.png)
Media plans are a key part of any brand's marketing activities. For most teams, the evaluation happens after the fact: the plan runs, the results come in and the insights inform the next cycle. Marketing Mix Modelling changes this.
Traditionally, creating a media plan began with analysing past marketing activities. Based on the data available, you would select the main objective and then build your plan, incorporating a variety of media channels. After executing the plan, you would gather performance insights to inform future plans. The question is not whether you can access insights before executing your plan. With the right tools, you can.
Today, marketers use Marketing Mix Modelling (MMM) to understand how their media channels contribute to business goals. Modern MMM solutions go further. Firstly, they analyse your media investments in greater depth, explaining campaign performance, customer types, sales channels and the products you sell. Secondly, they provide tools to simulate your media plan and forecast business outcomes, helping you optimise investments. These tools also enable you to justify budget decisions and understand the impact of changes to your plan. This allows you to focus on generating value rather than relying on channel-specific metrics that only capture part of the picture.
Predictive marketing is the future of marketing analytics. Many marketers have already embraced marketing forecast tools to elevate their efforts. These tools enable you to predict the performance of your campaigns before running them. Not only that, but you can also identify performance drivers and outliers, fine-tune various factors and craft the ideal media plan.
Before forecasting the performance of your media plan, you must first define its framework. Start by selecting a specific time period and adding your campaigns. For each campaign, specify the timeline, campaign type (e.g., performance, branding or always-on) and product type if applicable. Next, define the channel mix for each campaign, along with the budget and media pressure for each channel.
Enhance the accuracy of your forecast by incorporating factors that influence your marketing performance. Marketing outcomes extend beyond your immediate efforts and are affected by external factors like events and market trends. The same media plan may yield different results across brands due to varying business dynamics, such as price gaps or competitor spending. A robust marketing forecast tool should integrate these elements to produce realistic forecasts tailored to your brand.
.png)
Predictive marketing is not just about insights — it empowers brands to optimise their efforts and maximise impact. Advanced forecasting tools allow you to create multiple what-if scenarios that anticipate changes in campaigns and budgets. The media plans you design will be actionable, avoiding rigid forecasts. You can continue refining the details until you achieve the ideal scenario. Ultimately, you will select and implement the most effective media plan.
.png)
A common challenge for media planners managing multiple campaigns simultaneously is that each campaign tends to be evaluated in isolation — branding teams look at brand KPIs, performance teams look at conversions and no one has a clear view of how the campaigns interact.
The Compare Campaigns view within the Media Scenario Planner addresses this directly. By selecting multiple campaigns and viewing them side by side, planners can compare investment, conversions, revenue and brand consideration across different campaign types in a single view.
The example below shows four campaigns running concurrently: a Winter branding campaign, a product branding campaign, a performance campaign and an always-on campaign.
Looking at raw conversions alone, the performance campaign appears to dominate — generating 54,935 conversions from €185,059 in investment. The Winter Campaign, by contrast, generated 8,506 conversions from the highest investment of the four at €417,894. A planner relying on conversion volume alone might conclude the Winter Campaign is underperforming and should be defunded.
The cross-campaign view tells a different story. The Winter Campaign is a branding campaign contributing to brand consideration over time, while the branding campaign for Product B is achieving 19.19% brand consideration with the most modest investment of the four. This brand foundation is precisely what makes the performance campaign efficient. Without it, the cost per conversion on the performance campaign would be higher.
This is the insight that cross-campaign comparison unlocks: the relationship between campaigns, not just their individual performance. Used alongside the Media Scenario Planner's forecasting tools, it allows media planners to make allocation decisions based on how campaigns work together rather than how each performs in isolation.
Traditional media planning meant committing budget and waiting to see what happened. The cross-campaign comparison above illustrates what changes when you plan with predictive tools: the same budget can produce significantly different outcomes depending on how it is distributed, and the insight that drives better distribution is only visible when you can see all campaigns in one view.
Channel-level metrics tell you how a channel is performing. Campaign-level comparison tells you how your campaigns are working together — which is ultimately what drives your overall media efficiency.
For a practical breakdown of how investment sensitivity and incremental ROAS inform these kinds of decisions, see Incremental ROAS: Why Average ROAS Misleads and How to Fix It. For a guide to how experiments can validate your channel assumptions before planning, see How to Use Experiments in Marketing Measurement.
What is predictive media planning?
Predictive media planning is the practice of using historical performance data and statistical models to forecast the likely outcomes of a media plan before it is executed. Rather than evaluating results after a campaign has run, marketers can simulate different budget allocations, channel mixes and timing scenarios to identify the most effective plan before committing spend. Marketing Mix Modelling is the most commonly used methodology for this type of forward-looking planning.
How does Marketing Mix Modelling enable media plan forecasting?
MMM builds a statistical model of how different media channels, external factors and business dynamics contribute to sales and conversions over time. Once the model is calibrated, it can be used to simulate future scenarios by adjusting budget levels, channel allocations or campaign timing and predicting the likely outcome of each configuration. The Media Scenario Planner within Objective Platform operationalises this, allowing marketing teams to run unlimited what-if scenarios before finalising their plan.
What is the difference between channel-level and campaign-level measurement?
Channel-level measurement shows how a channel — TV, paid search, social — contributes to conversions in aggregate. Campaign-level measurement goes deeper, showing how individual campaigns within a channel perform and how they interact with campaigns in other channels. The cross-campaign comparison view in the Media Scenario Planner makes this visible in a single dashboard, which is particularly useful for understanding how branding and performance campaigns depend on each other.
What is channel saturation and how does it affect media planning?
Channel saturation occurs when additional investment in a channel or campaign produces progressively smaller returns. It is visible in investment sensitivity curves as a flattening of the response curve at higher spend levels. Identifying saturation points before planning rather than after execution means budget can be redirected to campaigns with more room to grow, improving overall media efficiency without increasing total spend.
How often should you run media plan scenarios?
Media plan scenarios should be run at the start of each major planning cycle and whenever a significant budget change is being considered. For brands managing multiple campaigns across several channels simultaneously, running scenarios quarterly — or whenever a budget reallocation decision is on the table — ensures that allocation decisions are always grounded in the most current model outputs rather than historical assumptions that may no longer reflect current channel performance.
