Taming your Assumptions Mammoth II

This post is a continuation of https://adityarsmishra.medium.com/taming-your-assumptions-mammoth-c6fe403d4d36

The Noise Factor in Product signals

Lets revisit our signal equation

Signal-Final= {M0*S0 + M1*S1 + M2*S2 …. }

Every signal inherently has a noise component which distorts and changes how these signals reach you. The efficacy of your actions achieving the outcomes are dependent on your evaluation and reduction of noise in the incoming signals.

Remember signal processing?

Unfortunately, the input signal to a transmission line is seldom identical to the output signal. If you understand how the transmission line (the system) is changing the signal, maybe you can compensate for its effect. In terms of system theory, the problem is to find the system that changes the transmitted signal into the received signal. Source — Linear Systems

To help understand noise distortion, our original signal equation now changes to:

Signal-Final= {M0*(S0+N0) + M1*(S1+N1) + M2*(S2+N2) …. }

The noisier your signals, the riskier your assumptions.

Here are a few common sources of Noise

  • Overfitting and Availability: Noise in customer signals introduced from listening only to specific customer audiences while ignoring others. For example, hearing from your most active users and not actively pursuing the customers who have churned or dormant. Another flavor of this is availability bias i.e. magnifying signals that are easier to access. A common one is PMs who over-rely on customer support tickets. Note, in many cases overfitting is helpful for example when you are creating a version 0 product for your early adopters.

Feedback Loops magnify certain signals and can lead to riskier assumptions

I love drawing connections from different subjects in a hope to simplify the world of product management. https://www.linkedin.com/in/adityarsmishra/