Predictive Analysis with AI: How to Optimize Your E-commerce Inventory

Discover how artificial intelligence can help you predict product demand and efficiently manage your warehouse.

---

About the author: Lorenzo Bortolotto, Alpenite

---

What is Predictive Analysis and Why is it Important for Your E-commerce?

Predictive analysis is the use of data mining, statistics, and artificial intelligence techniques to analyze historical and current data and predict the future behaviour of customers, markets, and processes. For an e-commerce business, predictive analysis can be a valuable tool to optimize inventory management, determining the quantity and types of products to stock to meet demand.

Optimal inventory minimizes storage and procurement costs, avoids stockouts and excess inventory, and maximizes sales and profits.

How Predictive Analysis Works for Inventory Management

Predictive analysis relies on machine learning algorithms that learn from past and present data to generate forecasts on various aspects of demand, such as volume, seasonality, segmentation, price sensitivity, and promotions. These predictions can be used to calculate the optimal inventory level for each product based on factors like probability of sale, purchase cost, profit margin, delivery time, expiration date, etc. Additionally, predictive analysis can help identify the most profitable products, emerging trends, cross-selling and up-selling opportunities, and the most effective pricing and marketing strategies.

Advantages of Predictive Analysis for Your E-commerce

Predictive analysis can bring numerous advantages to your e-commerce, including:

1. Reducing inventory management costs by avoiding waste, losses, damages, and obsolescence.
2. Increasing sales and profits by offering the right products at the right time and price.
3. Improving customer satisfaction and loyalty by ensuring the availability of desired products and swift deliveries.
4. Optimizing strategic decisions based on reliable and up-to-date data.

Implementing Predictive Analysis in Your E-commerce

To implement predictive analysis in your e-commerce, you need to:

1. Have an e-commerce platform that allows you to collect and integrate data from various sources such as the website, social media, customer feedback, suppliers, competitors, etc.
2. Use a predictive analysis solution that suits your needs, budget, and level of expertise. Choose from cloud, on-premise, or hybrid solutions, and ready-made or custom solutions.
3. Define goals and metrics for predictive analysis, monitor results, and performance.
4. Test and validate predictions by comparing them with real data and customer expectations.
5. Act based on predictions by adapting inventory management, pricing, and marketing strategies.

Moreover, having an experienced partner in predictive analysis projects is often a competitive advantage. Such a partner can offer qualified and personalized consultation, supporting the client in every project phase—from defining the problem and goals to selecting the most suitable solutions to implementation and maintenance, ensuring quality, security, and effectiveness.

Predictive analysis requires continuous model verification to ensure they are updated, aligned with reality, and in line with business objectives. A dedicated team can monitor model performance, detect anomalies or deviations, and make necessary adjustments to optimize results.

Lastly, a partner can provide the necessary skills for managing and using the predictive analysis solution through training, assistance, and knowledge transfer, making the client independent in system management to fully exploit predictive analysis potential for their business.

A real Use Case example

Now, let’s present the use case of Modayola, a multi-brand online platform offering high-quality clothing and accessories.

Before describing Modayola’s benefits and results, it’s essential to note that Modayola is a fictional name used to preserve the company’s privacy, with a non-disclosure agreement (NDA) in place to protect the competitive advantage derived from the investment in predictive analysis.

Modayola faced the challenge of managing an extensive and diverse catalog with thousands of products from various suppliers. Additionally, it had to consider the ever-changing and heterogeneous purchasing preferences and behaviours of its customers.

To tackle this challenge, Modayola turned to Alpenite, a company within the Arsenalia group specializing in AI applied to the e-commerce sector. Alpenite’s idea for Fashionista was to create a predictive system integrated with e-commerce and the company’s ERP, utilizing artificial intelligence to anticipate the demand and supply for each product, streamlining the purchasing process from various suppliers.

The project unfolded in several phases.

1. Initially, Alpenite studied the process and collected and analyzed Modayola’s historical and current data related to sales, inventory, costs, suppliers, customers, feedback, and marketing campaigns.
2. Next, Alpenite constructed and trained predictive models using machine learning and deep learning techniques to generate accurate and reliable forecasts for various aspects of Fashionista’s business.
3. Finally, Alpenite integrated the predictive models into Modayola’s ERP, creating interactive dashboards and automatic alerts to facilitate decision-making and action.

Predictive analysis brought significant benefits to Modayola. After the first year, online revenue increased by approximately 20%, while profit margin increased by 10%. It was able to reduce waste and excess inventory, optimize collaboration with suppliers, and also respond more accurately to customer needs.

---