Brook Taverner

Brook Taverner is a well-known British clothing brand, specialising in high-quality men's formalwear and business attire. Founded in 1912, the brand has a long history of producing tailored clothing, with a focus on classic British style combined with modern fits and materials and is recognised for its attention to detail, quality fabrics, and craftsmanship.

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the challenge

The team at Brook Taverner were looking to improve return on ad spend (ROAS) and conversions on their Meta account and approached Vagabond for support in overcoming the challenges they had faced in making paid social successful for the brand. 

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After experiencing significant inconsistency across the performance of various paid social campaigns, with certain strategies working intermittently before tapering off, the team lacked trust in the Meta platform. The Vagabond team was brought in to investigate the account’s structure, optimise it, and implement testing strategies to create a more reliable, high-performing paid social presence.

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our solution

Vagabond began by utilising tools, like Motion, to understand existing performance and analyse the data to identify previous strong and poor performing ads. We also utilised Madgicx, to test AI Lookalikes, allowing us to broaden the brands top of top-of-funnel marketing.

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As well as utilising the data from previous campaigns also listened to the clients feedback and insights from previous campaigns, when determining which ads to run initially. We also used introductory offers, such as 4 shirts for £120, to entice and offer an accessible introduction to the brand's audience. These offers helped us foster feelings of initial value and lower the initial barrier to purchase which is sometimes necessary when acquiring new customers that have not yet been converted to loyal customers yet. We also saw great successes with the AI Lookalikes, which became part of our BAU strategy

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Step 1: Account Audit 

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We conducted a thorough review of the client’s existing account to pinpoint areas for improvement and refine the ad strategy.  We began with an audit to analyse the account’s structure and ad performance, identifying weaknesses and opportunities. This assessment provided a roadmap for areas to enhance, focusing on structure optimisation and potential improvements.

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• Key insights into underperforming ads, gaps in audience engagement, and structural adjustments needed for better ad delivery.

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Step 2: Strategic Restructuring of the Ad Account

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• Following the audit, the Vagabond team developed an optimised account structure designed to drive performance, efficiency, and achieve the client’s objective to increase ROAS.

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• This structure would streamline ad targeting and placement, ensuring a cohesive, high-performance campaign architecture. Setting a strong foundation for future campaign efforts.

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Step 3: Data-Driven Performance Analysis

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• Once we had developed a strong foundation, the team combined insights from the audit with data collected using data analysis tools to gain insights into existing ad performance and identify top and underperforming ads. The tools they used included: 

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Motion: This, X tool, enabled in-depth analysis of ad data to gauge which campaigns were performing well and which were underperforming, informing adjustments to future content and targeting strategies.

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Madgicx: An AI-driven Meta Ad optimisation tool, Allowed the agency to test AI-driven “Lookalikes” to broaden our top-of-funnel marketing and optimise their ad campaigns on Meta.

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Using a combination of data-driven insights and close client collaboration, we incorporated client feedback on their successes and promoted offers that effectively introduced the brand to new audiences, like the “4 shirts for £120” campaign.

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results

The ongoing use of AI-driven tactics, particularly AI Lookalike audiences, achieved an increase of 11.6% ROAS and became part of our BAU strategy. This means that the performance of AI-driven lookalike audiences, as measured by ROAS, is 209% better than the performance of interest-based targeting. In other words, AI-driven lookalike audiences are generating more revenue for each dollar spent on ads compared to targeting people based on interests alone.

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