Case Study
AI-Powered Inventory Optimization
Overview
A $200M B2B distribution company serving the retail supplies market faced mounting operational inefficiencies as its teams struggled to keep pace with growing product volumes and shifting customer demand. Critical decisions around inventory selection, pricing, and customer targeting took days to finalize, often based on gut instinct rather than data. These delays and manual workflows led to missed revenue opportunities, inconsistent margins, and reduced agility in a competitive landscape. To overcome these hurdles, the company turned to Sphere for a solution that could bring speed, precision, and intelligence into their inventory and sales operations.
Challenges
Accounts team struggled to match new inventory to customer preferences quickly.
2-3 days to finalize inventory selection and pricing between Buying and Accounts teams.
Negotiations relied on gut feeling vs. data-driven pricing, demand, or margin guidance.
Manual processes led to missed opportunities for volume discounts or premium pricing.
Our Solution
Sphere deployed an AI Assistant powered by proprietary accelerator frameworks to:
1. Integrate siloed data
Unified CRM data (sales history, customer behavior) with real-time 3rd-party market trends (eBay, Amazon, competitor pricing).
2. Automate recommendations
Buying Team: Received AI-driven guidance on optimal SKUs to purchase, vendor negotiation ranges, and target margins.
3. Accounts Team
AI identified high-potential customers for new inventory, ideal pricing tiers, and upsell opportunities.
4. Predictive analytics
Forecasted demand surges, price elasticity, and margin thresholds to de-risk decisions.
Key Achievements
85% faster process (3 days → 4 hours) for inventory approval and sales targeting.
18% YoY increase ($36M+) from faster inventory turnover and hyper-targeted customer outreach.
10% improvement in gross margins via optimized vendor negotiations and dynamic pricing
30% reduction in operational costs tied to manual data analysis and trial-and-error processes.
Result
Within 12 months, the company achieved a 25% increase in repeat buyers through personalized inventory recommendations—driving stronger customer relationships and loyalty. Combined with faster decision cycles and margin gains, this contributed to accelerating annual revenue growth from $200M to $250M, while enabling teams to shift focus from manual coordination to strategic execution.
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VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
