{"id":22163,"date":"2025-02-26T16:26:22","date_gmt":"2025-02-26T15:26:22","guid":{"rendered":"https:\/\/www.nexoya.com\/?post_type=help_center&p=22163"},"modified":"2025-03-10T10:11:16","modified_gmt":"2025-03-10T09:11:16","slug":"understanding-optimization-uplift-and-its-limitations","status":"publish","type":"help_center","link":"https:\/\/www.nexoya.com\/it\/help\/understanding-optimization-uplift-and-its-limitations\/","title":{"rendered":"Understanding optimization uplift and its limitations"},"content":{"rendered":"\n

Every marketer wants to maximize their return on investment. While Nexoya’s algorithm consistently strives to improve performance by reallocating budgets, sometimes it makes recommendations that might raise eyebrows. These unexpected suggestions aren’t errors \u2013 they’re often the best possible moves within real-world constraints. Let’s look at why this happens and explore some real-world examples that demonstrate how these seemingly counterintuitive choices can actually lead to better results.<\/p>\n\n\n\n

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What is the % uplift in an optimization<\/strong><\/h3>\n\n\n\n

Let\u2019s break down how the uplift is calculated. Many might assume we simply compare new predictions to current performance, but our approach is more sophisticated.<\/p>\n\n\n\n

Our baseline isn’t just your current spending \u2013 it’s a proportionally scaled version of your existing budget allocation. Here’s why this matters: If you’re planning to increase your overall budget by 20%, we don’t compare our optimized recommendations against your current spending. Instead, we compare them against what would happen if you simply increased each campaign’s budget by 20%.<\/p>\n\n\n\n

This method ensures we’re making fair comparisons and delivering real improvements, not just riding the wave of increased spending. Let me walk through the process:<\/p>\n\n\n\n

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1<\/strong>. Let\u2019s assume that the new optimization budget is increased from $900 to $1200.<\/p>\n\n\n\n

2<\/strong>. With this new budget, Nexoya finds the ideal budget allocation for all campaigns and predicts the expected performance for each campaign. In this example, we would achieve a total of 34\u2019500 leads with the new optimization.<\/p>\n\n\n\n

3.<\/strong> To calculate the uplift, a baseline needs to be defined. This baseline is achieved by linear rescaling. Since there is a +33% overall budget increase this week, all campaigns must first be rescaled.<\/p>\n\n\n\n

4.<\/strong> The expected leads are now also predicted with the linearly rescaled budgets, resulting in an overall amount of 33\u2019000 leads.<\/p>\n\n\n\n

5.<\/strong> The last step is to calculate the uplift. The numbers of the baseline scenario are compared with the numbers of the optimisation proposal, which is done for each campaign in particular and returns the % uplift, as shown in the example above.<\/p>\n\n\n\n

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Common limitations to maximize optimization potential<\/strong><\/h3>\n\n\n\n

Several factors can restrict the algorithm from finding a maximum that results in a positive uplift. These are business limitations in the form of minimum and maximum budget constraints, but also environmental factors, that make it impossible to scale best-performing campaigns further:<\/p>\n\n\n\n