Case Study: Revolutionizing Product Lifecycle Management with AI-Driven GTM

Case study demonstrates how Angmar's integrated Stack and Pipe services can drive efficiency and revenue growth

This case study demonstrates how Angmars’s integrated Stack and Pipe services, enhanced by embedded SDR expertise, can revolutionize Product Lifecycle Management (PLM) SaaS adoption within the fashion industry, driving both efficiency and revenue growth.

The Solution: Precision Targeting and Specialized Outbound (Stack & Pipe with Optional Embedded SDRs)

Angmars partnered with the client to overcome these obstacles by implementing a tailored strategy leveraging our Stack and Pipe services, with the option of embedded Sales Development Representatives (SDRs).

Stack: Building an AI-Powered GTM Foundation for Fashion

We began by deploying an AI-powered GTM tech stack specifically configured for the fashion industry. This involved integrating market intelligence tools with predictive analytics capabilities to identify emerging trends, analyze competitor strategies, and pinpoint fashion brands most likely to benefit from advanced PLM solutions. The system also automated lead scoring, prioritizing prospects based on their digital footprint and intent signals within the fashion ecosystem.

Pipe: Hyper-Targeted Outbound with Sector Expertise

Leveraging the precise insights from the Stack, our Pipe service developed and executed highly targeted outbound campaigns. These campaigns were meticulously crafted to resonate with fashion industry decision-makers, emphasizing how the PLM solution directly addressed their specific pain points, such as improving supply chain visibility, accelerating time-to-market, and ensuring sustainability compliance. As an optional enhancement, an embedded SDR team was deployed. These specialized SDRs possessed deep knowledge of the fashion and apparel sector, enabling them to engage prospects with highly relevant value propositions and navigate their unique procurement processes, ensuring only high-quality leads reached the sales team.

Client has requested to remain anonymous.

The Challenge: Slow Adoption and Inefficient Leads in Fashion PLM

A B2B SaaS company, specializing in Product Lifecycle Management (PLM) solutions for the fashion industry, faced significant headwinds. Despite offering a cutting-edge platform designed to streamline design, development, and production, they struggled with slow adoption among potential clients. The fashion sector, often characterized by traditional processes and resistance to rapid technological change, presented a unique challenge. This resulted in prolonged sales cycles and a high volume of unqualified leads, making it difficult to demonstrate immediate Return on Investment (ROI) and scale their Go-To-Market (GTM) efforts effectively.

The Results: Accelerated Sales Cycles and High-Value Pipeline

Q: What was the primary challenge faced by the client in the fashion PLM space?

A: The client struggled with demonstrating the immediate value of their PLM SaaS to fashion brands, leading to prolonged sales cycles and a high rate of unqualified leads. The industry's inherent resistance to rapid technological change exacerbated these issues.

Q: How did Angmars address these issues with the ‘Stack’ service?

A: We deployed an AI tech stack that provided deep market intelligence on fashion industry trends, competitor analysis, and predictive insights into which brands were most likely to adopt PLM solutions. This allowed for precise targeting and messaging, transforming their understanding of the market landscape.

Q: What role did the ‘Pipe’ service and optional embedded SDRs play in improving lead generation?

A: Leveraging ‘Stack’ insights, we launched hyper-personalized outbound campaigns. The embedded SDR team, specialized in fashion B2B, engaged prospects with tailored value propositions, resulting in a 60% increase in qualified meetings within the first quarter.

Q: What were the measurable outcomes of this engagement?

A: The client saw a 35% reduction in sales cycle length and a 50% increase in qualified pipeline value within six months, demonstrating a significant shift towards efficient and effective GTM in a niche market.

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Case Study: Accelerating Pipeline Generation with AI-Powered Outbound