Deconstructing Competitor ROI: Pinpointing High-Value Influencers by Scraping DTC Campaign Performance

Our client, a respected legacy fashion house, faced a classic modern dilemma. They were being consistently outpaced in the digital space by a handful of agile, direct-to-consumer (DTC) competitors. While our client invested heavily in traditional marketing and a few big-name 'macro-influencers' on Instagram, their online sales growth was stagnating. They knew their competitors were doing something different with influencer marketing, but they couldn't see the full picture. Their strategy felt like guesswork, and the cost per acquisition was becoming unsustainable.

They came to us with a clear objective: 'Show us our competitors' influencer playbook.' They didn't just want a list of names; they wanted to understand the strategy—which platforms, which type of influencers, and for which products. Our team at Iceberg Data proposed a comprehensive competitive intelligence project. We would systematically scrape and analyze the public social media activity of their three primary DTC competitors across both Instagram and TikTok.

Our first step was to build targeted scrapers to monitor the feeds of hundreds of influencers known to work with these brands. The scrapers were designed to identify sponsored posts by detecting keywords like '#ad' and '#sponsored', as well as direct brand tags. Crucially, we also configured them to extract any unique discount codes mentioned in the captions or comments, such as 'KATE20', as this would be a key indicator of a performance-based partnership.

The data began to pour in, and within weeks, a starkly different picture emerged. While our client was spending six figures on Instagram influencers with millions of followers, their competitors were executing a high-volume strategy with 'micro-influencers' (those with 50k-100k followers) almost exclusively on TikTok. This was a revelation. The data showed that these micro-influencers had significantly higher engagement rates—often exceeding 4-5%, compared to the sub-1% our client was seeing from their mega-influencers.

One influencer, whom our report identified as '@style_maven_kate', was a prime example. With just over 75,000 followers, she had created a video for 'dtcbrand-a.com' showcasing a new line of sneakers. Our scraped data, reflected in our output JSON, showed the post had amassed over 580,000 views and 45,000 likes. The comments were filled with users confirming they had used her 'KATE20' discount code. This single post from a relatively small creator likely drove more direct sales than one of our client's massive, expensive Instagram campaigns.

By aggregating thousands of such data points, we delivered a strategic dashboard that broke down competitor strategy by product category. We discovered that for footwear, TikTok video content from micro-influencers delivered the highest ROI. For handbags, however, Instagram Stories from 'mid-tier' influencers were more effective for competitors. This granular insight was the playbook our client had been missing.

Armed with this intelligence, the client completely overhauled their influencer marketing strategy. They paused their high-cost contracts and reallocated the budget to build a network of 50 micro-influencers on TikTok. The results were immediate and transformative. Within the first quarter, their average campaign engagement rate shot up by 25%. More importantly, by partnering with creators whose rates were a fraction of their previous partners', they reduced their overall influencer acquisition cost by a staggering 40%. They were no longer guessing; they were making data-driven decisions based on a clear view of the competitive landscape we had provided.