Case Study: Achieving 30% Decrease in Stockouts With SKU-Level Demand Forecasting

Understanding the Challenge

Stockout Definition and Impact

Stockouts happen when stock on hand goes to zero and no customers can buy a product. Stockouts do more than inconvenience retailers—they lead to chain reactions of economic loss impacting customer retention and customer lifetime value. IHL Group had found that inventory distortion, comprised of stockouts and overstocks, was to cost retailers nearly $1.8 trillion in 2023. And with 25% of consumers less likely to buy when stock is low according to research, and a whopping 65% rating a poor view of any brand experiencing frequent stockouts, it's clear that out-of-stock doesn't pay. This is where ecommerce analytics and e-commerce analytics become critical for modern commerce operations.

The Mid-Sized Retailer's Situation

As with many retailers, this mid-market company dealt with the age-old inventory management problem of maintaining enough stock to supply demand without carrying too much excess inventory. Lacking precise forecasting of demand by stock keeping unit (SKU) through predictive analytics ecommerce methods and ecommerce data analytics, they had too many out-of-stocks on high-volume products and too much slow-moving inventory for others. Their ecommerce platform needed better analytics in ecommerce to optimize the customer journey and reduce cart abandonment.

The SKU-Level Demand Forecasting Solution

What is SKU-Level Demand Forecasting?

SKU-level demand forecasting includes future sales prediction for specific stock keeping units (SKUs) rather than product categories. The granular nature of this approach acknowledges that every product variant; such as colours, sizes and configurations have varying demand patterns based on variables like time of year, promotions and customer preferences. Modern ecommerce tools and ecommerce software like Shopify analytics, Google Analytics ecommerce, and platforms similar to Triple Whale, triplewale, and tripple whale provide the foundation for this level of ecom analytics.

Key Elements that Make for a Great SKU Forecasting Model Include the Following Must-Have's:

• Historical Sales Analysis: Learn on how to look at SKU level performance data and draw patterns and trends from it using ecommerce tracking and ecommerce performance analytics.
• Integration of External Factors: Include factors like time of the year, promotions, weather and market state through comprehensive ecommerce data analytics and social media analytics including TikTok analytics.
• Real-time Data Processing: Constant update of forecasts relying on the current sales pace and the changes within the market, similar to what whale ai and triplewahle platforms offer for ecommerce insights.

Benefits of SKU-Level Approach:

While aggregate forecasting can be used to get enough insight, SKU level forecasts allow you to see everything in granular detail so that you can make decisions about inventory at the product variant level through analytics in ecommerce. This accuracy ensures that retailers are able to hold the right level of stock for their fast moving items, but to invest less into the slower selling alternatives, optimizing their ecommerce website performance and overall commerce operations.

Implementation and Results

30% Stockout Reduction Achievement

When the retailer set up demand forecasting at SKU level using predictive analytics ecommerce methods and ecommerce anlytics, stockouts decreased significantly by 30% in only three months. This betterment was achieved through a variety of mechanisms:

• Proactive Replenishment: The model maintained dynamic reorder points per SKU considering sales velocity, lead times and demand variability through real-time ecommerce tracking. Proactive ordering meant the most popular lines were always in stock when they needed to be, not waiting on urgent orders, improving customer retention rates.
• Live Monitoring: Live stock monitoring enabled immediate catch of stock levels per each SKU to respond rapidly for unpredictable spikes on demand using ecommerce performance analytics. Managers received automatic alerts if stocks were approaching minimum limits, helping them make decisions about reordering in a timely manner through their ecommerce tool dashboard.

How SKU-Level Forecasting Reduced Costs:

• Optimized Inventory: The retailer developed inventory forecasting algorithms through ecommerce data analytics that allowed them to efficiently manage their set levels to ensure they had just enough or slightly more stock then what would have in a perfect world score. They also decreased investment in non-selling items and maintained the right level of inventory for fast movers based on individual product demand pattern, leveraging insights from their ecommerce platform.
• Warehouse Efficiency: Improved inventory ratio lowered warehouse congestion and handling cost. But by keeping just the right amount of stock, they could maximize their use of warehouse space and minimize the labour demands of managing unsold inventory using ecommerce software optimization.
• Optimisation of working capital: A 12% cut in holding costs released funds for investment elsewhere in the business resulting in better financial agility and return on assets, improving overall customer lifetime value metrics.

Operational Excellence: 8% Improve the On-Time Delivery

The Relationship Between Inventory and Delivery Performance

Promoters of Better Delivery Performance:

• Backorders Reduced: With less stockouts, the retailer saw a significant reduction in backorders that were holding customer shipments, improving the customer journey experience. By forecasting at the SKU level through ecommerce analytics, popular products were always in-stock and available for immediate sale.
• Improved Supplier Communication: When the demand forecast was accurate using predictive analytics ecommerce and ecommerce insights, communication with suppliers improved and they were better able to maintain delivery schedules more consistently and lead time variability decreased. This synchronising of activities facilitated smooth inventory movement and enabled the timely delivery promises.

Industry Context and Validation

Comparable Success Stories

The retailer has obtained results on par with, or better than, those achieved by other manufacturers implementing demand forecasting through advanced ecom analytics and ecomerce analytics. Companies that meet their forecasts at this level of accuracy report dramatic operational improvements:

Amazon: At 95% making the tenants demand forecast, employed just in time inventory a year saved $5 billion reduced overhead and actually increased sales by over 10% using sophisticated analytics in ecommerce and e-commerce analytics platforms.

Research-Backed Benefits

Academic research reinforces the retailer's findings. According to studies, businesses with reliable demand planning through ecommerce data analytics are able to lower inventory costs up to 20% and improve order fulfillment rates. Further research suggests that small gains in the accuracy of predictions can result in significant payoffs—Roughly a 15% forecast-accuracy gain can generate pre-tax profit improvements of 3% or more, directly impacting customer retention and reducing cart abandonment.

How trivas.ai Drives Successful SKU-Level Demand Forecasting

Advanced Analytics Platform

trivas.ai delivers e-commerce analytics features that would have helped this retailer succeed. The AI-driven cross features of the platform are an ideal fit with what is needed for accurate demand forecasting at the SKU-level, comparable to solutions like Triple Whale, triple whale, and other leading ecommerce tools:

• Real-Time Performance Insights: trivas.ai provides single-panel views on e-commerce performance with instantaneous feedback through all channels including Shopify analytics, Google Analytics ecommerce, TikTok analytics, and social media analytics. This functionality allows retail customers to constantly monitor the performance of their SKUs and tune forecasts based on the current market through comprehensive ecommerce tracking.
• AI-Based Recommendations: Intelligent AI assistant on this platform brings automatized recommendations using powerful analytics, similar to whale ai capabilities. For demand prediction, these amount to actionable ecommerce insights such as inventory optimization, reorder moment and stock level adaptation, helping improve marketing attribution and email marketing analytics.
• API Integration Capabilities: trivas.ai has flexible APIs, robust developer help and experience integrating with existing inventory management systems to ensure smooth data flows for better forecasting capability. This includes integration with popular ecommerce platforms and ecommerce software, with a comprehensive GA4 guide for seamless setup.
• Multi-Channel Analytics: Track performance across your entire ecommerce website with unified analytics in ecommerce, including influencer marketing and marketing analytics to understand the complete customer journey and optimize customer lifetime value.

By leveraging trivas.ai's complete analytical platform with predictive analytics ecommerce capabilities and comprehensive ecommerce performance analytics, retailers deploying SKU-level demand forecasting should see increased precision, operational efficiencies and advanced inventory optimization algorithms. The AI-driven analytics and 24/7 performance monitoring on the platform are the technological layer that will enable these results from this case study – and blow them away in fact. Whether you're managing cart abandonment, optimizing influencer marketing campaigns, or analyzing marketing attribution across channels, trivas.ai provides the ecommerce insights needed for success in today's competitive commerce landscape.