Context

Faced with challenges around audience targeting, a leading ad tech specialist had to adapt their services and offers to both the short-term and long-term perspective.

The client is growing in the sector of digital marketing telecommunications, and they needed to improve their services to deliver digital audio advertisements worldwide, at any time, and to a precise audience.

In advertising, additional parameters have a substantial impact on performance. However, their previous solution had granularity, seasonality, and forecasting limitations.

The client needed better insights into how they could use their vast data. Feeling overwhelmed and uncertain, they contacted Agilytic to optimize the allocation forecast.

Approach

Forecasting offers a methodology that prepares a range of possibilities to choose from. Ensuring a desirable future occurs or allowing to quickly adapt to an unfavorable future. Leaders in business and industry have widely used it for decades to improve present decision-making and efficiently implement strategies.

To develop a better service allocation estimate, the client wanted to answer questions like:

• How accurate is this prediction compared to the actual hours available?

• What is the impact of seasonality?

• What is the effect of ratio approximation?

We began with a diagnosis of current data. Next, we worked on a data modeling exploratory analysis to quickly compute an allocation estimate based on the input parameters.

Finally, we offered documentation for a data roadmap, looking at initiatives to enrich the database and strengthen analysis, a potential technical framework, and data governance. We delivered the following:

• Detailed analysis and our conclusions for each milestone taken

• Our development scripts justifying our model selection, our findings, and resulting intellectual property

• Complete documentation of the project, methods and processes used, and the results obtained

Results

We presented scalable allocation forecasting to improve services in advertisement and marketing. The model can provide an instant response regarding allocation while offering better granularity, accuracy, and consistency.