CASE STUDY

COVID-19 Impact on eCommerce Retail: Increasing Average Order Value (AOV) by 53% Using a Holistic Demand Forecasting Framework

Apr 27, 2020

eCommerce Retail: Engagement Summary

In the wake of the coronavirus outbreak, an eCommerce retail industry client based out of Germany needed to rapidly redefine its demand planning and demand forecasting framework. We built a data repository and a robust demand forecasting framework for examining data and forecasting demand for different product categories. We also deployed a reporting process for capturing demand data and providing updates on future demand patterns. As a result, the e-commerce company was able to provide an effective response to the health crisis and cater to the dynamic demands of its customers.

ecommerce retail

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About the Client

The client is a leading German eCommerce retail company specialized in the online sale of home improvement items, personal care products, accessories, sports goods, and daily essentials. Founded in 2003, the eCommerce retail company has expanded its operations to five new countries with spin-offs and subsidiary business units spread across Europe.

Business Challenge

Driven by the rise in internet penetration, smartphone usage, shift to digital wallets & online payments, and language diversity on eCommerce platforms, the eCommerce retail industry in Germany has grown substantially and is poised to witness an accelerating growth margin in 2020. Fuelled by the ongoing COVID-19 crisis the eCommerce retail industry is also expected to witness an all-time high demand rate globally. While the rising demand brings in new opportunities for eCommerce companies to improve margins, it also sheds light on a few inherent challenges that the eCommerce industry is trying to address for quite some time. Some of the issues that confront the sector include high-cost pressures, high returns, inadequate workflows, and incompetent demand forecasting frameworks.

Tackling these challenges and generating accurate sales and demand forecasts was crucial for an eCommerce retail industry client based out of Germany. The pandemic outbreak had resulted in a huge surge in demand for certain product categories due to which the client faced several challenges in fulfillment and demand management. This is when the eCommerce company approached us looking to leverage our demand forecasting capabilities to devise a robust demand forecasting framework that could help them accurately forecast sales and demand for different product categories.

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Solution Offered

A detailed analysis of the client’s challenges revealed that the existing state-of-the-art techniques deployed by the client were typical univariate methods that generated demand forecasts considering only the historical sales data of different product categories.

However, in the current scenario, the client had access to large quantities of related time-series datasets, and analyzing demand based on current and historic information proved to be useful in generating accurate forecasts in the current setting. The sales and demand forecasting experts at Quantzig adopted a comprehensive three-phased approached that focused on conditioning the forecast of individual time series on past behavior of similar, related time series data sets.

Phase 1: Data Standardization and Incorporation

The initial phase of this demand forecasting engagement revolved around standardizing current and historical product demand datasets in order to incorporate them into a unified data model. This was achieved through the collection and integration of demand and sales data obtained from various internal and external sources as well as the online e-commerce platforms.

Phase 2: Product-Level Demand Forecasting Framework

The second phase of this engagement-focused solely on the interpretation of data sets obtained from various sources. To do so, we had to first devise and implement a product-level operational framework that could help solve the challenges faced by the client.

Phase 3: Unified Data Model Implementation

The final phase revolved around incorporating the cross-series data sets into a unified data model that was designed to correlate demand and sales patterns. This was achieved by training a Long Short-Term Memory network (LSTM) that exploits the non-linear demand relationships between various e-commerce product assortment hierarchies.


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Value Delivered

The proposed demand forecasting framework provided granular demand and sales insights that enabled the client to plan their inventories and manage the demand surge without affecting customer relationships. The demand forecasting framework was designed to leverage 40+ statistical techniques for enabling an automated forecast of product orders with machine learning-based selection of appropriate techniques based on the changes in data patterns. This enabled the eCommerce retail industry client to better plan their inventories to cater to the demand surge in times of such outlier events like the COVID-19 pandemic.

The demand forecasting solutions also empowered the e-commerce retailer to:

  • Forecast and communicate expected orders for relevant products including essentials, disposables, and personal hygiene products in advance
  • Accurately forecast the demand for each SKU based on the regions and zip codes
  • Deploy holistic demand forecasting modules to analyze and model scenarios and set sales goals by infusing market data and e-commerce trends
  • The devised demand forecasting framework helped the eCommerce retail industry client to increase average order value (AOV) by 52%

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