Moez Ali, AI Director, and Caleb Popow, AI Product Manager, Zebra Technologies, explore the impact AI Advanced Analytics and Anomaly Detection is having on existing organisational toolsets.
Exception based reporting (EBR) tools have long been the backbone of retail analytics, designed to identify outliers and potential issues by flagging deviations from expected patterns. While effective for pinpointing specific errors or fraudulent activities, EBR’s scope is limited. It focuses on anomalies after they occur, offering little in the way of understanding broader trends or predicting future events. This retrospective approach is no longer sufficient.
The inherent limitations of rule-based systems like EBR stem primarily from their reliance on the domain knowledge of retail workers. This knowledge, while invaluable, is susceptible to erosion through employee turnover. As experienced staff leave, they take the insights that power these systems, leaving a gap that is hard to fill. Moreover, while simple rules can be effective for straightforward scenarios, the complexity of retail operations today demands more nuanced approaches.
Complex rules, necessary for sophisticated analysis, quickly exceed the creation and maintenance capabilities of the human mind. This is not only a matter of intellectual capacity; it’s about the practicality of continuously updating these systems to keep pace with evolving retail landscapes. Over time, the cost of maintaining such rule-based systems grows, not linearly, but exponentially. In the long run, the financial and operational overhead required to keep these systems relevant and accurate can far outweigh the benefits they provide, making them an increasingly untenable solution for modern retail operations.
Common Challenges
One of the common retail challenges that EBR solutions try to solve is the scenario where a cashier refunds a transaction where they were the same cashier that ran the purchase transaction. EBR solutions take the two-step process and provide an output of hundreds or thousands of occurrences. In the age of “making the point-of-sale experience frictionless,” transaction refunds are now able to be processed by an associate rather than the classic service desk employee or floor manager. As a result, the number of instances where this activity takes place is increasing exponentially, causing more work for loss prevention and store operations teams to investigate. In a time when labour is hard to maintain, this EBR style approach to problem solving is unsustainable.
Now add in omnichannel initiative such as “purchase online fulfil in store,” “purchase in store fulfil online,” “try before you buy” and “buy through partner, return to store.” Adding a few scenarios where associates have new touchpoints to transactions would balloon the results output of an EBR solution and make it unmanageable with many false positives.
The solution is to realise that AI is the right tool for solving complex problems and realising that more data is the gasoline for AI to comprehend complex omnichannel challenges from multiple different viewpoints. AI does this with ease and can also take it a step further and articulate in sentence form why the anomaly was detected, thereby reducing time to action and understanding, but more importantly feeding decision-makers with intelligent real-time explanations regarding abnormalities in their business.
What is AI Anomaly Detection?
EBR’s linear, rule-based analysis struggles with the volume and variety of data typical in today’s retail sector. However, AI thrives on complexity, analysing diverse data types—sales, social media, weather patterns—to deliver nuanced insights that drive strategic decisions. This shift from EBR to AI in retail is an upgrade in technology and a fundamental change in how data informs business strategy, offering a more dynamic, predictive approach to retail analytics.
AI anomaly detection refers to the use of AI to automatically identify patterns within data that do not conform to expected behaviour. Unlike traditional methods, which rely on predefined rules and thresholds, AI anomaly detection utilises machine learning and statistical algorithms to learn from data over time, becoming increasingly adept at spotting irregularities. This approach enables the system to uncover a wide range of anomalies, from straightforward errors to complex patterns that hint at deeper insights or trends. By harnessing the power of AI, anomaly detection in retail and other sectors can adapt to new data dynamically, making it a powerful tool for real-time analysis and decision-making.
Anomaly detection is a critical component of retail analytics, tasked with identifying patterns that deviate from the norm. Where traditional systems might flag a sudden dip in sales or an unexpected spike in returns, AI-based anomaly detection digs deeper. For instance, AI can detect anomalies in purchase behaviour during unusual weather patterns or shifts in consumer preferences that are invisible to the naked eye. This capability is not just about catching fraud or errors; it’s about understanding the dynamic retail environment at a granular level.
The move from traditional EBR to AI-powered analytics brings substantial advantages to retailers. One of the most immediate benefits is the ability for real-time data analysis. Unlike EBR systems that operate with a lag, AI tools analyse data as it comes in, allowing for swift decision-making that can dramatically influence outcomes. An advanced deep learning network can be designed to take all data in an on-demand or automated manner from the point of sale, inventory, IoT, RFID, telemetry, etc, and train on that data in real time, ultimately producing results in real time.
Going beyond with AI Predictive Analytics
Predictive analytics take this one step further by forecasting future trends and demands, giving retailers a roadmap for inventory management, marketing strategies, and more. AI also enhances customer experiences through personalisation, analysing behaviour to tailor recommendations, promotions, and interactions to individual preferences.
Unlike EBR, AI-based analytics anticipate future trends. By leveraging machine learning algorithms, AI systems can sift through mountains of data, learning from it to identify patterns, predict consumer behaviour, and optimise inventory management. This forward-looking approach allows retailers to be more agile, adjusting their strategies in real time to meet consumer demands and stay ahead of the competition.
Consider the case of a retail business that shifted from EBR to AI analytics. Initially relying on EBR tools for fraud detection and inventory discrepancies, the retailer struggled with delayed responses and missed opportunities. After integrating AI, the business not only improved its efficiency in identifying and addressing anomalies but also gained insights into customer buying patterns. This transition allowed the retailer to adjust inventory in real time, predict future sales with greater accuracy, and personalise marketing efforts—results that far surpassed the capabilities of their previous EBR system. The outcome was a significant boost in both customer satisfaction and profitability, showcasing the transformative potential of AI in retail.
Conclusion
AI anomaly detection could ease the burden of needing to know the complexities of machine learning or having domain knowledge of the challenge to be solved, with an added reasoning engine working with a deep learning network. A user would no longer need to guess or research machine learning terms they do not understand. A reasoning engine could provide a written explanation for why the anomalies were identified using the language dialect of the user.
Find out more about AI-powered inventory software solutions here.
Image Credit: Zebra Technologies
