The Future of Fast Food: QSRs Predict Orders Before They're Placed
What is predictive demand intelligence?
Predictive demand intelligence combines AI-powered tools and smart data to forecast how real-world events impact consumer demand patterns. This intelligence enables businesses to make accurate predictions about demand, customer behavior, and market trends, driving more informed decisions across operations, marketing, pricing, and more.
With global events such as concerts, festivals, sports, and more influencing over $1.1 trillion in consumer spending annually, tracking these events and understanding their impact on demand has historically been a challenge. Predictive demand intelligence uses AI, machine learning, and predictive analytics to analyze vast amounts of data, identify patterns, and generate accurate future demand forecasts.
Leading QSRs use predictive demand intelligence to predict (and prepare for) demand volatility
Each restaurant has unique demand drivers based on its location, customer demographics, and operational priorities. For example, a QSR near a sports stadium will experience more demand fluctuations during local events such as a sports game or concert than a drive-thru right off of a major highway, or near an airport.
Similarly, a restaurant near a college may have higher demand during specific times of the day or year, driven by student schedules. Predictive demand intelligence accounts for these variables, delivering highly accurate forecasts on when and where demand will rise or fall, and to what extent.
By integrating demand intelligence into their forecasting systems, global fast-casual brands are achieving:
QSR uses smart data to predict orders before they come in
A leading fast-food casual restaurant is leveraging PredictHQ’s Sports and Live TV Event Data Data to revolutionize demand forecasting. By integrating over 200 features from PredictHQ’s Features API, the restaurant built an advanced forecasting model to guide kitchen staff on how many burgers, pizzas, wings, and fries to prepare—well before orders come in.
This data-driven approach feeds real-time demand forecasts to their kitchen display system every 20 minutes, providing precise guidance on what to cook and when. The result? A seamless kitchen operation that ensures the right amount of food is ready at the right time.
Key benefits include:
On the very first day of using their real-time demand forecasting system, the kitchen staff were prepping a large order when the system suddenly predicted a surge—25 burgers, fries, and other popular items needed immediately. Though skeptical at first, the cooks followed the forecast and within 20 minutes, a surge of orders came in, using up all the food they had just prepared. The system’s forecasting accuracy left the team amazed as they saw the prediction come to life in real time.
The future of flavor
By taking into account the predicted local TV viewership, sports attendance, and associated spending, the company’s kitchen display system is aligning closely with actual demand. With PredictHQ’s predictive demand intelligence as a key ingredient, their kitchens are now more efficient and responsive to customer cravings.
This is just one example of how predictive demand intelligence is revolutionizing the QSR industry. As companies continue to explore new data-driven solutions, advanced AI techniques such as deep learning and natural language processing will further refine predictive models, enabling even higher levels of forecasting accuracy.
In the next section, we’ll delve into how you can seamlessly integrate PredictHQ’s predictive demand intelligence into your forecasting strategy to leverage real-time data on sports, live TV events, and other factors that influence consumer demand. By doing so, you can make more informed decisions, optimize operations, and enhance customer satisfaction.
Elevate your demand forecasting with AI-powered event intelligence
By incorporating PredictHQ's demand intelligence, you can significantly enhance the accuracy of your machine learning models. For example, the delivery company Favor reported a reduction of 5-6% in their forecasting error, with others often ranging from 5% to 10% or more.
Our comprehensive suite of APIs provides the tools you need to identify, predict and then seamlessly integrate the most impactful events by store location into your existing systems. From granular event data to pre-built features, PredictHQ offers a flexible approach to enhance your forecasting accuracy.?
Next, we'll guide you through a three-step process to effectively leverage our suite of APIs to integrate relevant, demand-driving event data as event features in your demand forecasting models to make your forecasts more accurate.?
领英推荐
3 steps to integrate PredictHQs event features into your demand forecasting model
Built upon extensive event coverage, PredictHQ’s event features aggregate similar events into predefined groups for specific locations at set intervals, such as daily aggregations. These prebuilt, forecast-ready features can be added directly to machine learning models without further preprocessing. Here’s how to identify, retrieve, and integrate these event features into a demand forecasting model in three steps:?
Step 1: Select Relevant Event Features
Accurate demand forecasting at scale requires machine learning models and ruthless prioritization of the features you build into the model. Making a pipeline ever more complex with additional models can actually hurt performance.?
The Beam API automatically provides a list of Important Features based on your historical demand data and location. Alternatively, you can access Beam via Demand Analysis in our web application and copy the Important Features directly from your browser.
Learn more about Selecting Event Features.
Step 2: Get Features
Once you’ve identified which features are most relevant to you, get access to an extensive library of prebuilt, forecast-ready features ready for direct integration into your ML models with our Features API.?
It’s a powerful tool that reduces the time it takes to use our demand intelligence in your forecasting models from months down to days. Simply specify the date range, location and list of features, all of which can be sourced from the Beam API.
Learn more about Getting Event Features.
Step 3: ML Model and Future Predictions
Our Features API provides pre-built event features that are ready to be integrated into your existing dataset. Simply merge them based on location ID and date to enhance your model's accuracy and predictive power by adding valuable demand-driving event data.
For future predictions, you can access forward-facing data, such as the next two weeks or the upcoming month by querying the Features API. Work closely with your engineering team to ensure these new features are effectively incorporated into your production pipeline.
Get the full tutorial on Integrating event features.
Boost forecasting accuracy by at least 10%
Are you ready to transform your QSR operations with the power of predictive demand intelligence? Contact us today to learn more about how our solutions can help you:
Don’t miss out on the opportunity to revolutionize your QSR business. Sign up or Book a demo?today and discover how predictive demand intelligence can drive significant improvements.