Seller Tools

Location:

United States

Partnership period:

2020-2021

Industry:

Ecommerce

Technologies:

Python, React, MongoDB, Django,

Team Size:

1 Manager, 2 Frontend Developer, 2 Backend Developer, 1 Database Engineer

Competitive Environment

The client operated an e-commerce data analytics and optimization platform tailored for Amazon's vast third-party seller ecosystem. Within the explosive rise of Amazon as a consumer juggernaut, over 5 million merchants leveraged the marketplace to tap surging e-commerce demand. However, the increasing complexity of managing a successful Amazon store posed steep challenges. Sellers struggled to manually track chaotic inventory levels, sales velocities, advertising performance, competitor activities, and reviews across thousands of product pages in their catalog.

This overwhelming volume of multivariate data blinded many to critical opportunities and risks. While enterprise-grade business intelligence solutions emerged, subscription costs and data science resource requirements put them out of reach for most lean SMB merchants.

The client's platform aims to address this gap by unlocking insights and efficiency from Amazon data to level the playing field. However, fragmented insights across dashboards failed to provide the clarity needed for time-pressed sellers to act. The core value propositions around identifying data-driven portfolio optimization opportunities and automating tedious processes struggled to resonate without consultative hand-holding. To sustain competitive positioning against rivals and prove commercial potential to investors, an integrated hub simplifying data-driven management through machine guidance became imperative.

Problem Statement

Fragmented tools and dashboards overwhelmed merchants rather than harnessing data as a strategic asset. With no single source of truth, sellers struggled to make informed decisions managing extensive multifaceted Amazon stores spanning thousands of ASINs. Lacking full visibility into inventory positions, sales trends, advertising performance, competitor actions, and reviews led to revenue leakage and reactive moves.

Required a simplified self-serve experience providing clarity for SMBs to benefit from enterprise-grade capabilities tailored to their budget. Burying complex analytics instead of turning findings into plays, the existing platform failed to help merchants quantify or capture lucrative opportunities independently. An integrated data-to-guidance solution sought to combine portfolio analytics, competitive benchmarks, machine recommendations, and workflow automation tools into a commercial engine that creates remarkable business outcomes from previously untapped signals.

Solution and Objectives

Consolidate existing capabilities into an integrated suite of data-driven management tools tailored to Amazon sellers’ needs around business intelligence, safeguards, task automation, and omnichannel optimization. Transform chaotic multivariate data points into clear, actionable recommendations that create remarkable commercial outcomes. Package sophisticated analytics and machine guidance into easy-to-follow playbooks lifting seller strategy beyond guesswork heuristics. Drive efficiency through reliable store data connections and automation so clients can focus on high-judgment tasks.

Goal to efficiently equip over 5,000 merchants managing over $2B in Amazon revenue within the first 18 months post-launch by providing an enterprise-grade self-serve digital commerce suite catered to lean SMB budgets. Support 20%+ month-over-month subscriber growth by quantifying value in unlocking previously opaque profit growth opportunities at scale. Expand TAM long-term by localizing capabilities for international Amazon marketplaces.

Technology Planning

Built on scalable cloud infrastructure leveraging Django, MongoDB, Express, and React to facilitate secure and reliable store data connections serving thousands of unique Amazon accounts. Combined analytical databases for query efficiency with flexible NoSQL architecture to handle unstructured large-scale datasets fueling machine learning algorithms. Kubernetes cluster managed versioning and traffic load balancing while Docker containers enabled rapid parallel capability development sprints using microservices principles.

Applied agile product methodology to systematically evolve platform priorities, emphasizing foundational data services followed by the highest complexity tools integrating predictive analytics and machine learning models to prescribe lucrative business growth opportunities. CI/CD automation streamlined coding while extensive unit testing and monitoring procedures ensured system stability during growth.

Phases

Phase 1

Phase 2

Phase 3

Conclusion and Results

In 18 months after its launch, over 5,000 merchants trusted the platform to manage over $2B in Amazon revenue by leveraging tools tailored for e-commerce growth. The 20%+ month-over-month subscriber growth trajectory highlights strong product-market fit as existing clients also expanded usage rapidly after onboarding.

Sellers gained access to enterprise-grade business intelligence capabilities catered to lean SMB budgets, quantified in strong engagement metrics around identifying advertising performance gaps and demand forecasting for inventory planning and logistical purposes. Unlocking growth opportunities through leveraging data analytics at scale, the platform aims to level the playing field against mammoth competitors through machine leverage.

Key Challenge

Early technology hurdles existed around facilitating reliable, scalable store data connections across thousands of disparate Amazon accounts to fuel the platform's multivariate analytics engines while balancing security and permissions compliance.

Continual prioritization tensions posed challenges in allocating finite development resources between short-term subscriber feature requests and long-term high-complexity capabilities enabling orders-of-magnitude commercial opportunity through machine intelligence over heuristics.

Next Steps

Besides localizing capabilities for international Amazon marketplaces, the significant untapped potential exists in further personalizing automated insights and recommendations to each seller's specific business structure, stage, and objectives using machine learning.

Exploring embedded software partnership distribution models to provide suite access to agencies managing large seller portfolios also seeks to strategically drive subscriber growth.

Seller Tools

Seller Tools

Competitive Environment

Problem Statement

Solution and Objectives

Technology Planning