Food Service (Delivery)

Analysts expect delivery to continue to grow across all segments of the industry, but particularly among consumers already focused on convenience.

Usman Mahmood

Written by
Usman Mahmood

March 20, 2019

Morgan Stanley estimates that meals ordered online can grow 20x their current size to nearly half of restaurant sales in the United States[1]. Analysts expect delivery to continue to grow across all segments of the industry, but particularly among consumers already focused on convenience[2].

The Challenges

In their attempt to remain competitive and keep pace with this growing consumer trend, restaurants choose to either provide their own delivery service or partner with third-party delivery companies (e.g. GrubHub, UberEats, DoorDash).

While an own delivery offering provides increased control over the quality of service and customer feedback, the logistics quickly become complex and costly.

Alternatively, third party service providers, although convenient, can significantly impact profits due to high fees and disrupt order management systems and procedures.

The greatest tradeoff is the loss of valuable customer data that ultimately becomes proprietary to delivery providers and not utilized for business strategy and growth.

The Keboola Way

How can delivery customers be identified and better engaged with for better targeting?

How are delivery orders affecting wait times and service quality for other dine-in or take out customers?

What is the best way to leverage data shared via third party systems?

Will a facility dedicated solely for delivery orders be more profitable and improve speed of delivery than the traditional hybrid concept?

Keboola helps restaurant executives evaluate the impact of various business strategies on company profits through intelligent use of their data. Insights derived from this data can be instrumental in planning operational changes based on variances in orders volumes, geography, and other related factors. In addition, real-time operational data can be used to make proactive and automated advertising decisions to further drive revenue and profits.

Use Cases

Maximize Capacity Utilization: Identify traffic and order patterns based on time of day, location, and other factors and allocate resources (inventory, staff) to maximize throughput and minimize waste.

Text Analytics of Customer Reviews: Gather review data (text, review scores) and perform sentiment analysis and text analytics. Cross reference results with location specific business data to identify patterns and trends associated with positive/negative customer reviews and take remedial actions.

Supply chain optimization: Improved demand forecasting can help make optimal inventory and logistics decisions to improve customer satisfaction both in terms of quality/freshness of product and speed of delivery service.

Marketing Ads: Experiment and optimize online ad spend with delivery partners or across other digital channels based on customer response as well as capacity utilization. For example, automated increase of push ads or special offers to people nearby on UberEats in case a specific restaurant location is having excess capacity at a particular time of day.




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