![]() ![]() There are a number of campaigns for which we have historic data and we want to optimise the allocation of budget to each based on this data, with the key aim being to maximise the revenue. ![]() ![]() This should be solvable without the use of an LP process, but should hopefully help to illustrate the process and potential. The problem we’ll look at is how to optimise the allocation of budget to a range of AdWords campaigns. Let’s now consider how this would look in a relevant real-world scenario. We won’t delve into any technical definitions here, but it is useful to illustrate the basic form of such a problem would be to do something like maximize ‘x’ where:į(x)=x 2-x+2 with the constraint -1 ≤ x ≤ 5 In simple terms, LP solves the problem of maximising a target variable (a goal, essentially) subject to some defined constraints (or inequalities) It usually involves a system of linear inequalities, called constraints, but in the end, we want to either maximize something (like profit) or minimize something (like cost).” “Linear Programming sounds really difficult, but it’s just a neat way to use math to find out the best way to do things – for example, how many things to make or buy. A good introduction to LP is given over at LP is great because it is one of those mathematical techniques which is useful and relatively easily applied within many real-life situations and industries. In this article, I’ll introduce linear optimisation (or Linear Programming ) and give an example of how to use the Excel Solver add-in to solve some common search marketing scenarios. If we think about these optimization problems formally, we can categorize them as either linear or non-linear optimisation. The process of thought which you will go through (either knowingly or not) is a ‘what-if?’ analysis – weighing up the possible courses of action, the expected outcomes and deciding on the optimal solution. In marketing optimisation you are constantly tasked with making the right decision based on a number of known or projected factors, with the end result along the lines of maximising return or total revenue. ![]()
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