By leveraging AI and machine learning we built a tool that could continuously learn from historical campaign performance data at scale while reducing planning time. It has proven to be 4 x more effective in allocating budget compared to previous methods, taking into account high-level marketing objectives.
It is crucial for marketing budget owners to strategically allocate funds across the breadth of their campaigns to maximize return on investment and deliver impact at scale.
Marketers burdened with a three-month campaign cycle can benefit from leveraging data to build rapid feedback loops and reduce planning time, crucial in large-scale enterprise environments.
Our client had a vast amount of accessible data and recognized the potential of algorithmic marketing to make informed decisions. However, due to the high volume of campaigns there was a lack of standardization in ROI metrics to form the basis of the model.
Algomarketing was engaged to consult on a solution that utilized machine learning to develop a tool that could continuously learn from historical performance across campaigns at scale.
Starting with a consultative period to ideate a proof of concept, with senior stakeholders benchmarking performance indicators and confirming the correct data points to inform the system.
The Algos cleansed the data and inconsistent data points were normalized to create a consistent view of ROI metrics.
As part of minimizing risk and demonstrating impact quickly, the tool was piloted in a single region which was a resounding success with the new model demonstrably generating more revenue per dollar compared to previous campaign budget methods. It provided the opportunity to iterate and improve at a small scale and get buy-in to its success from stakeholders before rolling it out to larger teams.
The model rolled out globally, was enhanced to include the organization’s key objectives, such as allocating a greater portion of the budget to regions targeted for growth. It would allocate budget due to past successes but required a nuanced approach when taking into account other factors.
Algomarketing’s experience enabled us to support the client with the necessary change management. By identifying regions that were allocated less budget, our team supported the stakeholders with the possible political ramifications of a tool coming to this conclusion. The Algos nurtured and educated champions in those regions to embed the adoption of the tool during the launch and implementation phase.
The tool was scaled to other teams and continues to be in use as a critical part of the budget allocation and planning process, consistently proven to benchmark higher than previous methods.
The implementation of the AI budgeting and ROI maximization tool was game-changing for budget allocation.
It took approximately 4 months from the idea to launch. The design and build phases were carefully planned for ideation, solution design, data collection, cleaning, and model-building within the first 12 weeks of the project. The final deployment, testing, and rollout phase took less than a month to complete.
A three-month pilot project ran to gather enough data to evaluate the results. This demonstrated the advantage of using the tool to allocate spending across multiple campaigns. The number of opportunities found using the model compared to not provided clear evidence of its effectiveness. Algomarketing's solution was 4 x more effective.
A significant outcome of this project was the realization in the marketing team of the organization’s ambition to be data-driven. A culture of data and iterative improvements is something that this organization is known for.
Marketing teams now make better decisions across the organization based on performance data across all campaigns, improving the overall efficiency and effectiveness of their marketing strategies and budgets.
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