Product Analysis

Purpose

Understand how a product is actually used and decide what to do about it. The failure mode is a dashboard full of numbers that go up, none of which are connected to whether the product is working.

When to Use

• Deciding what to build next.
• A metric moved and nobody knows why.
• Assessing whether a feature worked.
• Setting up product analytics.

Capabilities

• Metric selection: the one that matters versus the ones that flatter.
• Funnel analysis and drop-off diagnosis.
• Retention and cohort analysis.
• Feature-adoption measurement.
• Prioritization on evidence.

Inputs

• Usage data, at the event level.
• What the product is meant to do for the user.
• The decision this analysis informs.

Outputs

• The metric that actually reflects value, and where it stands.
• The specific point of failure in the funnel, or the specific cohort that churns.
• A prioritized recommendation.

Workflow

1. Choose the metric that reflects value received — Not signups, not page views, not "engagement". What is the action that means the user got what they came for? That is the metric.
2. Look at retention before acquisition — A product with a leaking bucket does not need more water. If week-4 retention is 8%, acquisition spend is being poured into a hole.
3. Segment before concluding — An aggregate number hides everything. A flat retention curve can be two cohorts: one that retains at 60% and one at 2%. Those require completely different responses.
4. Find the drop-off, then find out why — The funnel tells you where users leave. It never tells you why. That requires session recordings, support tickets, or asking them.
5. Distinguish a movement from noise — A 6% week-on-week change on a small base is noise. Before declaring a trend, check whether the change exceeds the normal variance.
6. Recommend something specific — With the expected impact and how you will know if it worked.

Best Practices

• Vanity metrics go up regardless of whether the product works. Total registered users, cumulative page views, and total revenue since launch can only increase. If a metric cannot go down, it cannot tell you anything.
• Retention is the product metric. Everything else — acquisition, activation, revenue — is downstream of whether people come back.
• A cohort retention curve that flattens has found product-market fit for that cohort. One that goes to zero has not, regardless of how good the early numbers look.
• The aggregate hides the answer. Always segment: by acquisition channel, by cohort, by use case, by company size.
• A funnel identifies where users leave. It cannot tell you why, and guessing at the why is how teams ship the wrong fix.
• Before acting on a change, check whether it is larger than the week-to-week noise. Most "the metric moved" investigations are investigations of noise.

Examples

Segmentation revealing the actual product:

Aggregate week-4 retention: 22%. Flat for six months. Universally described in
the company as "our retention problem".

Segmented by the first action taken in the first session:

  Created a project + invited a teammate (11% of signups) : 71% retained at wk 4
  Created a project alone                (34% of signups) : 24%
  Browsed, created nothing               (55% of signups) : 3%

There is no retention problem. There is an activation problem, and a specific one:
users who invite a teammate in the first session retain at 71%, which is an
excellent number for this category.

The aggregate of 22% is a weighted average of one product that works extremely
well and one that does not exist — because 55% of signups never create anything.

What this changes:
  - The roadmap item "improve retention with weekly digest emails" is targeting
    the wrong thing. It emails people who never activated.
  - The correct target is the 55% who create nothing, and the specific question
    is why they leave without acting. That is a session-recording and
    user-interview question, not a data question.
  - The second target is moving single-user projects toward invites, which the
    data suggests triples retention.

Neither of these was visible in the aggregate.

Checking that a movement is real before acting on it:

def is_signal(series: pd.Series, window: int = 12) -> Signal:
    """Most 'the metric moved!' investigations are investigations of noise."""
    recent = series.iloc[-1]
    baseline = series.iloc[-window - 1 : -1]

    mean, std = baseline.mean(), baseline.std()
    z = (recent - mean) / std if std > 0 else 0

    return Signal(
        value=recent,
        baseline_mean=mean,
        z_score=z,
        # Within 2 standard deviations of the trailing mean is normal variation.
        verdict=(
            "signal" if abs(z) > 2 else
            "noise — this is within normal week-to-week variance"
        ),
    )

# Signups fell 9% this week. Panic in the standup.
#   trailing 12-week std: 7.4%
#   z-score: -1.2
#   Verdict: noise. This week is not unusual. Do not investigate; do not
#   change anything. It will "recover" next week and someone will take credit.

Notes

• The segmentation example is the most common shape of real product analysis: the aggregate says there is a problem with X, and the segments reveal the problem is entirely elsewhere. Segmenting first is almost always the highest-value move.
• A flattening retention curve is the clearest evidence of product-market fit available, and it is visible in a cohort chart long before it is visible in revenue.
• Before investigating why a metric moved, establish that it moved. A large fraction of analytics work is the careful investigation of random variation.