Anomaly detection is about identifying outliers in a time series data using mathematical models, correlating it various influencing factors and delivering insights to business decision makers. Using anomaly detection across multiple variables and automatically correlating it among them has significant potential to increase sales and margins for a typical ecommerce business. Advances in artificial intelligence, machine learning, and deep learning algorithms has 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.
1. Effortless: An ecommerce 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 the competing on this data is the key to success of a retailer. Retailers who were the first to use the advances in data analytics always beat their competition. Anomaly detection is one of such advances that has the potential to deliver significant benefits to the retailer with the use of data that is already existing 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. Bigdata that is already available with the ecommerce business can be effortlessly fed into the automated anomaly detection system to identify the sources of significant business benefits.
2. 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 ecommerce businesses. Availability of off the shelf software / cloud-based platform that could be purchased and implemented in the tech stack of an ecommerce retailer according to their customised requirements can now be done almost instantly. The advancements in microservices and docker based services has enabled easier implementation. Today, the entire anomaly detection product can sit in a docker, which can easily be placed within the technology stack of an ecommerce retailer, who can then connect it to the existing systems and generate actionable insights as soon as they are implemented.
3. 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, latest advances in machine learning and deep learning algorithms has 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) has significantly decreased the cost of anomaly detection and made it affordable even for small businesses.
4. Better: A significant amount of managerial time is spent in firefighting because of wide variety of reasons. After any issue becomes big it takes enormous amount of organisational time and effort to get the business back on track. AI powered automated anomaly detection can enable the decision makers to manage by exception. With AI powered anomaly detection, the decision makers can spend their time focusing on the anomaly and extinguishing the fire before it spreads. This will save significant energy, which can be utilised to build the business and improve profitability. For example, using real time AI for detecting the anomaly and using it to gauge the interest for the product with the early adopters in a market segment and amplifying it with the use of marketing efforts focused on that segment would lead to significant increases in revenues and market share. Real time AI and anomaly detection techniques will help the retailers achieve the potential incremental value over other analytics techniques.
5. Faster: Ecommerce retailers are in a need to respond much faster to market changes than any time 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. With this state of art technology, data is robotically fed and read by the AI engine that powers the 24 x 7 real-time anomaly detection. As soon as anomalies occur, the system detects it, automatically correlates to figure out whether there is a business incident and alerts the respective business owner in a matter of minutes through their existing communication medium: slack, text messages and so. on. This empowers the ecommerce retailer to respond effectively in the fastest possible pace.
Anomaly detection is going to become the way of life for business managers. Already majority of ecommerce brands are testing various AI tools in some form or the other. Ecommerce retailers who skip AI are expected perish soon. When you adopt a state of art anomaly detection engine powered by artificial intelligence, it has the potential to predict new trends, identify latent waste in the form of stock, help spot pricing errors at a micro level and stop revenue leakages. In the suite of AI tools, anomaly detection has the potential to add significant business value. Bigdata has made it effortless, integrations with existing deliver 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 ecommerce businesses to respond faster than ever before.
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This article was originally published on ITProPortal
Kumar is a principal consultant at CrunchMetrics. He is an alumnus of IIT- Madras and IIM- Calcutta. As an entrepreneur, he has co-founded an analytics company and then an omni channel retail company. He has worked in advisory roles for Fortune 500 companies such as Deloitte and Tesco in various multinational locations. He has also worked in technology roles for MNCs such as Cognizant and Virtusa. He is a Good Reads author with the pen name Khun S. Kumar and has published seven novellas in Amazon.