Artificial Intelligence is Eating Exception-Based Reporting (EBR) Tools
And it’s changing the future of advanced analytics and anomaly detection in retail. Here’s how.
In the rapidly evolving retail sector, we’ve both seen the approach to data analytics undergo a significant transformation in recent times due to advancements in technology. Traditionally, you (and every other retailer) probably relied on exception-based reporting (EBR) systems to flag discrepancies and outliers in data – essentially focusing on the exceptions to the norm. We know this method served as a critical tool for identifying fraud, operational errors, inventory shrinkages, and other operational areas requiring attention. However, the retail landscape's complexity and the sheer volume of data generated have outpaced the capabilities of EBR systems.
That’s why we’re seeing retailers increasingly turn to AI-driven analytics to sift through vast amounts of data in real time. AI can offer predictive insights in addition to reactive ones, and it can go beyond spotting anomalies. A well-trained AI can analyze trends, predict future patterns, and provide actionable intelligence that helps you and your team make more strategically sound decisions. This shift marks a fundamental change in how retailers like you can approach data analytics. Finally, you have the tools to move from a reactive stance to a proactive and predictive operating model.
That’s why we fully expect AI to replace traditional data analytics tools in the retail industry. AI's ability to analyze and learn from data can lead to more efficient operations, enhanced customer experiences, and ultimately, a more competitive position in the market. But those aren’t the only reasons why EBR systems are bound for early retirement.
What Happens If You Don’t Shift from EBR to AI?
We know EBR tools have long been the backbone of retail analytics, designed to identify outliers and potential issues by flagging deviations from expected patterns. However, while they’re effective for pinpointing specific errors or fraudulent activities, the scope of any EBR system is limited. It focuses on anomalies after they occur, offering little in the way of understanding broader trends or predicting future events.
The inherent limitations of rule-based systems like EBR tools 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 with them 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 just 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.
Then again, you probably already know this from personal experience. So, let’s talk about how AI is changing the game.
Unlike EBR systems, AI-based analytics don't just react to past events; they anticipate future trends. By leveraging machine learning algorithms, these AI systems can sift through mountains of data, learning from it to identify patterns, predict consumer behavior, and help your team optimize inventory. In turn, you’ll find it easier to be agile and capable of adjusting various labor, stocking, pricing, and merchandising strategies in real time to meet consumer demands and stay ahead of the competition.
When comparing EBR and AI systems, the contrast in their ability to handle data complexity is stark. An EBR system's linear, rule-based analysis struggles with the volume and variety of data typical in today's retail sector. An AI system, on the other hand, thrives on complexity. It can analyze diverse data types – sales, social media, weather patterns – to deliver nuanced insights that drive strategic decisions. This shift from EBR to AI system use in retail operations is not just an upgrade in technology; it's a fundamental change in how data will inform your business strategy, offering a more dynamic and predictive analysis of your retail operation and the outside factors that commonly influence operational decisions.
One of the common retail challenges that EBR systems try to solve is the scenario in which the same cashier processes both the purchase and refund transactions. In some cases, this is indicative of fraud. Many times, it’s not. But it’s hard to know with EBR systems as they take a two-step process to figure out how many times this has occurred before providing an output of hundreds or thousands of occurrences. Here’s the real problem: In the age of “making the point-of-sale experience frictionless,” transaction refunds are now able to be processed by the average associate rather than the classic service desk employee or floor manager. As a result, the number of instances in which the same associate handled both the sale and return is increasing exponentially, causing more work for loss prevention and store operations teams to investigate whether any of those were criminal acts or just coincidence. In a time when labor is hard to maintain, this EBR-style approach to problem solving is unsustainable.
Take the same example mentioned above. Now add in omnichannel transaction options such as “buy online and pick up in store,” “purchase in store and ship to your home,” “try before you buy,” and “buy through a partner but return to store.”
We have now added just a few scenarios where associates have new touchpoints to transactions that would balloon the results output of an EBR system and make it unmanageable with many false positives.
Without the expansive analysis capabilities of an AI model that can be trained to consider these situations, you’re going to find it impossible to get the answers you want.
So, we always tell retailers now that if you really want to distinguish criminal acts from coincidence or figure out what’s happening in your operation and why, you must treat AI as a mandatory analysis tool. This isn’t a luxury investment anymore. It’s as necessary as that barcode scanner is at the point of sale.
It’s also important to know that data is the gasoline for AI systems. The more data you can feed to your AI analytics system, the more able it will be to comprehend complex omnichannel challenges from multiple different viewpoints such as time, relationships, and macro vs. micro data comparisons – and well beyond the average human comprehension.
Better yet, the AI systems trained to analyze retail operations can articulate in sentence form why each anomaly was detected. This reduces your time to action and understanding and, more importantly, feeds all decision- makers with intelligent real-time explanations about abnormalities so that there’s no delay in action.
How Does AI Anomaly Detection Work, Exactly?
AI systems can be trained to automatically identify patterns within data that do not conform to expected behavior. Unlike traditional EBR anomaly detection methods, which rely on predefined rules and thresholds, AI anomaly detection utilizes 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. So, by harnessing the power of AI, your anomaly detection system can adapt to new data dynamically, making it a powerful tool for real-time analysis and decision-making.
Where traditional analytics systems might flag a sudden dip in sales or an unexpected spike in returns, an AI-based anomaly detection system digs deeper. It leverages complex, retail-tailored algorithms to sift through massive datasets, spotting not just clear outliers but also subtle trends that could indicate emerging issues or opportunities. For instance, AI can detect anomalies in shoppers’ purchase behavior 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 predictive analytics capabilities of AI systems take this one step further by forecasting future trends and demands, giving you a roadmap for inventory management, marketing strategies, and more. AI can also be used to enhance customer experiences through personalization, analyzing shopper behaviors to tailor recommendations, promotions, and interactions based on their individual (and AI-identified) preferences.
For example, one retailer that shifted from EBR to AI analytics was initially relying on EBR tools for fraud detection and inventory discrepancies. However, its team struggled with delayed system responses and missed opportunities. After integrating AI, the retailer’s leaders reported efficiency gains in identifying and addressing anomalies and in understanding customer buying patterns. This transition to AI-driven analysis ultimately allowed the retailer to start adjusting inventory in real time, predict future sales with greater accuracy, and personalize marketing efforts—results that far surpassed the capabilities of its previous EBR system. The outcome was a significant boost in both customer satisfaction and profitability, showcasing the transformative potential of AI in retail.
Implementing AI in Retail: Steps and Considerations
Adopting AI-based analytics in your retail environment is not a plug-and-play solution; it requires thoughtful preparation and strategic planning. You must ensure the necessary infrastructure is in place to support data-intensive AI applications, safeguard customer data privacy, and provide staff with the training needed to leverage the new technologies effectively.
Therefore, integrating AI tools into your existing retail systems begins with a clear assessment of current capabilities and needs, followed by the selection of AI systems that align with your specific business improvement goals. The implementation process will then involve data integration, where existing data sources are merged with AI analytical tools, and system compatibility checks to ensure smooth operation. You must also prepare for ongoing maintenance and updates to AI systems, a task that involves both financial investment and a commitment to continuous learning.
The challenge of implementing AI? Ensuring that you’re feeding your system quality data. “Garbage in, garbage out” remains a truism in the age of AI. So, be prepared to cleanse and standardize any data you want to feed into your AI systems.
Additionally, navigating system compatibility issues requires a technical audit of existing infrastructure and possibly significant upgrades or changes. The cost, both in terms of financial outlay and time, can be substantial, but the long-term benefits of AI integration –increased efficiency, improved customer experiences, and enhanced decision-making capabilities –often justify the initial investment.
The good news is that there are AI anomaly detection systems already designed to solve these challenges. We know because we’ve been personally working with retailers to implement them for a few years already.
For example, the anomaly detection capabilities of the Zebra Workcloud Actionable Intelligence system are optimized using a deep learning neural network that is designed to take all your data from your POS, inventory, IOT, RFID, telemetry and other sensors and systems in an on-demand or automated manner, train on that data in real time, and produce results in real time.
And since you’re going to ask…no, you don’t need to be a data scientist (or have one on standby) to interpret the results, at least not with the way we designed the AI analytics system for retailers. We added a reasoning engine to our system’s deep learning network so users don’t have to guess what the results mean or research machine learning terms they do not understand. Our reasoning engine provides a written explanation for why the anomalies were identified using the language dialect of the user! This is why we call it the “Actionable Intelligence” system. It allows whoever receives those results to do something about it immediately.
The Main Takeaway?
The complexity of challenges that you have faced over the last few years has grown to an unthinkable level, and everything that was once a simple two-step problem that could be solved by an analyst and an EBR system has become something that far surpasses human comprehension and the limits of EBR’s analysis capabilities. AI must be used to fill the gap and, fortunately, with the recent generational jump in computational horsepower and machine learning capabilities, neural networks can now solve these types of problems at a scale. That was unthinkable just a year ago!
So, if you want an easier way to see what’s happening in your operation and a commonsense explanation for detected anomalies, or you want to better prepare for outside factors that could require some changeups in your operating strategy, reach out to us. We’d be happy to show you how an AI system like the one we described works for other retailers and talk about how you could make it work better for you than your existing EBR system.
###
Curious how else AI can help you improve the performance of your retail operations? These insights might help...
