The importance of data analytics in QSR
How data analytics can help improve your QSR business
What is data analytics and how can it help QSR businesses?
The quick service restaurant (QSR) industry is a data-rich industry. Every day, QSRs collect terabytes of data from a variety of sources, such as sales transactions, customer feedback, and social media activity.
Data analytics is the process of collecting, organising, and analysing data to extract meaningful insights. Data analytics is becoming increasingly important for QSR businesses, as industry technology advances.
This article will discuss how data analytics can be used to improve your business in a number of ways, including increasing sales, reducing costs, improving customer satisfaction and making more informed decisions.
How data analytics can be used to increase sales
Data analytics can provide valuable insights into customer behaviour, assisting QSR businesses in personalising customer experiences, tailoring product offerings and optimising pricing. This could be in the form of targeting the most profitable customers with personalised offers, tracking customer data on websites and apps to see what products are the most popular, or seeing what day of the week is the busiest and adjusting marketing campaigns and staff rosters accordingly.
Tracking inventory levels and predicting demand can also help ensure that QSRs have the right products available to meet consumer demand. Tracking customer feedback and identifying areas where customer service can be improved can assist in making changes to a QSR’s policies and procedures to improve customer satisfaction.
Summit Panorama Use Case
Recently one of our clients suspected his stores were losing drive-thru revenue from closing too early and/or customers coming through after trading hours. He used Summit Panorama to find that the stores had increased activity in the drive thru after the store closed, leading them to increase sales by extending their stores’ business hours.
How data analytics can be used to reduce costs
Data analytics can be used to reduce a variety of costs for QSRs. Storage, spoilage, and waste costs of inventory can be minimised, as tracking inventory can help avoid overstocking or understocking products. Tracking product prices as well as competitor prices and customer demand can assist in pricing strategies to help increase profits and reduce costs. Analysing customer behaviour, wait times, and staffing level data can help identify areas that QSRs can improve efficiency, leading to reduced labour costs, utilities, and other overhead expenses.
Fraudulent activity can be found when analysing customer transactions, saving costs on fraud prevention and investigation. Wasted marketing spend can be minimised as understanding customer demographics, purchase history, and social media activity, can help steer marketing strategies on the right track.
How data analytics can be used to improve customer satisfaction
Data analytics can be used to improve customer satisfaction by providing insights into customer behaviour and preferences, which can guide the implementation of customer-centric strategies.
Identifying areas of improvement, such as a QSRs’ wait time being longer than the expected wait time, can help QSRs take the appropriate steps to minimise pain points. Tracking customer feedback and complaints can quickly find areas in need of improvement, such as customer feedback on food quality, cleanliness or staff, alerting QSRs to what is working and what isn’t.
Customer experiences can also be personalised by recommending products or services that customers are likely interested in as shown in data.
Summit Panorama Use Case
Summit Panorama helps QSRs compare their specified time goals for each stage of the drive-thru process to the actual time that is taken. This allows QSRs to see what stores and areas of the drive-thru are underperforming, so that improvements can be made for increased customer satisfaction.
How data analytics can be used to make better decisions
Ultimately, data analytics is all about using real time information from many areas of the QSR to make better decisions.
Identifying trends in customer behaviour can be used to adjust marketing campaigns and staffing levels accordingly. Analysing operations data helps improve inventory management, reduce food waste and improve efficiency. Customer data can be used to improve customer service, personalise marketing campaigns, and offer targeted promotions.
Sales data can help QSRs identify which products are the most popular and profitable, assisting in product pricing, marketing, and inventory choices. The data from QSRs that is collected and analysed is important in making sure decisions are made using the latest and most accurate information available.
How can QSR businesses use data analytics (and specifically Summit Panorama) to improve their business?
In today’s competitive QSR industry, data analytics is essential for success.
By collecting and analysing data from a variety of sources, QSRs can gain insights into their customers, operations, and market landscape, by accessing and comparing data at a moment’s notice. This information can then be used to make better business decisions, improve efficiency and boost profits.
Summit Panorama Use Case
Summit Panorama is a cloud-based store data aggregator that provides timely information to all levels of management, using one platform. Using scheduled broadcasts, exception notifications and ad-hoc enquiries, the information can be accessed whenever, wherever, and by whomever you choose. Being able to customise the information that is shared and what stores are compared, enables managers to view a comprehensive snapshot of individual stores and where they rank compared to others.
Summit Panorama shines a light on what areas of QSRs are overperforming or underperforming, so that informed decision making can be made to make the drive thru process as efficient and as cost-effective as possible.
If you think your QSR could benefit from an aggregated data management system, learn more about Summit Panorama