Three Ways Food Delivery Companies Use Predictive Data Analytics
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Personalize Services & Recommendations
Predictive data analytics plays a vital role in helping food delivery companies offer personalized recommendations based on the customer’s food habits and preferences. It also helps them analyze their order history and curate a personalized menu with new dishes that they are most likely to try.
Predict Demand
Demand prediction is crucial for food delivery companies looking to drive growth through personalization and by catering to the needs of their customers. Predictive data analytics solutions that leverage data mining techniques can help predict the demand for their services based on user interests and online journey.
Ensure On-time Delivery
Using predictive data analytics food delivery companies can not only understand customer needs but analyze the traffic patterns to ensure on-time delivery and enhance customer satisfaction.
To tackle the growing competition, food delivery companies need comprehensive insights that can help improve operational efficiency and drive growth. Request a FREE proposal to learn how we can help!
About the Client
Founded in 2012, the client is one of the world’s leading online food ordering and delivery platform. Based out of Austria, the company has over 100+ restaurants and retails chains associated with its brand name.
Business Challenge
With the growing competition from different brands globally, food delivery companies are now combating the challenges using advanced predictive data analytics solutions. Today, a common challenge faced by food delivery companies revolves around analyzing the data that’s available by tracking key metrics to measure performance, sales, and improve operations. Moreover, based on the need to manage increasing volumes and types of data from hybrid environments, food delivery companies also face challenges around data warehousing and data management.
Our client, a leading food delivery company, was looking to leverage predictive data analytics to analyze historic sales and customer data sets to understand its sales processes and customer needs. Though the food delivery firm had an on-premise business intelligence (BI) solution to analyze data sets and generate reports, their siloed data management system posed several challenges that curtailed its ability to do so. This is when they approached Quantzig looking to leverage its predictive data analytics capabilities to bring about a significant improvement in their business process in terms of revenue, customer satisfaction, and market position. Additionally, they wanted to deploy a scalable analytics-driven framework to manage the huge volumes of data sets obtained from disparate sources.
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Solution Offered
Quantzig’s predictive data analytics solutions helped the client deploy a custom-built solution to gain insights into customer preferences and market trends. The engagement comprised of three phases each of which played a crucial role in collecting, segmenting, and analyzing their business data.
Phase 1
Quantzig’s predictive data analytics team worked with the food delivery firm to help them frame problem statements and offered actionable insights based on data analysis, predictive analytics, market trend analysis, and reporting.
Phase 2
In phase two, our predictive data analytics experts leveraged non-SQL unstructured data, descriptive and predictive data mining techniques, and data visualization tools to deliver self-serve reports & a build a robust framework for data management. Our predictive data analytics solutions offered meaningful customer insights from high-level traffic data to granular insights on click behaviors.
Phase 3
The final phase revolved around the implementation of a robust predictive data analytics framework that focused on improving their ability to manage complex data sets from hybrid environments.
Request for more information to learn why food delivery companies are leveraging analytics to gain a competitive edge.
Result
With Quantzig’s predictive data analytics solutions, the online food delivery services provider was able to:
- Gain real-time insights to boost sales
- Offer personalized recommendations
- Increase sales by shortening the path to order
- Improve data management ability
