You almost certainly wouldn’t expect that the go-to meeting spot for online dating is Panera Bread. That’s because Panera doesn’t expend any energy branding itself as a rendezvous point for existing couples. And of course, most people dream of meeting their soulmate across a Venetian gondola, not a Bacon Turkey Bravo.
Herein lies the marketer’s challenge for a quick-service restaurant: How do you wade through the sea of online noise and find the revenue-driving insights? And then how do you eliminate your blind spots and find new ways to steal market share?
The solution is as clear as it is difficult to illuminate. Brands must listen to the voice of the buyer. And if Panera is unaware of its popularity with those going on dates, it is a pretty costly blind spot.
This first step toward listening to the buyer voice is finding the signal that correlates certain language patterns to actual sales. Companies will never be able to prove causation definitively. No one can. But they can use data science to eliminate anything that clearly isn’t driving revenue, measure and rank what remains for customer resonance, and find the likely drivers behind both their competitors’ business and their own.
Once quick-service marketers have tuned into this “buyer signal,” the next step is to listen to what consumers have to say about who competitors are winning over at their cash registers, and then determine how to steal them back.
Here, Panera could take its newfound insights to drive revenue and steal market share of digital dating users. This competitive advantage could be leveraged through creative messaging (like showcasing first dates in ads), product packaging, date night limited-time offers, and partnerships, such as offering movie coupons.
So how can quick-service restaurant marketers actually make this happen and find the noise in the signal?
Let’s start with this Tweet:
"I want to take a date to cold stone or Dairy Queen so bad"
While it’s quite clear the author would like to visit Cold Stone or Dairy Queen, we have no inkling if she has actually done so or if she ever will. Perhaps she’s someone who sets a goal and always achieves it. But maybe she’s budget-conscious this month and not dining out. We just don’t know.
This Tweet represents only an organic data point—an unprompted voice talking about brands. It certainly doesn’t give Cold Stone or Dairy Queen any insight into what drives revenue.
That’s because social listening isn’t enough. Today’s restaurant data operations demand clustering techniques that zero in on the buyer language to compile specific language clusters that are predictive of business metrics.
Instead of relying upon the data tables of cookie-cutter demographics (age, gender, marital status, income, etc.), the brand can instead define audiences by how they define themselves and the content they consume. This is accomplished by grouping folks into interest buckets they opt into online.
For example, if a quick-service restaurant is keen to understand its performance with people meeting for dates, it should look at the revenue-driving brand conversations of, say, all the people who engage online with at least one major dating site.
With that in mind, take a look at another tweet:
"I was able to buy 30 dollars worth of clothes and 13 dollars worth of panera with just my tip money thank god lmao"
Remember, we don’t need the consumer to tell us both that she spent money at Panera and that she’s on a date. But since this person also follows dating sites and discloses her purchase information, the share does represent the voice of the buyer.
After norming the dataset for sample sizes and intensity, it can be inferred that Panera over-performs relative to the competition with consumers who follow dating sites. Because of this revenue-driven approach, the brand can understand nuances in the competitive landscape to an extent that social listening simply cannot match.
Of course this insight alone isn’t a golden ticket to world domination, but it certainly could be used to drive growth and steal market share. Maybe Panera could offer bottomless tea and coffee after 7 p.m., allowing people to get to know one another later into the evening. Maybe the background music in stores starts featuring less cowbell and more Marvin Gaye. Maybe there are previously hidden pockets of McDonald’s customers looking to migrate to anotherrestaurant for their dates.
The voice of buyers can inform restaurants about how to adjust their product marketing to drive additional revenue, get rid of blind spots, and poach customers from the competition.