Artificial Intelligence (AI) has changed the face of business and it isn’t done yet. With its ability to provide real-time business intelligence in an easily understandable and adaptable fashion, this technology has unlocked new ways to exploit the massive data sets generated across the industry.
Daily business activities and performance are now being deciphered and yielding valuable conclusions not just about financial performance, but also about employees and customers. Companies can now move easily from a big picture perspective all the way down to a single data point analysis and along the way are discovering actionable insights in areas such as loss prevention, employee scoring, customer intelligence, and location performance scoring.
For the quick service restaurant industry, with annual sales totaling $799 billion annually, AI solutions don’t just bring a potential for new financial success, they are generating insights that offer concrete competitive advantages.
Where does AI lead us?
A restaurant sector driven by the insights generated through machine learning algorithms is an exciting proposition, but what does it mean? Basically, it means taking advantage of a real-time data platform that ingests data provided via a PoS terminal or other employee and customer interfaces and then utilizes models to create a deep understanding of business performance.
This process consists of billions of data points that are processed in seconds and which, once ingested, enable the identification and categorization of common patterns and trends. These numerous metrics are then combined into easy to read scores and indicators and sent to a customized dashboard for interpretation and action by management and auditors.
For the restaurant industry, there are several areas in which developing AI solutions have yielded exciting new innovations.
Loss Prevention / Fraud Detection.
Internal fraud by employees can cause significant damage to a restaurant’s bottom line. According to industry magazine QSR, as much of 75% of quick service restaurant loss is due to employee fraud, e.g. refund abuse, floating transactions, and gift card fraud, etc. However, external fraud by customers in the form of charge backs and other deliberate acts is also not uncommon.
With dedicated algorithms for all types of theft, AI models can be trained to look for fraudulent patterns and anomalies in transactions. With its ability to drill down to minute particulars such as voids, deletes and discounts and then combine them by time of day, employee role and product type sold, the algorithm can identify fraudulent activities in specific locations and by specific employees. These suspicions can then be verified through video surveillance or other 3rd party assistance.
Employee Scoring.
As the models ingest input from countless PoS terminals spread across a restaurant network, they can also be trained to examine the performance of the employee who is generating each piece of data. This enables the algorithm to analyze mistakes, track transaction time, monitor cross-selling, etc. and generally gauge employee performance.
While models for scoring employees are not solely AI driven, AI plays a major role in how we compare different employees. Ordinarily, such employee comparisons can be difficult - employee transactions can vary not only by location, but by the time of day, day of the week, and even POS terminal used. However, AI clustering can help us group employee transaction together, allowing us to compare employees on an even footing. This not only helps discern those employees who are underperforming, it also highlights those who are performing above standard.
Location Performance.
Using data-driven scoring to analyze location performance can reveal the best and worst locations in a restaurant network as well as other possible specialized features such as compliance and queue time. Much of this modeling is predictive - based on what items are sold, how many, location, etc: what should a location’s KPIs look like?
Location performance can also be presented in drill down reports that will analyze store traffic according to time of day, allowing for targeted promotions. With these types of models, KPIs can also be formulated easily and on the fly according to management needs.
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Customer Intelligence.
Building on the information gathered through PoS terminals, a restaurant can develop a greater understanding of their customers. When this is combined with location specific scoring, it can yield a highly specific analysis of buying behavior and inventory needs.
AI has the capability to understand and categorize items through NLP algorithms in order to identify trends and problems beyond the individual item level.. With the help of 3rd party solutions to process this data, a restaurant can create customer interactions that are more personal.
While adoption of these technologies is slow, restaurant owners are starting to see the potential of AI in changing the way they do business. But it is certainly clear that with the assistance of AI, the industry can gain the tools needed to cut fraud loss and build profit margins through data-driven insights.
Deep BI is a comprehensive data solutions company that offers powerful flexible products for data analysts, product teams and management through a platform incorporating data analytics, a Business Intelligence layer, and a data warehousing platform with a key focus on real-time scoring & data augmentation. Using our Machine Learning and Data Science capabilities we're able to serve customers in the retail, fraud detection and financial market verticals as well as the media and marketing sectors.
