eCom Forecast Plan & Paid Media aMER Modeling
Our agency’s process for forecasting and planning revolves around data-driven strategies to align with client revenue goals. We start by collecting extensive historical data on our clients' performance, which we use to build predictive models. For example, we analyze data from December 2023 to project future outcomes. We create two different projection models: one based on the rolling average of the last four months and another that weights last year’s performance more heavily, especially for key months like November.
Projection Models:
When planning for a target of $1.2 million in gross revenue for November, we weigh both models. The first model, using recent averages, might suggest a $75K ad spend to achieve around $781K in paid revenue. However, the second model, which uses last November’s data, indicates that spending just $60K could achieve the revenue goal, resulting in about $564K attributed to paid ads. Although this approach yields a lower ROAS, it’s more efficient in terms of spend, with a healthier contribution margin.
Budget Allocation:
The agreed-upon budget (e.g., $60K) is then split between platforms like Google and Meta. Instead of dividing the budget evenly, we consider historical data to optimize contribution margins. For instance, Google’s contribution margin begins to diminish beyond $30K, making that the ideal spend for a 6.15x ROAS. Although lower spend levels might yield a higher ROAS, they wouldn’t drive enough overall business impact.
Media Planning:
For Meta, analysis shows that spending $35K yields the best results before the margin starts to drop. By adjusting the allocation (e.g., spending $35K on Meta and $25K on Google), we optimize both platforms. Once the total budget is finalized, we develop detailed media plans, specifying campaigns and ad groups tailored to achieve the desired performance metrics.
We follow a similar approach for other platforms like TikTok, Pinterest, and programmatic channels, always ensuring plans are rooted in historical and projected data. Our role as media buyers is to refine these plans, specify campaign types, and align strategies to hit projected ROAS, prioritizing insights from past performance over gut feelings.