# multi-objective UberKPI

It is quite common to cast a multi-objective problem of, for example, several KPIs as a weighted sum to define an UberKPI as described here:

https://marketingland.com/5-simple-steps-defining-multi-objective-uber-kpis-93516

One thing I have noticed is, that the KPIs are not normalize. Should one not normalize the KPIs (e.g. by dividing them by the maximum)?

The reason to normalise our variables before their use in a technical analysis is to ensure that their importance appears similar in our context-agnostic procedure. An optimisation procedure does not know if Sales from channel A have a higher return-on-investement than Sales from channel B.
Therefore normalising our KPIs wholly depends on how are these KPIs evaluated internally. If KPI_1, KPI_2 and KPI_3 are three different revenue streams, measured using the same monetary unit, then normalising them is probably unnecessary and potentially misleading. If KPI_1, KPI_2 and KPI_3 account for number of clients, number of purchases, and average cost per purchase respectively, then these KPIs are incompatible and makes little sense to simply sum them together; normalising them (by dividing by their max as you describe) or scaling them (such that they have mean $$0$$ and variance $$1$$), it probably more relevant.