New Restaurant Management and POS Systems Drive Restaurant Performance Improvement
The 2017 Smart Decision Guide to Restaurant Management and POS Systems, which published earlier this month by Starfleet Research, the leader in best practices market research for the hospitality sector, is now available for complimentary access.
According to the research, restaurant performance improvement is fueled by advanced ordering, payment processing, inventory control and labor management features along with sophisticated sales and marketing, guest relationship management and loyalty management capabilities.
Restaurant performance improvement also depends to a large extent on technology interoperability and data integration. The POS system needs to seamlessly connect with online ordering systems, guest-facing apps, kiosks, display hardware and any number of third-party modules and solutions, such as those related to inventory management and workforce management.
In a hotel or resort location, the POS may need to integrate with the hotel property management system (PMS). Large, complex restaurants and chains often need to integrate applications across multiple locations. Increasingly, the POS system also needs to integrate with third-party CRM, marketing and guest loyalty and rewards management solutions, allowing restaurant operators to gain a deeper understanding of guest preferences and deliver more personalized messages and offers as well as tailored services, such as seating and beverage options based on preferences.
The ability to customize communications based on purchases, frequency, dayparts, preferences, etc., depends on the ability to connect, interface and seamlessly share data, preferably in the cloud. According to the research, 91 percent of restaurant operators “agree” or “strongly agree” that technology platform interoperability and compatibility is a key success factor for restaurant performance improvement.
The more robust data analysis and reporting capabilities that many solutions now offer are also key factors in performance improvement. Smart restaurant operators are collecting, integrating and analyzing data from multiple sources, including POS transactions, loyalty programs and guest-facing apps, to better understand their guests at both an individual and aggregate level, to interact with them more effectively and to identify opportunities for improvement in business operations.
Which dishes are the most popular? Which dishes are the least popular? Which are the most profitable? Which are the least profitable? Which servers are the most productive? Which are the least productive? Which types of guests are the most valuable? Which are the least valuable?
In the past, mining data to extract meaningful insights tended to be an onerous and time-consuming proposition. Many next-generation POS systems, by contrast, have built-in capabilities that make it possible to readily generate all types of actionable insights, oftentimes on an ad hoc basis. This gives restaurant operators the ability to make more informed decisions about all aspects of the business.
Some systems can even open a window into the future and predict financial outcomes based on such factors as dining-out trends and seasonal dish popularity. In some cases, alerts can be set based on predefined triggers. These alerts can notify a manager when inventory is running low and needs to be restocked, for example, or when high-cost or high-risk food items like liquor are going out the door unaccounted for, indicating the possibility of theft.
Published by Starfleet Research, the leader in best practices market research for the hospitality sector, with underwriting support from Agilysys, Lavu, Linga POS by Benseron and TouchBistro, the updated and expanded edition of this popular Smart Decision Guide (click here to access) offers a roadmap to success for new and existing restaurants. It includes insights for evaluating different solutions and practical guidance for making the right selection as well as pointers for maximizing the value of the investment over time.
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