

Dimension-by-dimension comparison

Notes and references:

• Optimizely documents algorithm choices and clarifies that bandit modes prioritize lift over traditional significance, and also discusses bias and mitigation around Simpson’s paradox in its Stats Engine; see Optimizely’s 2024–2025 support history of bandits and the Stats Accelerator/epoch engine documentation.
• GrowthBook’s 2025 docs call out that bandits typically support a single decision metric and that results can be biased versus standard experiments—useful for governance expectations.

What changes on a homepage hero: non-stationarity, seasonality, novelty

A hero module is rarely stationary. Creative fatigue, promotions, traffic mix shifts, and day-of-week patterns move the goalposts.

• Non-stationarity mitigation. In dynamic environments, bandits perform best when they discount stale data—e.g., a sliding-window approach that weights recent performance more heavily. The 2024 LIPIcs APPROX/RANDOM paper on sliding-window bandits in uncertain environments formalizes this idea.
• Seasonality. Retail peaks (e.g., Cyber 5) and dayparting can flip winners. Adobe’s Auto-Allocate illustrates practical guardrails: floors for exploration and hourly re-allocation rounds after hitting minimum data thresholds. If you are not using a contextual bandit, ensure your evaluation spans full cycles.
• Novelty effects. New creatives can spike CTR at launch. Without safeguards, a bandit can over-allocate too early. Early-stage caps and minimum impression floors are standard remedies; Adobe’s design of a fixed random-exploration share is a platform-level example.
• A practical illustration. Klaviyo’s engineering blog demonstrates how creative relevance around holidays causes certain images to surge and then fade—a reminder that allocation algorithms must adapt to shifting preferences over time (Klaviyo Engineering, 2020s write-up on bandits and seasonal shifts).

Metric strategy: CTR vs conversion vs revenue per session

• CTR on the hero CTA: Fast signal and great for early detection, but can be misaligned with revenue if a clickbait creative disrupts downstream flow.
• Conversion rate (macro or micro): Closer to business goals but slower and noisier; requires enough sample and clear attribution windows.
• Revenue per session (or per visitor): The most financially aligned but high variance and sensitive to outliers; often needs trimming or winsorization.

Practical policies for 2025:

• Dual-metric guardrails. If you optimize allocation on CTR, monitor conversion and revenue/session as guardrails (halt or cap if they regress). GrowthBook’s 2025 bandit docs note a single decision metric is typically supported—so you may need separate monitoring to protect business outcomes.
• Delayed feedback handling. For bandits, define a credit assignment window (e.g., attribute conversions occurring within N days of the hero impression). VWO’s bandit explainers emphasize that bandits shine for immediate, frequent signals and that Bayesian framing helps reason about decisions under uncertainty; see the VWO Help Center article on MAB working and the VWO blog on Bayesian A/B testing.
• When in doubt: use A/B for primary business decisions (e.g., pick a single hero for a quarter) and bandits for always-on rotation with fast proxies.

Implementation and governance in 2025

• Tooling and allocation mechanics. Major platforms support bandits with tunable exploration and update cadence. Optimizely documents Thompson Sampling for binary goals and epsilon-greedy for numeric metrics; Adobe Auto-Allocate uses hourly rounds with an exploration floor after data thresholds; GrowthBook implements Thompson Sampling with a minimum traffic per variation and cautions about bias and single-metric limitations.
• Progressive rollouts as a cousin. Even feature-flag platforms without a “bandit” label mimic adaptive allocation via progressive/guarded rollouts with randomized traffic and monitoring—see LaunchDarkly’s docs on progressive and guarded rollouts in 2025.
• Sequential testing and peeking. If you run classic A/B with a sequential engine, peeking is expected and accounted for; Statsig’s 2024–2025 primer explains how sequential testing adjusts error rates when you check early. Optimizely’s support library also covers why significance moves over time and how sequential sample ratio mismatch (SSRM) detection protects integrity.
• Hygiene. Ensure consistent impression counting on the hero, bot filtering, and performance budgets (heavy hero assets can degrade outcomes and confound your read).

Decision flow: how to choose for your hero

• Do you need an auditable, defensible winner for a finite campaign? → Favor classic A/B (or A/B/n). Run across full weekly cycles, pre-register a stopping rule, and report confidence intervals.
• Is the hero an always-on rotation with frequent creative refreshes? → Favor a bandit (Thompson Sampling). Set a minimum exploration floor (10–20%), early allocation caps, and re-seed when creatives materially change.
• Is seasonality/day-of-week strong? → Either run A/B across full cycles or use a contextual/sliding-window bandit. Consider warm starts at season transitions.
• Low traffic and too many variants? → Bandit to reduce regret; limit the number of variants and use informative priors. If inference clarity is paramount, reduce variants and run longer A/B.
• Are your primary outcomes delayed (checkout conversion, revenue/session)? → Favor classic A/B or bandits with explicit credit windows and proxy guardrails (e.g., optimize on CTR initially, then shift weight to conversion as data accrues).

Scenario-based recommendations (with configs)

1. Stable traffic; need a defensible seasonal hero pick

• Method: Classic A/B or A/B/n
• Configuration: Fixed split; sequential testing allowed via your platform; cover at least 2 full weekly cycles; freeze creative mid-test; analyze by time-since-launch to check novelty decay.
• Governance: Pre-register hypothesis/metrics; document sample size and power; run SRM checks (many platforms, e.g., Optimizely, include automatic SSRM detection per 2024 docs).

2. Always-on rotation with frequent refreshes

• Method: Multi-Armed Bandit (Thompson Sampling)
• Configuration: Exploration floor 10–20%; cap any single arm at ≤60–70% during the first 24–48 hours; sliding-window or discounting over 7–14 days; re-initialize priors when creatives change materially.
• Guardrails: Monitor conversion and revenue/session; pause or cap when guardrails breach.

3. High seasonality or strong day-of-week/daypart effects

• Method: Contextual bandit if available (features like device, geo, referrer, time-of-day); otherwise A/B covering the full cycle
• Configuration: If bandit, include time features or run separate bandits per key segment if traffic allows; consider warm starts at season transitions.

4. Low traffic (<10k sessions/week) and many variants

• Method: Bandit to reduce regret; keep variant count modest (3–4)
• Configuration: Use informative priors if supported; exploration floor; longer sliding window to stabilize learning.
• Alternative: If legal/compliance demands a clear inference, reduce variants and run an extended A/B.

5. Delayed primary metrics (checkout conversion, revenue/session)

• Method: A/B with a fixed analysis window, or a hybrid bandit with delayed-feedback handling
• Configuration: Define a credit window (e.g., 3–7 days) for conversions; allocate on a fast proxy (CTR) early but enforce guardrails; shift weight toward conversion as sufficient data accumulates.

Risks and mitigations

• Novelty bias. New heroes often spike CTR. Mitigate by early-stage allocation caps and minimum impressions per variant; if running A/B, extend duration and examine time-since-launch curves.
• Creative fatigue and drift. Expect decay; for bandits, use discounting/sliding windows; for A/B, schedule periodic re-tests.
• Proxy misalignment (CTR vs revenue). Enforce guardrails, and validate short-term CTR winners against conversion and revenue/session before rollouts.
• Data quality and integrity. Ensure deduped sessions, consistent CTA click tracking, and that CDN caching doesn’t skew impression counts. Run SRM checks; Optimizely documents automatic SSRM detection and explains why significance fluctuates over time in sequential engines.
• Governance and bias in bandit results. GrowthBook notes that bandit outcomes can be biased vs standard experiments and typically center on one decision metric; document floors, update cadence, and decision rules.
• Ethical/brand constraints. Set caps on aggressive promos in rotation to avoid overexposure.

A practical hybrid playbook (2025)

• Filter, then optimize. Start with a short A/B/n (fixed split) to eliminate egregious losers (e.g., 5–7 days or to pre-set confidence bounds). Then hand the top 2–3 variants to a Thompson Sampling bandit for ongoing rotation.
• Re-baseline for major seasons. Ahead of peak periods (e.g., holiday), run a quick calibration A/B to re-anchor priors or reset the bandit.
• Multi-objective in practice. Allocate on a fast signal (CTR), but hard-stop or cap if conversion or revenue/session guardrails breach; once conversion data matures, switch the decision metric.
• Document everything. Keep an experiment registry with configurations (exploration floor, caps, window), incident plans, and a change log for creative swaps.

“Choose quickly” cheat sheet

• Choose classic A/B when you need: auditability, a single seasonal winner, or your primary metric is delayed/noisy.
• Choose a bandit when you want: higher in-test gains, always-on rotation, or you expect preferences to shift.
• Choose a hybrid when you need both: de-risk with a short A/B/n, then let a bandit manage rotation—and re-baseline at seasonal inflection points.

References and further reading

• Optimizely Support (2024–2025): algorithm choices and lift focus in MAB modes — see the History of Stats Accelerator and multi-armed bandits, and the article on maximizing lift with multi-armed bandit optimizations: Optimizely’s bandit algorithm history, Optimizely on maximizing lift with MABs. Also on integrity and peeking behavior: Optimizely on significance over time and automatic SSRM detection. For paradox mitigation see Optimizely’s Epoch Stats Engine note.
• Adobe Experience League (2025): Auto-Allocate thresholds, rounds, and exploration floor: Automated traffic allocation; bandit capability in A4T: A4T and Auto-Allocate.
• GrowthBook Docs (2025): Thompson Sampling bandits, configuration, and limitations: Bandits overview, Configuration, Results caveats.
• VWO (2024–2025): Bandit working and Bayesian perspective: VWO Help Center on MAB, VWO blog on Bayesian A/B testing.
• Statsig Perspectives (2024–2025): Thompson Sampling explainer and MAB vs A/B primer; sequential testing overview: Thompson Sampling balance, MAB vs A/B, Sequential testing and peeking.
• Eppo Blog (2024–2025): When to use bandits vs experiments and batch-updating perspective: Bandit or experiment.
• LIPIcs APPROX/RANDOM (2024): Sliding-window bandits under uncertainty: Sliding-window bandits 2024.
• Klaviyo Engineering (2020s): Visual intuition for seasonal/creative shifts in bandits: MABs in pictures.
• LaunchDarkly Docs (2025): Progressive and guarded rollouts with randomized allocation: Guarded rollouts, Progressive rollouts, Randomization.
• INFORMS Management Science (2023): The Pareto trade-off between regret minimization and power: Regret vs. power frontier.