Influencer Case Studies

case study

Overview

We had influencers focus messaging on family fun during the Halloween timeframe, in a multi-retailer candy campaign aimed at increasing household spending. To measure the impact of the program, Collective Bias enlisted Nielsen Catalina Solutions, who paired retailer frequent shopper card (loyalty card) data with our first-party audience pixel data.

Statistics

After weighing exposed households (Test) vs. unexposed households (Control), this is what the independent study found:

Overview

Collective Bias worked with a Laundry Detergent brand to determine the ROAS for a 75k influencer marketing program. During the timeframe aligning with the influencer activation, sales across this brand in test regions were up $233K when compared to control stores. The sales in the control regions were $8.00MM, while the sales the the test regions were $8.23MM, resulting in a 3.1x ROAS for the client.

Example A | Laundry Detergent

Overview

For this campaign, influencers focused on holiday recipes which incorporated our client’s product. Influencer content was promoted through targeted paid media amplification throughout the program. To measure the impact of the program, we enlisted a 3rd party partner — who specializes in quantifying the impact of content and ads on in-store visits by collecting data from mobile panels as they visit geo-fenced locations. 

The traffic data from the program period was paired with CB’s first-party audience pixel data, and after weighing exposed households (Test) vs. unexposed households (Control) this is what the independent study found:

Overview

Collective Bias and its client determined that influencer content could be a powerful tool for educating consumers attempting to make healthier cooking choices. Our client’s product was featured as a key ingredient in better-for-you recipes, and messaging focused on education and differentiation within the crowded cooking oil category. As a result, influencer content and targeted media delivered quantifiable impact at the store level — when studying purchase patterns of test regions (those exposed to content) vs. control regions (those not exposed to content).

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