Citing the 2016 National Restaurant Forecast from the National Restaurant Association, Restaurant Business reports that online ordering, mobile apps, analytics, and loyalty programs are being used to encourage customers to come back, and to make it easier for them to do so. And the benefits don’t stop there.
“Even beyond the cues consumers themselves give off by their past behavior and preferences, restaurants are looking to anything that can help with predictions—including anticipating weather changes,” writes Restaurant Business. So how can you use your mPOS technology to predict customer behavior?
Here are three tips to bring the omniscience of data predictions into your planning process:
Use loyalty program data to create “next best offers”
I’ve written before about the benefits of a simple loyalty program with clear incentives, but what about leveraging those loyalty programs to figure out what your customers want to order?
An mPOS system like Instore makes it easy to build customer reward programs that also allow you to create a valuable customer database. By linking an email address to a credit card number, and the credit card number to a sale, you can track which customer likes to buy what, and when.
While the jury’s still out about whether past consumer buying can predict future—though you could argue that favorite food purchases are often more repeated than, say, a sneaker purchase—there are still many ways to use the loyalty program data on buying. There’s the most obvious—someone buys a pear and honey scone and you send them a targeted email discount for, you guessed it, another pear and honey scone. Or you could acknowledge the fickleness of humans and cast a wider net, as the Harvard Business Review suggests in its article on NBOs, or “next best offers.”
NBOs can get highly technical, relying as some big companies like Microsoft do on advanced analytics software, but the basic concept is simple—consider what else your customer might like and offer them that instead of what they actually purchased. For example, our pear and honey scone customer might really appreciate a coupon for a latte to dip the scone into. Or maybe you aggregate all the customers who bought pear and honey scones the previous week and discover that many of them also bought an iced tea. So you send all your scone folks a two-for-one offer on iced tea. For NBOs, think about Amazon’s “people who bought this also bought”. This is a basic NBO, based not only on data from one buyer’s behavior but from a cohort of similar buyers.
Link online ordering with daily deals analytics
Online ordering has become more popular in recent years and many anticipate it will soon overtake phone ordering. Online ordering allows you to add information to your customer database, to interact more often with your wired customers, and also to share relevant daily deals and coupons with your customers during the ordering process. Track the success of these daily deals and coupons with an mPOS system like Instore using the backend analytics built into the software. See how often your daily deals and coupons were used, track who used them, and use this information to predict which deals and coupons might be successful among certain buyers or at certain times of the year.
Refuse to be rained out
Finally, this last tip is a nice mix of high-tech and old-school business acumen. Inc. magazine reports that restaurants are among the many businesses lately taking advantage of improvements in weather forecasting. What does this have to do with predicting consumer behavior? According to the National Restaurant Association via Inc., nine out of 10 restaurants report that a grisly stretch of local weather affects their sales and customer counts. Turns out some restaurants are trying to make the best out of those bad days.
Bistro superchain Panera is one of them. According to Inc., Panera links weather data with its POS system to record what dishes sell well during certain weather patterns. Inc. writes: “If, for instance, Panera finds that its roast beef sandwiches sell best when it’s 65 degrees outside, the company can then promote that product when it’s 65 degrees outside.” Weather-based consumer data can also help a restaurant accurately stock its shelves (i.e. more hot cocoa if a blizzard is coming) and with a little marketing savvy can offset some of the slumps associated with particular weather (like the rash of pumpkin lattes advertised at the first autumn chill).
Whatever tack you take to predict consumer behavior, it’s bound to be an evolving process. But with an mPOS solution, you can learn from customer behavior and optimize your offers based on what’s resonating—helping you make informed, data-driven decisions.
Photo copyright: NASA Goddard Space Flight Center by CC BY 2.0