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5 Reasons Why Anomaly Detection Is Important For Your E-Commerce Business?

Advances in artificial intelligence, machine learning, and deep learning algorithms have enabled automated anomaly detection to become a reality

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Anomaly detection is about identifying outliers in a time series data and using that information for increasing sales and margins. Advances in artificial intelligence, machine learning, and deep learning algorithms have enabled automated anomaly detection to become a reality. A dotcom retailer must adopt anomaly detection as a way of life because it is effortless, easier, cheaper, better and faster.

Effortless: An e-commerce business generates large quantities of data because of all the information they collect during their daily operations. These data are being used to make better decisions and competing on this data is the key to the success of a retailer. Retailers who were first to use the advances in data analytics have an opportunity to stay ahead of their competition. Anomaly detection is one of such advances that has the potential to deliver significant benefits to the retailer through the use of data that already exists in the database. While anomaly detection aims to detect the needle in the haystack, the entire process is effortless and does not require any additional work pertaining to data collection. Big data that is already available with the e-commerce business can be effortlessly fed into the automated anomaly detection system to identify the sources of significant business benefits.

Easier: Advancements in cloud computing and hybrid delivery models for analytics-as-a-service and emergence of platforms that provide AI analytics has eased the implementation of anomaly detection for e-commerce businesses. Availability of off-the-shelf software / cloud-based platforms that could be purchased and implemented in the tech stack of an e-commerce retailer according to their customized requirements can now be done almost instantly. They can also connect to the existing alerting mechanisms and deliver insights as soon as they are implemented.   

Cheaper: In the early days of building models in business analytics, outliers were identified and removed while building models. Looking at those outliers and detecting the reason behind those anomalies was seemingly impossible. However, the latest advances in machine learning and deep learning algorithms have made it possible to spot these anomalies and automatically integrate with the existing alert mechanisms and generate insights without any difficulty. Advancements in technologies (both hardware and software) have significantly decreased the cost of anomaly detection and made it affordable even for small businesses.

Better: A significant amount of managerial time is spent in firefighting because of a wide variety of reasons. After any issue becomes big it takes an enormous amount of organizational time and effort to get the business back on track. Automated anomaly detection can enable the decision makers to manage by exception. Most of the normal course of businesses can be automated. And when an anomaly is detected the decision maker can spend time towards focusing on the anomaly and extinguishing the fire before it spreads. This will save significant energy, which can be utilized to build the business and improve profitability. Using anomaly detection helps in better decision making.  

Faster: E-commerce retailers are in a need to respond much faster to market changes than in the past. Having information in real-time or near real-time is the need of the hour. However, crunching data with that speed and having metrics in real-time at a granular level always remains in the wish list of the analytics teams. Automated anomaly detection has brought real-time analytics to life. This empowers the e-commerce retailer to respond effectively.  

Anomaly detection is going to become a way of life for business managers. Already the majority of e-commerce brands are testing various AI tools in some form or the other. E-commerce retailers who skip AI are expected perish soon. Within the suite of AI tools, anomaly detection has the potential to add significant business value. Big data has made it effortless, integration with existing delivery mechanisms and advancements in various delivery models has made it easier to adopt, advances in machine learning and deep learning has made it cheaper, and it is better for decision-makers to manage by exceptions and it empowers e-commerce businesses to respond faster than ever before. When you adopt a state of art anomaly detection engine, you can seal revenue leakages, improve margins and increase sales.













Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.


Tags assigned to this article:
e commerce artificial intelligence machine learning

Rohit Maheshwari

The author is Head of Products and Strategy, Subex.

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