business-intelligence | 11 mins read

5 Ways a Restaurant Can Profit From Business Intelligence and Analytics

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Debdutta Bhattacharjee

By Debdutta Bhattacharjee

What's the Difference Between Business Intelligence and Analytics?

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Business intelligence Vs. Business Analytics
Business analytics and business intelligence together aid decision making so that an organization can identify problem areas and address them. However, there is a difference between the two concepts, and a business intelligence vs business analytics discussion is in order.

Business intelligence concentrates on descriptive analytics and looks to create a summary or a report of past and present data. It seeks to ask 'what', so that processes and practices that are successful can be replicated, and those that are not can be dropped. Business analytics, on the other hand, deal with predictive analytics. It uses data mining, modeling, and machine learning to forecast future outcomes.

Business analytics goes beyond 'what' and asks 'why', 'how' and 'what next?' The answers to these questions help a business make sense of the data provided by periodic reports, which would have remained inert without the application of business analytics tools. Big data, in this regard, can give an idea about future trends as well.

The objective of business analytics and data analytics is to make data gathered through point of sale (POS) systems, business management software solutions, and employee management tools comprehensible. They should also address the specific concerns of a business. This makes the role of a data scientist extremely important.

What is Predictive Analytics?

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Predictive analytics studies past and present data trends to determine whether those trends can recur. It allows businesses to adjust their strategies and resources in view of future possibilities, make more informed decisions, increase operational efficiencies, minimize risks, and do well in a competitive market.

Predictive analytics draws on machine learning, artificial intelligence, modeling, and statistics to make forecasts. Such forecasts help in better supply chain management and allow a business to make optimal use of its resources.

Here's an example of predictive analytics in action. Reports generated by a restaurant's POS system say that the sale of its signature chicken dish has spiked over the past three weeks. Business analytics, which uses predictive analytics, seeks to know why this has happened. A deep dive into the reports and data mining reveals that the traffic was fueled mostly as a result of a noted food blogger favorably reviewing the dish on social media.

Equipped with this valuable insight, the restaurant can now devise strategies to promote the chicken dish to the other food bloggers in the country. Based on the demand predicted, the restaurant can also quickly count its inventory levels to determine how much chicken and other ingredients it needs to order to match the possible surge in demand.

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How Can Restaurants Gather Business Intelligence Data?

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The most common way for restaurants to collect data is through POS systems. These platforms generate reports on digital and in-store sales, sales per worker per month, sales per location, sales per channel, sales per time slot, sales from specific timeframes, the average number of orders, employee activities and performance, and so on.

Business intelligence tools or BI tools enable the collection and processing of substantial amounts of unstructured data from both, internal and external sources. A BI tool also allows an enterprise to produce data for analysis so that reports, data visualization, and dashboards can be created.

If the software that the POS system uses is cloud-based, the POS Cloud Data can be viewed and analyzed in real time from anywhere in the world, and on any device. Cloud-based software updates itself automatically across locations and is ideal for multi-store businesses.

Restaurants may also be able to gather information on their customers by having them sign up for promotions through email, phone messaging, or social media. Insights may also be provided by third-party vendors like GrubHub and OpenTable.

Feedback and survey forms may have columns for diners to fill in their personal data. Some foodservice businesses may also tie their signature offers to conditions that require diners to furnish personal data. For example, as of April 2018, Starbucks customers had to submit their full names, email addresses, and zip codes before they were allowed to access free wi-fi services.

A POS system can provide data on employee management too, like hours worked, best and worst performers, peak hours in the restaurant, and so on. It may also multi-task as an employee time clock.

Business intelligence data thus accumulated lets a restaurant make more profit-focused decisions, manage the Supply Chain better, optimize the use of labor and other resources with an eye on cutting costs, minimizing food waste, and creating personalized loyalty and incentive schemes to retain loyal customers and build repeat orders from one-off customers.

Restaurants, whether independent fine-dining establishments or Quick Service chains, have been collecting customer information in a variety of ways. In today's competitive world, it is no longer enough for a business to track demographic data but to look at personalized and specific datafrom the flavor of coffee a customer prefers, to the time of the day the customer usually visits the restaurant.

  • According to a report by Research and Markets, the global market for mobile business intelligence is expected to grow from $8.8 billion in 2020 to $29.6 billion in 2027.
  • This translates into a compound annual growth rate (CAGR) of 18.9%.

5 Ways a Restaurant Can Profit From Business Intelligence

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1. Inventory management- POS systems update stock levels as ingredients are utilized and menu items sold. Therefore, an analysis of sales data will tell restaurants which ingredients are in short supply and need to be reordered, and which ones need not be purchased immediately. With this insight, the restaurant owner can manage the supply chain better. Moreover, a detailed analysis of the variation between the expected and actual utilization of resources lets a restaurant calibrate its strategies, and make the best use of its resources.

2. Identify popular items/profitable units- Some menu items may sell like hotcakes! Others may sit on the shelves without many takers. By analyzing POS data, customer feedback, and reviews a restaurant is able to identify popular and profitable items.

Restaurants operating across multiple locations may also acquire data on which units are doing better than others. This information can then be analyzed to see why some stores are doing well, while others are not. This would enable the restaurant owner to make more confident decisions on pricing, strategic promotion, and retaining, expanding, or discontinuing a menu item or a unit.

3. Reduce labor costs- Most of a restaurant's expenditure is on its employees. Therefore, reducing labor cost is one of its key objectives. Sales data let restaurants identify their busiest and slowest periods of traffic. Employees can be scheduled/cross-trained accordingly.

4. Create loyalty programs- Customer data gathered by restaurants can help them shape attractive loyalty and reward programs. With the help of data science, restaurants are able to understand whether casual guests can be turned into loyal customers, or whether they will always remain sporadic guests. It makes marketing expenditure more focused and can help boost the restaurant's bottom line. A Small Business can take advantage of a digital Marketing Platform and gain a high return on investment this way.

5. Reveal blind spots- Business intelligence and analytics allow restaurants to shed light on issues that could not have been easily pinpointed otherwise. For example, a supplier may be charging a restaurant a lot, and the same quality of the product may be acquired at a lesser price from another vendor.

What is Machine Learning and How Can it Benefit a Restaurant?

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Machine learning refers to the ability of algorithms to learn from the processing of data. Algorithms comprise a set of rules followed by a computer for problem-solving. The more data processing done by the computer system, the more algorithms learn, and the more accurate their responses.

Machine learning can help in the production of accurate sales forecasts. Basic algorithms can predict future sales, for example, using simple yardsticks like sales for the previous year/week, holidays, weather, and so on.

However, the flip side is that not all these parameters have the same impact on all restaurants. For example, an ice cream parlor next to the beach will be affected by the weather; a pizza joint in a mall not so much. While processing more and more data, algorithms can learn the factors that have a greater effect on sales in specific locations.

Machine learning can also enable robots to cook. For instance, 'Flippy', the flagship AI-driven kitchen assistant produced by the California-based Miso Robotics brings together thermal, 3D, and regular vision to help in frying, grilling, prepping, and plating.

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Top 5 Restaurant POS Systems for Data Management

1. Plum POS-
This POS software solution by Hubworks allows restaurant owners to take critical business decisions with the help of real time and in-depth reporting, wherever they are. This cloud-based solution is one of the best in the market and is ideal for kiosks, food trucks, medium-sized restaurants, ghost kitchens, and can also be custom configured to suit large restaurants. Plum POS can link with any device or app through the integration software Any Connector. Plum POS is able to integrate seamlessly with other restaurant management solutions like Plum Digital Signage, Plum Kiosk, and Plum Handheld and do away with multiple systems and logins.

2. Upserve-
The reporting module called Upserve HQ offer a number of templatized and customized reports. Apart from the sales and cost reports, Upserve provides standout reports such as server analysis and reputation management. Server analysis highlights employee productivity by sales category, thereby, alerting management to training opportunities. Reputation management lets the restaurant see online reviews from a central dashboard and quickly respond to negative feedback. This reporting module can also show how the restaurant stacks up vis-a-vis its competitors.

3. Clover-
This POS system offers powerful analytics tools to gain real time insights into sales trends and other data sets. Restaurant owners can track their businesses from anywhere using the dashboard or the Clover Go Mobile app. It informs them about their busiest hours, top-selling items, sales across locations, credit card transaction volume per card type, and other granular details so that the performance of the business can be viewed at a glance. Clover's built-in reports also make tax filing easier.

4. Revel Systems-
This POS system is built for high-volume and multi-location restaurants. Revel's reporting features let a restaurant owner drill down the most granular details of the company's performance. Reports on the cost of goods sold and labor can make businesses better prepared for future operations, while product mix and hourly sales reports can help in identifying the top-selling items. Inventory Management reports can help minimize wastage, and match costs with profits. Managers can set alerts for employee clock-ins, and unapproved overtime, which allows them to take better staff scheduling decisions.

5. Toast-
This is a hybrid POS system, which operates through a local network backed up by the cloud. Toast provides real time sales, menu, and labor data, and business metrics are sent to the user's inbox every night. All insights are condensed in one report, and no downloading or extra analyses are required. Toast lets users compare sales across several locations or time periods in a single report, and business performance on specific days can be seen to measure the impact of promotions or holidays. Product mix reports help in tracking sales of menu items.

Data Management FAQs

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1. What a centralized data management strategy, and how can it be achieved?

A centralized data management strategy describes the people, technology, and processes involved in the management of the organization's data. To achieve this, an individual may be designated to manage the strategy, and a team of stakeholders may supervise day-to-day data practices. The processes for collecting, storing, and using data should be defined, and the tools needed to implement the strategy must be determined.

2. How can data roles be defined and assigned?

Everyone should look to maintain data quality across the organization. When specific roles are assigned, it supports the enforcement of data policies. Important roles are those of data stewards, owners, and analysts, chief data officers, data protection officers, and others.

3. How can data governance be established?

A data governance strategy looks to maintain data quality throughout its lifecycle. Proper processes should be created to control the data. It should be ensured that users across the enterprise are well-versed with governance procedures, and a panel should supervise the implementation of those procedures.

4. How can data management be done with limited resources?

This can seem like an onerous task. To start with, one must highlight the benefits of data management for the company. One must then gain buy-in from the senior leadership. The most pressing issues, on which the limited resources can have the biggest impact, must be prioritized. Lastly, value must be demonstrated to make a case for investment in the data strategy.

5. How can data governance benefit a business?

Data governance allows organizations to avoid liabilities, save time and monetary resources on bad data, enhance customer relationships, and actively produce revenue.

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