## Metadata - Author: [[torsten-walbaum|Torsten Walbaum]] - Full Title:: The Ultimate Guide to Making Sense of Data - Category:: #🗞️Articles - Document Tags:: [[Metric trees|Metric Trees]], - URL:: https://towardsdatascience.com/the-ultimate-guide-to-making-sense-of-data-aaa121db1119 - Finished date:: [[2024-06-15]] ## Highlights > Quantifying the lag > It’s worth looking at historical conversion windows to understand what degree of lag you are dealing with. > That way, you’ll be better able to work ***backwards*** (if you see revenue fluctuations, you’ll know how far back to go to look for the cause) as well as ***project forward*** (you’ll know how long it will take until you see the impact of new initiatives). > In my experience, developing **rules of thumb** (does it on average take a day or a month for a new user to become active) **will get you 80% — 90% of the value**, so there is no need to over-engineer this. ([View Highlight](https://read.readwise.io/read/01j0ewxpdnj8jpjnq7qmmkc2ck)) > The main problem with monthly metrics (or even longer time periods) is that you have few data points to work with and you have to wait a long time until you get an updated view of performance. > One compromise is to plot metrics on a rolling average basis: This way, you will pick up on the latest trends but are removing a lot of the noise by smoothing the data. ([View Highlight](https://read.readwise.io/read/01j0exe0n6jabyh63ya29140xd)) > Looking at the monthly numbers on the left hand side we might conclude that we’re in a solid spot to hit the April target; looking at the 30-day rolling average, however, we notice that revenue generation fell off a cliff (and we should dig into this ASAP). ([View Highlight](https://read.readwise.io/read/01j0exeb7d4wbqysc0wfhtr72s)) > 3. Accounting for seasonality ([View Highlight](https://read.readwise.io/read/01j0exm310fgeq9w3hs6p908he)) > When you see a drastic movement in a metric, first go ***up*** the driver tree before going down. This way, you can see if the number actually moves the needle on what you and the team ultimately care about; if it doesn’t, finding the root cause is less urgent. ([View Highlight](https://read.readwise.io/read/01j0exp06qtadhsjxh1hf02x4w)) > When dealing with changes to ratio metrics (impressions per active user, trips per rideshare driver etc.), first check if it’s the numerator or denominator that moved. ([View Highlight](https://read.readwise.io/read/01j0expb6r5f8dw1xb4tw0nkjq)) > By segmenting across the following dimensions, you should be able to catch > 90% of issues: > • Geography (region / country / city) > • Time (time of month, day of week, etc.) > • Product (different SKUs or product surfaces (e.g. Instagram Feed vs. Reels)) > • User or customer demographics (age, gender, etc.) > • Individual entity / actor (e.g. sales rep, merchant, user) ([View Highlight](https://read.readwise.io/read/01j0expy2hmr5zh0zs70bdj0jx)) > One of the most common sources of confusion in diagnosing performance comes from mix shifts and ***Simpson’s Paradox.*** ([View Highlight](https://read.readwise.io/read/01j0exqca5ncm2z4bmk3gmwaj5))