Uncovering Sponsorship Effect for Italian Sportsbet Operator

-12%

Customer Acquisition Cost (CAC)

+5%

First-Time Depositors (FTDs)

+7%

Marketing ROI (Deposits)

Fieldstream helped an Italian sportsbet operator to evaluate its investments in team sponsorships - namely, a big team and several smaller teams. As there was an upcoming decision to be made regarding a new investment in the bigger team, Fieldstream was tasked with gaining insights into Marketing ROI and the impact of their sponsorships. The results: an 8% uplift in Marketing ROI and an organization aligned on the overall marketing budget strategy.

“Fieldstream helped us gain a better understanding of the optimal marketing mix and specifically, the effect of our sponsorships."
— Country Manager, Italian Operator

‍

Background

An Italian sportsbet operator had invested significantly into sports team sponsorships - specifically one large team and several smaller collaborations. They sought Fieldstream's services to gain insights into the ROI of their marketing activities, and more specifically on the impact of their large team sponsorship.

‍

Challenges

Big sponsorship costs – making it difficult to measure due to its nature.
Upcoming decision – regarding a new sponsorship investment in the large team.
Both long-term and short-term effect were relevant to understand.

‍

Why It Mattered

This investment was significant for the company, while alternative marketing options could be a smarter move.
The executive team needed data and supporting arguments for their decision.
Sponsorship could potentially have a positive brand impact, leading to more effective marketing. But just measuring short-term ROI would be too limited in scope.

‍

Fieldstream's Objectives

Provide an overall understanding of the company's Marketing ROI.
Enable data-driven decision making for sponsorships.

Fieldstream Solution

1. Data Quality Audit & Pipeline

Fieldstream assisted in reviewing and validating data sources.
Pulled five-year history directly from the operator’s data warehouse.

2. Sponsorship-Specific Data Analysis

Translated sponsorship details to media value.
Collected and reviewed organic traffic, share of search, and brand metrics.

3. Custom MMM Build

Core hierarchical Bayesian model, weekly granularity, channel-specific ad-stock and saturation curves.
Ran models and specific analysis for different KPIs to understand the brand effect of sponsorship.
Analyzed marketing effectivenss pre, during, and post sponsorship period.

4. Fieldstream Dashboard

Monthly auto-refresh; executive-ready ROI reports.
Optimizer and scenario planning tool for the team's ongoing work.

5. Specific Sponsorship Analysis Report

Decision material for future sponsorship investments.

Key Findings

1. Big sponsorship had limited short-term effect

Evidence: Analysis of Marketing ROi before, during, and after revealed low impact, nor did the MMM analysis reveal a significant effect of the sponsorship.
Action: Recommended not to proceed with the investment.

2. Big sponsorship also had limited longer-term effect

Evidence: Investigating brand metrics, organic traffic, and base revenue also did not reveal any significant effect of the sponsorship.
Action: Recommended not to proceed with the investment.

3. Smaller sports sponsorships and collaborations worked well

Evidence: These were used for creating relevant content and offline events that showed good ROI.
Action: Move some budget to this category.

‍

Results

We saw a 8% uplift in overall Marketing ROI.
Saved money on sponsorships, after realizing that the big sponsorship wasn't having any real impact on ROI.
Organizational alignment on overall marketing budget strategy.