What patterns really matter?

Think of data analysis like tuning a radio. The music you want to hear is the signal, while the static is the noise. In product data, noise consists of random variations that don't represent meaningful patterns. These might be temporary spikes from unusual events or fluctuations that happen by chance.

Real signals show consistent trends over time. They represent actual changes in user behavior or market conditions. To find them, look for patterns that repeat across different time periods or user segments.

Moving averages help smooth out daily fluctuations to reveal underlying trends. Statistical methods like standard deviation can help determine if a change is significant or just random variation. The key is patience. Don't react to every small change in your metrics. Wait for patterns to establish themselves over weeks or months before making major decisions.

One effective technique is to examine data points on a rolling basis over the past 30, 90, or 365 days, then break them down by geography, device, or other dimensions. This makes it easier to see if a meaningful signal consistently comes through. Tools like decomposition can further split data into trend, seasonal, and residual components, highlighting what truly matters.^[1][2]