Separate true retention trends from predictable seasonal swings. Learn practical ways to normalize cohorts so comparisons stay fair.
Seasonality can distort cohort retention comparisons when ______.
cohorts are identical sizes
time is indexed by months
cohorts started in different seasonal peaks or troughs
there are no holidays
One simple way to reduce seasonal noise is to compare cohorts on a ______ basis.
inventory turnover only
raw click-through rate
seasonally adjusted retention rate
U.S. CPI headline
If the business has strong holiday spikes, a helpful visualization is ______.
cohort heatmaps indexed by tenure and calendar month
a word cloud of features
a single total revenue bar
a pie chart of SKUs
To smooth retention before modeling, a common technique is ______.
add noise to labels
drop half the data
shuffle dates randomly
moving averages or LOESS on rate curves
When forecasting retention with seasonal structure, you might use ______.
an unscaled k-means on IDs
a model with seasonal terms or STL decomposition
a model that forbids time variables
a purely cross-sectional snapshot
A fair A/B test reading for retention during peak months should ______.
stratify or control for seasonality in the analysis
ignore sample size balance
pool all months without adjustment
use only the best cohort
A quick diagnostic for seasonality is to ______.
plot retention by tenure across calendar months
remove axis labels
hide months with low data
sort users alphabetically
If seasonality is weekly, measuring retention in ______ often clarifies the picture.
arbitrary fiscal quarters
iso-week or weekday-of-start groupings
random 9-day bins
annual bins only
For marketplace two-sided seasonality, a robust approach is to ______.
control both demand and supply seasonal drivers
control neither side
only control ad spend
fix prices to zero
When seasonality is strong, a good KPI for leadership is ______.
total installs lifetime
organic sessions last week
seasonally adjusted retention trend by cohort start month
pageviews only
Starter
Begin by spotting seasonal peaks and troughs before judging retention.
Solid
Nice work—apply seasonal adjustments and compare like with like.
Expert!
Expert calibration of seasonal effects and clear retention reads.
