How restaurants enhance their tech stack with intelligent event data
Restaurants are using new data to combat the labor shortage and rising operating costs
For restaurant operators, the cost of food is up by 10%, the average restaurant employee turnover rate is now at 32%, and there are 2 million job openings in the sector. It’s been a difficult market to navigate since the onset of the pandemic in 2020 and the resulting supply chain disruptions, higher costs, and dynamic fluctuations in demand levels.
Some restaurants are responding to these challenges with the use of new data – such as external events like concerts and conferences that impact demand. They are using this data to identify and incorporate peak demand days into planning and to train their forecasting models. With this data, restaurants are able to achieve the following at the individual restaurant level:
boost forecast accuracy
help guarantee great customer service
decrease employee stress and turnover
power faster food deliveries
avoid having to pay for costly overstaffing
minimize the impact of supply chain disruptions
optimize inventory levels and marketing plans
But you don’t have to be an industry leader, or even have a dedicated data team to integrate intelligent event data into your tech stack. Follow these four simple steps to unlock the power of event data and position your restaurant for success in the current market.
Improve your tech stack with event data in four steps
1. Prepare for integration
The first step to integrating intelligent event data into your ML models is to connect to our API and pull data using our Python SDK, or a tool such as Azure Data Factory. That is unless you plan to use Snowflake or Amazon Web Services data marketplaces, in which case, you can skip this step.
Once you've connected to our API, you’ll be able to extract, transfer, and load the data – which can be done easily using a tool such as PySpark.
2. Choose an integration partner
Ingest the world’s most trusted source of event data via the integration option that best fits your needs:
Quickly process impactful events near you without delay via Amazon Data Exchange (S3), a trusted APN technology partner
Skip over the convoluted ELT/ETL processes and start leveraging PredictHQ’s intelligent event data in minutes via Snowflake Secure Data Share.
PredictHQ offers options for different API access points to suit your needs– from a featurized API to build models faster, to an event API for more granularity.
3. Build and deploy models
At this point in the integration process, you’ll potentially be doing some form of data aggregation through SQL in Snowflake before you send it to your modeling tool. To go from experimentation to machine learning model deployments, you may use options such as the following:
4. Action the insights
After building your demand forecasting models, it will be time to surface intelligent event data and inform your teams of the impact of events, which you can do with your existing business intelligence or visualization tools.
This is a great way to allow your GMs or Operations Managers to not only know have a more accurate forecast for staffing decisions, but also give them confidence that they don’t need to worry about events – because they can see events directly in the platform.
Join the ranks of restaurants improving their tech stack with intelligent event data
Smarter demand forecasting needs external data to learn and adapt. With intelligent event data informing your planning, you an optimize operations to align with periods of higher and lower demand – which saves QSRs millions each year in labor-related costs, food wastage, and more.
PredictHQ collects and verifies billions of data points from across the globe so you can integrate verified, enriched, forecast-ready event data into your machine learning models.
Make data-driven decisions about staffing, inventory, delivery, and more with insight driven by 19 categories of events tracked across 30,000 cities and counting. Sign up for your free account today to explore events with our web application or suite of APIs.
