Remember 2019? Sitting down to dine indoors? In that long-lost era, the digital ordering and payment channel constituted only about 1 to 5% of sales.
Enter the Covid-19 pandemic, and suddenly many restaurants had to switch gears and launch or augment their digital ordering channels.
In 2020, digital ordering jumped to an average of 43% of sales. In 2021, digital sales rose an additional 39%. This was the highest rate for Q1 e-commerce sales since 2001. In other words, the adoption of digital in the restaurant industry constituted the biggest shift in commerce since the dot-com boom!
Connected commerce in the minds of restaurateurs really comes down to delivering a coherent, seamless guest experience, digitally. These experiences may look slightly different from one operator to another, whether it’s ordering via third party for delivery, ordering directly from the restaurant’s app or web site for self-service curbside pickup, or any other of a range of service options that have become even more prominent in today’s world.
Digital is What People Want
During COVID-19 lockdowns, grocery consumers and restaurant guests tried digital ordering, many for the first time. It started with necessity, as stores limited shopper capacity and restaurants closed dining rooms or limited capacity. Schools and offices closed, and families trapped at home needed nourishment and calorie-based comfort.
People tried digital to order restaurant food or groceries. Retailers and restaurants adjusted by implementing or expanding curbside pickup, delivery, or other means of fulfilling orders. Having discovered the convenience, guests and grocery customers are not going back. Sixty-five percent of consumers state that they intend to continue ordering food digitally.
To meet the need and demand for digital, some restaurateurs partnered with third parties. Others launched direct digital and delivery channels. Still others adopted a hybrid approach: direct channel orders fulfilled by delivery partners.
Digital Makes More Money for a Restaurant
During the pandemic, restaurants recognized that guests had different needs. More people in households were working and learning from home. Many restaurants offered family packs for delivery or pickup as an alternative to dining room service. Some households actually ordered more than one delivery meal at a time since they had to pay the delivery fee anyway, saving one for later.
Even without family packs, guests tend to spend more per order when they order online, in-app, or through a third party: around 15 to 20% more!
Digital Gives the Restaurateur Access to More Data
When guests order online, they typically have to register or at least provide basic contact information. This makes purchase data trackable to a specific human. This is much harder if not impossible to do with just POS sales and in a brick-and-mortar-only restaurant.
Combining POS data with payments data and individual identity is the trifecta in understanding restaurant guests. It opens the opportunity to market to them (following opt-in and other laws) with the goal of turning visitors into regulars.
Restaurateurs are climbing out of the pit of data silos caused by having so many channels and systems that don’t share data. Fifty-eight percent of marketers struggle with audience segmentation and targeting because they are unable to tap into their own first-party data. Worse, they might be working with third parties that don’t share guest data. Data scientists are expensive to hire and keep on staff, which puts restaurateurs in the unfortunate position of having a glut of data much of which they simply can’t use.
What’s the Good Data News?
There are companies that are working on the specific issues and challenges of normalizing data so it’s useful. Xenial is among the companies working on ways to help merchants and restaurant operators to solve these challenges.
What’s the Future of Data from Online Ordering and Digital Commerce?
It’s an exciting time, as companies apply emerging technologies such as machine learning and artificial intelligence (AI) to help normalize data, give it meaning and make it more useful in context.
