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Using E-commerce Datasets to Predict Demand and Optimize Inventory

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Contributed post.

For B2B SaaS providers and AI-powered platforms serving the retail and commerce sector, inventory optimization is no longer just a retailer’s operational problem, it’s a data intelligence opportunity.

As the e-commerce ecosystem becomes more complex, businesses are increasingly relying on software platforms to transform raw commerce data into accurate demand forecasting and automated inventory decisions.

E-commerce datasets are at the center of this transformation. When structured, richly, and modeled correctly, they enable SaaS platforms to provide predictive insights, power machine learning models, and help reduce costs while improving service levels to retail customers.

This article explains how B2B SaaS and AI platforms can use e-commerce datasets to predict demand and optimize inventory at scale.

Why Demand Forecasting Is a SaaS Problem

Retailers are struggling to manage unpredictable customer behavior, their large SKU catalogs and all of the non-traditional methods that customers are using to buy their product. While many of these challenges are primarily related to a retailer’s operations, solving them requires a B2B SaaS solution that will be able to collect, analyze and make adaptive changes on the fly with little or no human intervention.

The best possible modern B2B SaaS solutions will provide:

  1. Demand forecast signal
  2. Automated Inventory Suggestion
  3. Scenario Modeling & Simulations.
  4. Application Program Interface driven Integration with ERP, OMS, WMS systems.

To be able to provide these capabilities, B2B SaaS solutions will require access to consistent and accurate electronic/multichannel retail data to support the analytics and artificial intelligence predictive capabilities.

Understanding E-commerce Datasets in a Platform Context

E-commerce data consists of multiple live data streams that feed Artificial Intelligence or Software-as-a-Service (SaaS) intelligence layers.

Demand forecasting platforms typically use the following types of datasets:

When combined and/or standardized into a single source of truth across all datasets, they enable downstream analytics and machine learning workflows.

Predicting Demand with E-commerce Data at Scale

  1. Time-series forecasting has moved away from historical average sales data.

Instead of using past averages, software as a service providers utilize advanced time-series modelling to forecast SKU, regional and channel levels together.

The use of e-commerce data to build these models takes into account:

The data used in the AI platforms allows them to develop advanced predictive models that continually retrain using new data which increases the accuracy of the forecasts as they develop.

  1. Behavioral Indicators and Intention-Driven Signals

A major factor that sets e-commerce data apart from other data is the fact that e-commerce has access to demand drivers (the leading indicators of future sales). The data that shows customer behavior (for example, product views, search terms, additions to the shopping cart, etc.) can be used as early notifications about future sales prior to any financial transactions.

For SaaS companies, data has created the potential to:

By leveraging both behavioral indicators and intention-driven signals, SaaS platforms can provide their clients with the opportunity to transition from traditional reactive inventory management practices to proactive planning practices.

  1. Competitive/External Intelligence Drives Demand

Integrating external factors into demand forecasting models enables these models to provide greater accuracy when forecasting demand. As many B2B (Business to Business) e-commerce platforms have recently integrated other competitive e-commerce datasets (such as pricing changes, availability changes, and assortment changes) into their inventory forecasting models.

AI-driven demand forecasting models are now capable of the following:

For SaaS companies, the ability to integrate competitive datasets into their inventory and demand forecasting systems is “highly differentiating” when compared to similar SaaS deliveries or solutions.

Inventory Optimization as an AI Use Case

Predicting demand is only half of the puzzle. The real value of AI for enterprise customers lies in turning forecasts into decision-making data about optimized inventory.

1. Using transactional data obtained from online retailers, artificial intelligence (AI) solutions can evaluate, calculate and provide intelligent recommendations about the point-of-reorder, the quantity of stock to keep as a buffer in case of unexpected increases in demand, and the order quantity to place for each item when a product needs to be replenished. An example of a SaaS solution that could utilize an AI-powered inventory optimization system would be a solution that automates replenishment decisions by using the aforementioned capabilities as well as automatic or semi-automatic purchasing workflows and provides ai for automating replenishment-based on demand volatility.

2. In addition to providing intelligent recommendations for replenishment, AI-powered inventory optimization systems can also create strategies for SKU-level automated inventory management that provide various levels of classification for different manufacturers. The classifications that can be created include demand variability, margin contribution, and lifecycle stage of each product. Once these classifications are created, ai-powered inventory optimization systems will apply a unique stocking strategy for each SKU.

AI-powered inventory optimization systems also provide a way for SaaS vendors to automate their manual planning efforts and enhance customer return on investment through automation at every level of the inventory management process.

3. Multi Location and Omnichannel Optimization

Most enterprise customers use multiple warehouses, fulfillment centers, and marketplaces. E-commerce data with geographic and channel granularity allows a platform to optimize where inventory is placed on the platforms.

It enables:

1. Inventory  placed Allocated smarter by region
2. Lower cost of Fulfillment and More to deliver products faster
3. Ability for Cross-channel inventory to be available to the customer.

These capabilities will be especially valuable for any SaaS platforms that support retailers that operate globally and or retail via the marketplace.

Building AI Models on E-commerce Datasets

Artificial intelligence (AI) is primarily used on the platforms where e-commerce data is used as part of the training set for numerous types of models. Examples of some types of models that utilize e-commerce data for training include:

The large quantity and variety of e-commerce data allows AI to be used to model more complicated non-linear patterns, allowing for the creation of more valid AI capabilities over time, thereby increasing the cost of costs of switching to different providers for customers.

Business Value for SaaS Providers and Their Customers

Platforms that use e-commerce datasets to generate demand and inventory intelligence achieve results you can measure, including:

For SaaS vendors, these benefits support their value proposition, increase the revenue potential per contract, and facilitate broader use of the service by others.

Conclusion

When it comes to B2B SaaS and AI solutions, e-commerce datasets provide B2B SaaS and AI providers with an avenue to generate predictive intelligence and automation, thus translating into the elimination of one of the biggest challenges in retail that has historically occurred across multiple retailers at enormous scale and volumes.

As retailers continue to digitalize operations and demand smarter software solutions, SaaS platforms that invest in high-quality e-commerce datasets and AI-driven analytics will be best positioned to lead the next generation of inventory intelligence.

 

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