How a Retailer Added $100M in Sales with Better Inventory Allocation

It’s not every day that a retailer adds $100M in sales by simply allocating inventory better.

Proxima360 helped a $1B+ sports and outdoor retailer do just that.

Using their proprietary inventory allocation tool, Proxima360 transformed how the retailer managed inventory - turning stockouts and markdowns into higher sales and improved margins.

I had the chance to speak with Anil Varghese, CEO of Proxima360, and Carlos Diaz, Senior Director of Customer Success, to learn how they made it happen.

They shared insights on the opportunities they found, challenges they faced, key lessons for other retailers, and more.

Let’s dive in. 👇

Table of Contents

1. The Discovery Phase

2. Designing the Solution

3. Implementation and Collaboration

4. Training and Adoption

5. Measuring Success

6. Lessons Learned

7. What’s Next

Connect with Proxima360 on LinkedIn and their website.

This conversation has been edited for length and clarity. If you're short on time, skip to the TL;DR section for key takeaways.

1. The Discovery Phase

Q: Can you start by describing the retailer and their specific inventory challenges

Anil: This retailer is a multi-billion-dollar company with a diverse product portfolio that includes sports equipment, outdoor gear, apparel, footwear, and licensed merchandise like NBA team apparel. Their inventory spans both soft goods, such as clothing and footwear, and hard goods like kayaks and camping gear.

The retailer faced a unique set of challenges due to varying customer demographics across its locations. For example, apparel sizing needs in border states skewed toward smaller sizes (XS–L), while in Louisiana, demand was for larger sizes (M–XXL). The existing allocation system couldn’t account for these regional differences, leading to stock imbalances.

Some stores experienced frequent stockouts of popular items, while others were overstocked with products that didn’t sell. This mismatch not only frustrated customers but also created additional costs due to markdowns and inventory transfers between stores. The retailer needed a solution to better align inventory with demand at each location.

Q: How did the engagement with the retailer begin? What were the initial steps

Anil: The engagement started in April 2019. The retailer approached us with a pressing challenge: Could we improve their inventory allocation before the upcoming holiday season? This gave us a tight timeline of less than six months.

The first step was a detailed discovery phase. We conducted workshops with key stakeholders, including the retailer’s business and technical teams, to understand their existing processes and pinpoint pain points. We also analyzed their data systems to assess readiness and identify gaps.

2. Designing the Solution

Q: What made Allocation360 the right solution for this retailer?

Anil: Allocation360 is built for flexibility, which was crucial for this retailer. It can operate as a standalone tool or integrate seamlessly with existing big-box systems. This allowed the retailer to keep their ERP system in place while using Allocation360 as a top-layer solution to enhance their inventory allocation processes.

We tailored Allocation360 to address three key areas:

1. Size Profile-Based Allocation: This ensured that each store received the right sizes based on local demand, eliminating stockouts of popular sizes and reducing overstock of less popular ones.

2. Scenario Planning: The tool allowed the retailer to simulate various allocation strategies before high-demand periods, like Black Friday, enabling them to optimize stock levels proactively.

3. Omnichannel Fulfillment: Allocation360 supported initiatives like Buy Online, Pick Up In Store (BOPIS) while preserving floor stock for walk-in customers, creating a seamless omnichannel experience.

Q: What kind of data did the retailer need to provide for Allocation360 to work, and what challenges did you encounter with it?

Anil: Data quality was one of the most critical aspects of this project, and it’s something that any retailer undertaking a similar journey needs to focus on. We required foundational data, including:

• Product information: Complete and accurate details about each item.

• Store data: Attributes and performance metrics for each location.

• Historical sales data: Trends and patterns that would inform allocation decisions.

• Inventory data: Real-time insights into what was available at distribution centers and stores.

• Size data: Specific sizing information, which was especially important given the retailer’s diverse customer demographics.

However, as is often the case with large retailers, the quality of the data wasn’t ideal. 

Over time, decisions, system changes, and manual processes can introduce inconsistencies and gaps in the data. We encountered missing attributes, inaccurate records, and incomplete sales histories.

To address these issues, we collaborated closely with the retailer’s technical team, business users, and quality control team. The process was highly iterative: we cleansed and standardized the data, loaded it into the tool, and validated it to ensure it produced accurate and actionable results.

Q: How did collaboration help with the data challenges?

Anil: Collaboration was key. All stakeholders—from business teams to technical teams—worked together in a single room with two whiteboards. It was a real-time problem-solving environment. If we encountered a gap or issue, we could identify it, discuss it, and resolve it on the spot.

This close collaboration ensured that the data cleansing and validation process didn’t create delays. More importantly, it allowed us to align on expectations and ensure that the tool could make precise decisions based on accurate, reliable data.

3. Implementation and Collaboration

Q: What was the timeline, and how did you manage such a fast-paced implementation?

Anil: The timeline was extremely aggressive. We started in April 2019 and had the first phase—focused on soft goods—live by mid-September, just in time for the holiday season.

This required a highly collaborative approach. Everyone—from power users to leadership—was involved from day one. We worked out of a single room with two whiteboards, solving issues in real-time. This level of engagement ensured that we stayed aligned, hit our milestones, and delivered on time.

Q: What teams from the retailer were involved in the implementation?

Anil: The project’s success relied heavily on cross-functional collaboration. Key teams from the retailer’s side included:

• Merchandising Team: They provided insights into product demand, regional preferences, and historical trends.

• Technical Team: This group handled data integration and ensured seamless connectivity between Allocation360 and the retailer’s ERP system.

• Store Operations Team: They provided feedback on store-specific challenges, such as space constraints or staffing considerations.

• Allocation Team: These were the end users who directly interacted with the tool. Their input was vital for making the interface intuitive and user-friendly.

• Leadership: The C-suite played a crucial role by prioritizing the project, allocating resources, and empowering teams to make decisions quickly.

All these teams worked in lockstep, often sharing a single room during critical phases to enable real-time problem-solving.

Q: Once you implemented Allocation360, how did you approach the pilot phase and iterate based on feedback?

Anil: We implemented Allocation360 just before the holiday season, so it was crucial to measure its performance right away. The tool was first used during one of the busiest periods—Black Friday through Cyber Monday—and we closely monitored how it performed during that high-pressure timeframe.

During this phase, users provided continuous feedback. For example, they shared insights about how the tool handled certain scenarios, what features they found intuitive, and what areas needed improvement.

Because of the holiday season "freeze"—when retailers avoid making major system changes—we couldn’t roll out updates immediately. However, our development team worked throughout the holidays to address the feedback. As soon as the freeze ended, we implemented those updates to refine the tool further.

Q: Was this feedback process collaborative?

Anil: Absolutely. This was another example of how collaboration drove the project’s success. Users felt comfortable sharing their honest opinions, and we worked closely with them to make adjustments. By keeping the feedback loop open and active, we built a tool that truly worked for the people using it every day.

4. Training and Adoption

Q: Who were the primary users of Allocation360, and what training did they need

Anil: The primary users were allocators—individuals responsible for distributing inventory from distribution centers to stores and fulfilling e-commerce orders. These users are typically detail-oriented and analytical, as they need to interpret the tool’s recommendations and make final decisions.

To train the users, we used a "train-the-trainer" approach. Power users who were involved from the start became internal champions, conducting classroom sessions for different teams.

Resistance to change is natural. Some allocators were hesitant to let go of the old tool. To address this, we ran reports to identify who was still using the old system and engaged them individually to understand their concerns. Their feedback was incorporated into Allocation360, which eventually won them over.

5. Measuring Success

Q: What were the specific results, and how did Allocation360 achieve them

Carlos: The results were incredible:

• $100M in additional sales: By ensuring the right products were available at the right stores, we increased full-price sales significantly.

• $80M saved in markdowns: Fewer items needed to be discounted or liquidated because the inventory was allocated accurately.

• 25% improvement in BOPIS efficiency: The tool ensured that inventory for online orders didn’t deplete in-store stock for walk-in customers.

Anil: Beyond the numbers, the tool reduced operational inefficiencies. Allocators no longer needed to rely on manual spreadsheets or ad hoc reporting, which minimized human error and saved time.

6. Lessons Learned

Q: What factors were critical to the project’s success?

Anil: Three key factors stand out:

1. Leadership Focus: This project was the retailer’s top priority, with full backing from the C-suite. That focus ensured alignment on resources, budgets, and decisions.

2. Cross-Functional Collaboration: All teams—from merchandising to IT—worked as a single unit. This "one team" mindset eliminated silos and accelerated problem-solving.

3. Empowered Decision-Making: Team members on the ground were empowered to make decisions in real-time, which kept the project moving forward without delays.

Carlos: I’d add that having a dedicated team made a huge difference. Many retailers try to implement big initiatives while expecting their teams to manage day-to-day operations. This leads to delays and compromises. A focused team ensures accountability and better outcomes.

7. What’s Next

Q: What trends or technologies are shaping the future of this space?

Anil: AI and machine learning are at the forefront. Our platform, hosted on Google Cloud, is leveraging advancements in quantum computing to improve predictive analytics. This will enable even more accurate demand forecasting and allocation strategies.

Carlos: Another area we’re exploring is tax optimization. We’re developing features that allow retailers to create intercompany purchase orders, helping them save millions annually on imports and operations. This is a game-changer, especially for global retailers navigating complex supply chains.

TL;DR - Key Takeaways for Retailers

1. Data as the Foundation

   • Clean and reliable data is critical: Foundational data—like product attributes, store information, sales history, and inventory levels—needs to be accurate and standardized. Poor data quality creates roadblocks that can derail even the best tools.

   • Collaborate on data readiness: Invest in a collaborative approach to cleanse, structure, and validate data before implementation. This ensures the tool produces actionable insights.

2. Collaboration Drives Success

   • Cross-functional teamwork is essential: Include all key teams—merchandising, technical, store operations, allocation, and leadership—in the project. Siloed efforts lead to inefficiencies and missed opportunities.

   • Break silos with shared ownership: Ensure all teams work as a single unit with shared goals. Avoid an "us vs. them" mentality between vendors and internal teams.

   • Real-time problem-solving accelerates progress: Collaborative workspaces, like Proxima360’s single-room approach, allow for faster resolution of challenges.

3. Leadership and Focus Matter

   • Top-down prioritization ensures alignment: Full support from the C-suite guarantees that resources, budgets, and decisions align with project goals.

   • Allocate a dedicated project team: Avoid overburdening teams with both day-to-day operations and project responsibilities. A focused team drives accountability and better outcomes.

4. Empower Decision-Making

   • Enable real-time decisions: Empower team members on the ground to make decisions as challenges arise. Delays in decision-making slow progress and jeopardize timelines.

   • Fast feedback loops drive iteration: Regular feedback and iterative development ensure the tool stays aligned with the business’s evolving needs.

5. User Adoption is Critical

   • Handle change management thoughtfully: Resistance to new tools is natural. Engage users early, understand their concerns, and incorporate feedback to ease the transition.

   • Train effectively: Use a "train-the-trainer" model with power users leading training sessions. Follow up with open forums for ongoing support as users adopt the tool.

6. Results Speak for Themselves

   • Sales and margin improvement: By aligning inventory to demand, retailers can significantly boost full-price sales and reduce markdowns.

   • Operational efficiency gains: Automation and data-driven insights free up resources and reduce human error, improving overall efficiency.

7. Learn from the Process

   • Invest in collaboration and communication: The success of this project hinged on seamless communication between all teams—business users, technical teams, and leadership.

   • Focus on the people, not just the tech: Even the best tools won’t succeed without the right team dynamics and a unified vision.

8. The Future is AI-Driven

   • AI and machine learning are transformative: These technologies are reshaping inventory management by enabling better demand forecasting, scenario planning, and real-time optimization.

   • Explore additional tools for cost savings: Features like intercompany purchase orders can help global retailers save millions annually on imports and operations.

PS: Do you have any questions for Anil? Reach out to me at [email protected] and I can pass it on.