Harnessing the Power of Location & Weather Intelligence in QSR
Imagine this: as lunchtime approaches, Dave peruses vegetarian menu options at a local quick service restaurant (QSR) from his desktop, placing an order of a veggie burger with no cheese, and a cookie in his cart. In a rush to get to a meeting, he must exit out before completing the order. As the meeting progresses, the usual hot and sunny Bay Area temperature drops to a windy 68℉. When the meeting ends, Dave pulls out his phone to see a notification from the QSR’s app:
“It’s feeling a little cold outside. Warm yourself! Add a hot cappuccino to your order.”
When he taps the notification, he is brought to his cart, which has the same veggie burger, cookie, and customizations saved, along with the option to add a cappuccino. Impressed with the restaurant’s timely offer for a warm beverage, Dave accepts the promotion, and using his card on file, seamlessly completes the order.
Real-Time, Omni-Channel Engagement in the Modern Quick Service Restaurant
What you just read was real-time omnichannel marketing in action. For today’s on-the-go customer, oftentimes where an order begins is not where it ends—and the options for engagement are only increasing. After all, 70% of QSR operators intend to invest more in customer-facing technology to enable online or app ordering, mobile payments, and delivery management. That’s where an omnichannel approach to customer engagement comes into play. From the desktop to the mobile device to in-person ordering, omnichannel personalization means delivering relevant, seamless, and targeted experiences no matter where the customer engages.
However, true personalization goes beyond the omnichannel approach. Effective 1:1 personalized engagement also requires consideration of the customer’s in-the-moment context. This context includes a wide range of environmental factors such as weather, time of day, and location to determine the right promotion to serve up. As we read in the above-mentioned scenario, a sudden drop in temperature in the summertime in the Bay Area may feel cool, prompting a cross-sell of cappuccino.
Let’s consider another example:
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.
Reacting to In-the-Moment Context — Weather and Location
Restaurants and convenience stores can also leverage location intelligence and proximity data to serve up location-centric information and promotions to drive customer traffic to a particular location. These decisions, made based on the real-time context of their customers and their locations, keep wait times down for lasting satisfaction and loyalty.
This level of personalization requires the extraction of a large volume of ever-evolving data by zip code on temperature, season, location, and time. It is then paired with a customer’s in-the-moment context to use by-day historical averages to identify if it “feels cold” or “feels hot” at that location in that moment to offer real-time guidance.
Similarly, to enable location-based customer interactions, restaurants and convenience stores must understand the distribution of app users by zip code and DMA code, and create dynamic segments based on location proximity. When location permissions are shared, they must be able to recognize when customers are in new areas and adjust recommendations accordingly. At the same time, when location permissions are not shared then the company may have to partner with a solution provider that can use ML to ascertain the best location of a customer and deliver the appropriate experience.
For instance, suppose Dave drives from San Jose CA to San Francisco in the late afternoon to attend a meeting. Unbeknown to him, his favorite coffee shop has a free coffee offer going on between 3 p.m. and 6 p.m. Dave is very privacy conscious and has not enabled location permission on his phone. So, how can the coffee shop reach out to him if it cannot determine his location? An intelligent customer engagement platform can do this by calculating Dave’s best location and ensure that this broadcast announcement reaches him when he enters San Francisco, irrespective of permissions.
“Welcome to San Francisco, Dave! Enjoy a free cup of coffee on us today. Hurry, the offer ends at 6 p.m. …”
While gathering such intelligence might sound like a tall order, today’s AI and ML technologies can definitely make it happen. And the results of such real-time engagement are well worth it — a top QSR achieved a 31% boost in on-site traffic when it updated its ads according to changes in the weather. Additionally, it is forecasted that location targeting will grow to $38.7 billion by 2022.
Adding a Personal Touch: Serving Up In-the-Moment Personalization
However, for a successful real-time and omnichannel approach to customer engagement, it isn’t enough to simply be where the customer is. A restaurant or store must also offer up what the customers are looking for or what they usually prefer. Critical for doing so is the company’s ability to leverage historic data and loyalty program participation across devices—starting first with the mobile app.
As a channel-specific to the individual customer, the mobile device enables companies to serve up push notifications or in-app messages that are tailored to each customer, no matter where the customer engages. For example, say a customer typically stops by your drive-thru between noon and 1 p.m., but that tends to be a high-traffic time for the drive-thru. Then you could send this customer a promotion via their loyalty app for a free side, redeemable in-restaurant only. When the customer enters the restaurant, this reward is then seamlessly served up, regardless of if he places his order at a kiosk, tablet, or the front register.
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 the 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 to 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.
Learn more about how to attract customers to your restaurant in our video, or contact a member of the ZineOne team today.
Video: How to Attract More Customers to Your Restaurant