Mathematically Optimal Solution ≠ Optimal Marketing Solution
One of the important use cases of Marketing Mix Modeling (MMM) is the Marketing Budget Optimization. The results of MMM are directly leveraged to provide various ‘What-if’ budget allocation scenarios.
Such as:
▪️ Under the same budget, how could we have allocated the spends across media differently to yield a lift in KPI?
▪️ What should be the allocation across media if there is a 30% increase in budget?
▪️ What if we decide to cap media A’s allocation to 10% of budget, what would be the net effect on KPI?
Most organizations are keen to know various ‘what-if’ budget optimization scenarios for two reasons:
1) It lets them know what they could have done right in the past.
2) How they can efficiently allocate marketing budget in future.
To get marketing budget optimization right, we need to first understand that “Mathematically Optimal Solution ≠ Optimal Marketing Solution”.
Right out of the gate most marketing budget optimization tools provide only mathematically optimal solution. It is not optimal marketing solution.
The next question would be ‘how to make the mathematically optimal solution into optimal marketing solution?’
The answer is two fold:
– Domain informed constraints.
– Knowledge of cost of marketing channel.
A mathematically optimal solution might suggest allocating huge spends to a certain media channel but in reality, the cost of that media might be prohibitive.
So, it is important to layer your marketing knowledge on top of the mathematical solution.
