Case Study - Increasing Sales On Doordash, Uber Eats, And Grubhub Using Quick Service Restaurant Intelligence

By Author: Food Data Scrape
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Case Study – Increasing Sales on DoorDash, Uber Eats, and Grubhub using Quick Service Restaurant Intelligence
This case study focuses on a restaurant's decision to incorporate data analytics using Quick Service Restaurant Intelligence to gain insights into their performance on food delivery apps. By leveraging data, the restaurant aims to identify patterns, make informed decisions, and improve its overall performance on these platforms.

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The Client
Our client was an American Quick Service Restaurant wanting to gain insights into their performance on significant food delivery apps (Uber Eats, Grubhub, and DoorDash) to increase operational efficiency and satisfaction. They required data on delivery, availability, review & rating, discoverability, and advertising effectiveness.

Key Challenges
Obtaining accurate and comprehensive data from several sources seemed quite challenging.

Integrating data from multiple food delivery platforms was complex.

We realized it was a complex process to identify the performance, as sales depend on factors ...
... like competition, area population, local cuisine preference, etc.

The food delivery apps have mechanisms to limit automated data collection, which makes it difficult for us to access the data consistently.

Each app has its data structure and format. It made it challenging to adapt to changes in the app's design.

Major Solutions
Here is how we helped American QSR to get deep insights on different metrics:

Delivery Analysis
First scrape Uber Eats restaurant menu data and calculate their ETA index to help understand the significance of tracking delivery time and its impact on sales.

ETA index is the time compared to the lowest delivery time of other restaurants concerning the specific zip code, time, and date. We classified the indes into three major points:

0-100 signifies a highly competitive delivery time.
100-170 signifies a comparatively less competitive delivery time
170+ signifies a completely less competitive delivery time.
The lower the ETA, the low was the sales index. It indicates that customers preferred to choose restaurants with quick delivery time compared to our client.
Bakery, coffee, and tea in breakfast impacted high with the fastest delivery time.

Availability Analysis
Restaurant sales depend on the availability and the opening hours. Therefore, our Quick Service Food Intelligence analyzed DoorDash, Grubhub, and Uber Eats, especially during lunch and dinner time on weekends and weekdays. We found that:

The QSR availability on all these three platforms was 85.5%
Only for 2.6% of unavailability, there was a 26% drop in the sales index.
On DoorDash, we found the sales index doubled from 2.4 to 4.8
On Uber Eats, there was an increase in the sales index from 2.67 to 5.86, with only a 3% increase in availability.
Scrape Grubhub restaurant menu data to gain insights into the sales index that grew from 3% to 9%.

Discoverability Analysis
High discoverability and appearance on search engines indicate high sales volume. Hence, we discovered their online appearance by platform and category based on keywords.

On Grubhub, the sales index was, on average, 14.8%, which increased on weekends by 18.6% and on weekdays by 10%.
On Uber Eats, with the changing rank to 1, the sales index increased by 2%.
On DoorDash, with an average rank changing to 8, the sales index rose by 3.5%.
In the case of the bakery, tea, and coffee categories, there was an increase in sales by 15%. It got doubled on weekends.
However, in the healthy category, there was a rise in the index by 23%.
Advertising Effectiveness Analysis
Our client has spent dollars on these three major platforms to enhance discoverability. Hence, we helped them measure each campaign to understand their strengths, weaknesses, and ROI.

Of all the ads on DoorDash, the best-performing one was Late Night Craving which showed a 50% rise in sales.
There was a significant rise in 30% increase in sales, mainly when featured with 'Special offers.'
The two best-performing ads on Uber Eats were Today's Offers and the $0.99 delivery fee. Today's offer caused an increase of 13%.

Methodologies
Data Collection: We collected relevant data from various sources to comprehensively understand the market. It involved collecting data from internal and ensuring that the data collected was accurate and reliable. We performed analysis using 2000 outlets on the three major platforms. The total time duration for extraction was three months.

Data Analysis: Scrape DoorDash restaurant menu data to derive insights and make informed decisions. It involves applying various analytical techniques to uncover data patterns, trends, and correlations.

Prepared Action Plans: Based on the data analysis, we prepared action plans to improve the restaurant chain's performance. These recommendations covered men optimization, pricing strategies, marketing campaigns, customer experience enhancements, operational efficiencies, etc.

Advantages of Food Data Scrape QSR Intelligence Services
In-depth Market Insights: Our QSR Intelligence service offers comprehensive market insights, empowering our clients to make informed decisions and stay ahead of market dynamics.

Competitive Advantage: Using our QSR Intelligence service, our clients gain a competitive advantage and can analyze competitor strategies to benchmark their performance,

Menu Optimization: We assist our clients in menu optimization through data analysis and insights. We provide recommendations to optimize the menu, enhance profitability, and improve customer satisfaction.

Customer Experience Enhancement: Our QSR Intelligence service enhances the customer experience.

Data-Driven Decision Making: Our QSR Intelligence service helps make data-driven decisions, minimize risks, and maximize growth opportunities.

Source>> https://www.fooddatascrape.com/case-study-increasing-sales-on-quick-service-restaurant-intelligence.php

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