How Refix gained 23 new customers through automated prospect identification

The Customer

Refix specializes in a very specific problem: Amazon sellers who suffer from unjustified negative reviews. These can be fake reviews from competitors, AI-generated mass comments, reviews about late deliveries (which have nothing to do with the product itself), or simply reviews that completely miss the point of the product. Such reviews can massively damage a product's sales, often without the seller being at fault.

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Refix has developed a proven 3-step process: In the first one to two days, negative reviews across 15+ Amazon marketplaces are scanned and analyzed for potential policy or legal violations. On days three to seven, cases are created in collaboration with a partner law firm and submitted to Amazon's community and legal teams. By day 30, queries are answered, additional evidence is submitted, and if there is no response, the case is escalated until the review is deleted. The success rate is impressive: most justified cases actually result in the removal of the harmful review.

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The problem: finding the right customers who don't know they need help

The biggest challenge for Refix was not the service itself, which worked perfectly. The problem was: How do you find the sellers who have this exact problem right now? Amazon has millions of products and hundreds of thousands of sellers. Somewhere out there, there are hundreds or thousands of sellers at any given moment who are suffering from unfair reviews and urgently need help. But they often don't even know that solutions like Refix exist.
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Refix's previous approach was manual and time-consuming: various team members searched Amazon for products, looked at reviews, tried to assess whether there were any problematic reviews, then laboriously researched the seller, looked for contact details, and wrote individual emails. This was not only time-consuming, but also inconsistent. On a good day, an employee might find five potential customers. On a bad day, not a single one.
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What's more, the manual research was superficial. You could look at maybe fifty products a day, not five thousand. The chance of finding exactly the sellers who had the biggest problems (and would therefore be most willing to pay for help) was slim. It was like looking for a needle in a haystack, except you didn't even know which haystack to look in.
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The Refix team knew that somewhere out there, there were hundreds of sellers whose products were suffering from unfair reviews, who were desperately looking for solutions, but the two sides just couldn't find each other. A more systematic, scalable solution was needed.

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Die Lösung: Von 50.000 Produkten zu qualifizierten Gesprächen

bakedwith developed a fully automated system that handles the entire process from product identification to personalized outreach. The system works like a tireless research assistant, searching the Amazon marketplace around the clock for the perfect prospects.

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Here's how the process works in detail:

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Step 1: Massive scraping of Amazon products

The system starts with a broad net: it systematically scrapes over 50,000 Amazon products from relevant categories. These are not chosen at random, but strategically selected according to criteria such as product category, sales volume, number of reviews, and other indicators that point to professional sellers (as they are the relevant target group for Refix).

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For each of these products, the system pulls all available information: product title, description, price, seller information, and, most importantly, all reviews, especially the negative ones.

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Step 2: AI-supported analysis according to Refix criteria

Now comes the clever part. The system uses artificial intelligence to systematically analyze all negative reviews (1 to 3 stars). The AI has been trained using exactly the same criteria that Refix uses in its manual analysis of 15+ marketplaces: potential policy and legal violations.

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The AI recognizes the following categories of problematic reviews:

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Reviews that do not match the product: Reviews that obviously talk about something completely different or are completely generic without specific details about the product.

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Delivery and logistics-related complaints: Reviews about late delivery, damaged packaging, or logistics problems. These are all things that have nothing to do with the product itself and, according to Amazon guidelines, do not belong in product reviews.

AI-generated or spam reviews: Texts with repetitive wording, unnatural language patterns, or generic phrases without specific details.

Potential competitor manipulation: Reviews that mention competing products or suggest deliberate damage.

Legal violations: Reviews with defamatory, offensive, or legally problematic content.

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The AI processes thousands of reviews in seconds and creates a detailed score for each product: How many potentially problematic reviews are there? Which categories do they fall into? How much do they influence the overall rating? Which of them would have a realistic chance of being deleted through the Refix process?

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Step 3: Prioritizing the best prospects

Not every product with a few bad reviews is a good lead. The system intelligently prioritizes based on several factors:

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Number of reviews that can be deleted: The more problematic reviews that violate guidelines or rights are identified, the more urgent the need for action and the more convincing the business case.

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Impact on the overall rating: A product with 4.2 stars that is pushed down to 3.8 by reviews that violate the rules is a better prospect than one with generally poor quality.

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Sales volume and professionalism: Professional sellers with high sales are more willing to pay for solutions and understand the economic damage caused by bad reviews.

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Severity of violations: Reviews that clearly violate guidelines have a higher chance of success in the refix process and are therefore more convincing to the seller.

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Step 4: Identify sellers and find contact details

For the prioritized products, the detective work now begins, fully automatically: The system navigates to the Amazon seller page and extracts the seller name and company. Then the research continues: Using public databases, commercial register entries, and web searches, the system finds the company's website.

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The contact extraction then takes place on the website: The system searches the imprint, contact pages, and team pages for relevant persons, ideally managing directors, e-commerce managers, or owners. The corresponding email addresses are identified using various methods: directly on the website, via email pattern matching, or via professional enrichment databases.

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Step 5: Personalized, data-driven approach

Now comes the crucial moment: making contact. The system creates a highly personalized message for each prospect with specific, product-related information. The email shows the seller exactly:

• Which problematic reviews have been identified and which categories they fall into (delivery problems, non-product-related, potential fake reviews, etc.)
• How these reviews affect the overall rating (for example: “Without these irregular reviews, your rating could be 4.3 stars instead of 3.9 stars”)
• Why these reviews have a realistic chance of being deleted, based on Amazon guidelines and Refix's expertise
• The specific business impact: how much revenue the seller could potentially lose due to the lower rating

The email demonstrates expertise and provides immediate, concrete added value. The seller sees that someone has analyzed their product using the same professional criteria that Refix applies to its paying customers.

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Step 6: Automated review monitoring for new customers

As soon as a seller becomes a customer and activates their products, the same automated process that Refix also offers manually takes effect: The system continuously scrapes reviews across all relevant Amazon marketplaces, uses the same AI analysis to automatically identify new problematic reviews, categorizes them according to deletion potential, and alerts both Refix and the customer. A one-time service becomes ongoing support, without additional manual effort for initial identification.

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The results: From manual search to automated pipeline

The transformation was impressive:

• ‍23 new customers acquired in six months, allowing the team to focus entirely on its core competency: the successful removal of non-compliant reviews‍
• 50,000+ products analyzed using the same professional criteria used in Refix's core service, a scale that would have been impossible to achieve manually‍
• Extremely high response rate: The personalized, data-driven emails achieved response rates of over 30 percent, well above the average for cold outreach.
• Highly qualified leads: Because the system only contacts sellers who have been identified as having specific, actionable policy or legal violations, the conversations were highly relevant from the outset.
• Massive time savings: The team was able to invest the time that was previously spent on manual research into refining the 3-step process and supporting existing customers.
• Scalability: The system can easily be scaled to 100,000 or 500,000 products without additional resources.
• Consistent quality: The AI analyzes according to the same criteria as the Refix experts, but ensures that no product is overlooked.
• Continuous value: Automated review monitoring across 15+ marketplaces keeps customers around longer because they are continuously protected.

The business impact was enormous. Refix was able to massively expand its customer base within a very short time, with customers who had an acute, verifiable problem. This led to high conversion rates and satisfied customers because Refix specifically helped them to have non-compliant reviews removed via the proven 3-step process, thereby protecting their sales.

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Particularly valuable: the quality of the leads. Because the system only contacts sellers for whom it has identified specific problematic reviews based on Refix criteria, neither Refix nor the sellers waste time on irrelevant conversations. Every conversation starts with a clear, data-driven use case based on the same professional standards that Refix applies when actually handling cases.

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What we learned

Scaling requires automation. Manual research may work for ten prospects, but not for ten thousand. To truly find all relevant customers, systematic, automated processes are needed.

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AI can learn and scale expert criteria. The AI analysis is based on the same professional criteria that Refix uses in manual case handling. It makes expertise scalable without compromising quality.

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Multi-marketplace monitoring is complex but valuable. The ability to analyze across 15+ Amazon marketplaces provides a huge competitive advantage and finds problems that local research would overlook.

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Data beats gut feeling. The system finds prospects that human researchers would never have found, simply because it can analyze many more products.

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Timing is crucial. Approaching a seller when they are suffering from non-compliant reviews is a hundred times more effective than approaching them preventively when everything is going well.

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Concrete use cases beat generic pitching. Vague promises are not convincing. Concrete, product-specific data with verifiable policy violations are immediately convincing.

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Categorization increases credibility. Telling the seller not just “You have bad reviews,” but “You have 7 reviews with delivery issues, 3 non-product-related reviews, and 2 potential fake reviews” demonstrates real expertise.

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Personalization scales through automation. Truly personalized emails with product-specific data were too time-consuming to do manually. Automated, they are the standard, and that's what makes the difference in response rates.

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From one-time service to ongoing support. Continuous review monitoring across all marketplaces transforms a one-time service into a long-term customer relationship with recurring value.

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Automation creates space for core competencies. The Refix team no longer spends hours doing manual research, but focuses on what they do best: the 3-step process for successfully removing non-compliant reviews.

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Are you missing out on qualified leads because you can't find them systematically?

You know your target customers are out there, somewhere in the sea of data, profiles, and websites? Manual research eats up time and resources, but only finds a fraction of the prospects that are actually relevant? You could sell much more convincingly if you could show concrete, data-based use cases in your first message?

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We build you a system that systematically finds the right prospects at the right time, enriches them with the right data, and addresses them in a personalized way. Fully automated, scalable, measurable. Whether Amazon reviews, LinkedIn engagement, website data, or other signals: we transform raw data into qualified conversations.

Let's discuss your specific use case in a non-binding conversation.

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