What’s the Weather Intelligence Today?

By Team ZineOne September 2, 2020
Accounting for relative temperature perceptions by location

Harnessing the Power of Location & Weather Intelligence in QSR

Imagine this: it is a 70° F day in Houston, TX. Sarah receives a push-notification to her mobile device from her favorite quick service restaurant (QSR). The message reads:

“Want to cool off? Get $1 off any large iced beverage today!”

Now flash to Chicago, IL, where the weather also happens to be 70° F. Patrick, an equally loyal customer, receives the same message as Sarah offering him a discount on an iced beverage. Upon seeing the offer, Patrick heads to the restaurant later that day to redeem the reward; Sarah does not.

Why did Patrick utilize the offer, and Sarah didn’t?

It all comes down to the relevance of the offer based on their particular environmental conditions. In Chicago, 70° F is considered a relatively warm day, and so an offer for a cool drink would appeal. Conversely, 70° F is considered a cooler day in Houston, and as a result, likely would not lead to a sale. A more relevant offer in Houston instead would have been a discount on a hot beverage.

Introducing Weather into the QSR Personalization Strategy

For QSRs looking to increase sales and engagement through personalized customer outreach, it is critical not to underestimate the role that weather and location data can play in decision-making. Before initiating an engagement with a customer, QSRs should ensure they:

Know Where the Customer is Located

An individual’s surroundings often inform their dining decisions. For instance, if it’s raining in a particular zip code, an offer for discounted at-home delivery will likely resonate more than a BOGO offer that must be redeemed in-person. The same holds true when utilizing location data to access a customer’s time zone to ensure meal-specific promotions are deployed at the appropriate hour. When working with intelligent personalization solutions, QSRs are empowered to go one step further, knowing their customer’s precise location, rather than just their zip code. In the event a customer breaches the restaurant’s geo-fence, they can then serve up a targeted offer for in-person ordering and/or contactless pickup.

Know the Relative Temperature for That Location

Tapping into local weather services can provide data on current weather conditions. However, true personalization goes one step further to consider how that temperature is perceived in relation to the area under consideration. This would allow the quick service restaurant to understand that 70° F would be considered differently by a person living in Chicago than a person living in Houston, allowing the QSR to serve up relevant, timely and personalized offers based on each individual’s environmental context and perception.

Making Weather & Location Data Intelligent

While many QSRs are introducing solutions that utilize weather data, it is important to recognize that weather data only operates in absolute terms. That means that a universal trigger is applied, and once a particular temperature threshold is met, an offer will be sent out regardless of how that temperature is considered relative to an area. Instead, QSRs should utilize weather intelligence—a more sophisticated approach to detect a relative change in data—to evaluate local perception and ensure relevance. With this approach, weather data is analyzed in conjunction with location data to answer qualitative, rather than quantitative, questions regarding weather: Does 70° F feel hot or cold in this particular location? 

For many QSRs, understanding environmental conditions in the context of a myriad of other factors—such as the time a customer likes to eat, physical location, and menu specials—introduces too many variables to consider. That’s where ZineOne comes in. Our AI-powered Intelligent Customer Engagement (ICE) platform utilizes location and weather intelligence to serve up offers that are highly relevant for an individual’s preferences and perceptions. Through our growing library of machine learning models, we are able to gather and form insights that allow restaurants to bring in-the-moment value to every customer interaction.

Video: How to Attract More Customers to Your Restaurant

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