SPADE (Airbnb)

Use it when you need to turn feature ideas into statistically validated, business-impacting decisions.

What is it?

The SPADE framework, pioneered by Airbnb's Product Analytics Team, is a five-stage process designed to take raw feature ideas through a disciplined, data-driven decision cycle.

SPADE stands for Specify, Prepare, Analyze, Decide, and Execute, and it solves the common problem of ad-hoc prioritization by embedding statistical rigor into your roadmap. You begin by specifying a clear hypothesis and key metrics (for example, booking conversion rate), then prepare your data pipeline and segmentation plan to ensure clean inputs. In the Analyze phase, you run A/B tests or retrospective studies and apply significance testing to separate signal from noise. During Decide, stakeholders align on go/no-go actions based on real impact, and Execute covers rollout, monitoring, and iterative learning.

Each component tackles a critical pain point, vague hypotheses, data silos, inconclusive metrics, stakeholder misalignment, and weak follow-through, to help you ship high-confidence product bets.

Why it matters?

By embedding statistical rigor into each product decision, SPADE ensures you funnel resources toward changes that move the needle. That translates to cleaner experiment results, sharper prioritization, and compounding growth, every launch builds confidence, every metric lift proves ROI, and your team spends time shipping winners instead of spinning wheels.

How it works

Growth co-pilot turns your toughest product questions into clear, data-backed recommendations you can act on immediately.

Specify

Kick off by defining a clear hypothesis and success metric. For example, “Adding a one-click filter will lift booking conversion by 3%.” This aligns your team around measurable goals.

Prepare

Map out data sources, segments, and tooling. Set up dashboards, ensure tracking events, and pre-register your analysis plan to avoid p-hacking.

Analyze

Run your A/B test or retrospective study, then apply statistical significance tests. Use confidence intervals and p-values to verify real impact, not just random fluctuations.

Decide

Pull stakeholders in and present your findings. Based on data, decide whether to ship, iterate, or kill the idea. Document your rationale to build institutional knowledge.

Execute

Roll out the winning variant, track post-launch metrics, and loop back to capture learnings. Treat execution as the start of your next SPADE cycle.

Frequently asked questions

Growth co-pilot turns your toughest product questions into clear, data-backed recommendations you can act on immediately.

What does SPADE stand for?

It's an acronym: Specify (define your hypothesis), Prepare (set up your data), Analyze (run stats), Decide (go/no-go), Execute (rollout & learn).

What does SPADE stand for?

It's an acronym: Specify (define your hypothesis), Prepare (set up your data), Analyze (run stats), Decide (go/no-go), Execute (rollout & learn).

How is SPADE different from RICE?

RICE scores ideas by Reach, Impact, Confidence, and Effort. SPADE, on the other hand, is an end-to-end experiment playbook with statistical validation at every step. You can use RICE to shortlist and SPADE to verify winners.

How is SPADE different from RICE?

Do I need a data science team to run SPADE?

You don't need a PhD. Standard analytics tools like Amplitude, Mixpanel, or a simple SQL pipeline plus a spreadsheet for basic significance tests will carry you through Prepare and Analyze.

Do I need a data science team to run SPADE?

You don't need a PhD. Standard analytics tools like Amplitude, Mixpanel, or a simple SQL pipeline plus a spreadsheet for basic significance tests will carry you through Prepare and Analyze.

Is SPADE only for A/B testing?

SPADE shines with A/B tests but is flexible: apply the same steps to cohort or funnel analyses, retrospective studies, or even qualitative tests, anything with a clear hypothesis and data to back it.

Is SPADE only for A/B testing?

SPADE shines with A/B tests but is flexible: apply the same steps to cohort or funnel analyses, retrospective studies, or even qualitative tests, anything with a clear hypothesis and data to back it.

How often should I use the SPADE framework?

Use SPADE every time you have a new hypothesis or feature idea. High-velocity teams run it per sprint to keep experiments honest and learning loops tight.

How often should I use the SPADE framework?

Use SPADE every time you have a new hypothesis or feature idea. High-velocity teams run it per sprint to keep experiments honest and learning loops tight.

You've turned feature ideas into data-backed bets with SPADE. Now run those winners through the CrackGrowth diagnostic to root out hidden UX friction before you ship.

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