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Data Anomaly Detection- Case Study


A Large Comprehensive E-commerce solutions & Competitive intelligence provider

Our customer is a Global E-commerce solutions provider who offers MAP Violations Monitoring, Buy Box Intelligence and Competitive Pricing Monitoring to their end customers such as product owners and sellers with registered brands across multiple online marketplaces. Our client approached Mobius to enhance their platform capabilities by collecting related attributes to increase revenue, reduce cost, and check the quality of E-Commerce product listings. As an overall solution, they wanted to enhance their price monitoring solution by incorporating advanced analytical models to detect price anomalies and delivering proactive outcomes. 

The Business Need

A custom solution developed for our business owner can detect anomalies or extreme outliers or polar values that tend to deviate from an ideal data distribution. The primary objective of the requirement was to monitor the product price ranges with respect to multiple SKUs and retail marketplaces. Also, they required the visibility to validate the normal values against the anomalies and appropriately label them for an insightful analysis.  Our solution helped the client to automate the quality management process, effective tracking of the product prices and retail SKUs in real time.

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E-commerce companies similar to our customer deal with vast amounts of data, and manually monitoring prices for anomalies can be time-consuming and error-prone. A few prominent challenges include Pricing errors, inconsistencies/insufficiency in the data, whether it’s due to human error, technical glitches, or other issues, anomalies in price data can cause potential revenue loss or customer dissatisfaction. 


Mobius has developed a customized model that proactively identifies the anomalies, reduces the manual efforts and avoids potential errors with the application of machine learning techniques and provided the respective solution using the below steps 

  1. Exploratory analysis & Input data ingestion 
  2. Data pre-processing & Anomaly detection model creation
  3. Testing and validation of predicted outputs
  4. Feedback-based monitoring & Accuracy check 


Generally, brands can monitor the pricing listed by sellers on the platforms to ensure that the minimum levels are not breached. However, in case of rare occurrences of price variations/fluctuations due to market demand might lead to outlier values that need to be monitored on a real-time basis. With our in-house anomaly detection process, these extremities are flagged and reported periodically to the customer for a sustained flow of price monitoring.  

Competitive pricing analysis: Anomaly detection allows e-commerce companies to monitor the prices of their SKUs in real time.

Price optimization: By detecting unusual pricing patterns and outliers, anomaly detection can provide valuable insights into potential opportunities for price optimization. 

Fraud detection: Anomalies in price data may indicate fraudulent activities, such as price manipulation or unauthorized changes to product prices. 

Improved customer experience: Accurate and consistent pricing enhances customer trust and satisfaction. 

Demand and supply forecasting: Unusual price fluctuations can be indicative of changes in demand or supply for certain products. 

Data-driven decision-making: By leveraging anomaly detection insights, e-commerce companies can make data-driven decisions on pricing, inventory management, and marketing strategies, leading to better overall performance and competitiveness.

Early warning system: Anomaly detection serves as an early warning system, alerting the company to potential issues in price data before they escalate into larger problems. 

For a detailed presentation of specific use cases, please write back to us at support@techmobius.com