How Data From New Consumer Habits Will Drive the Future of Restaurant Personalization

Post written by: Chris Miller

The exponential rise of digital restaurant experiences in the last year has had a valuable side effect for brands driving toward personalized guest experiences – a treasure trove of data on individual customer behavior. 60% of restaurant users plan to continue ordering digitally, and digital orders are expected to grow to over 50% of total industry sales by 2025. Restaurants with online ordering are seeing more than 40% of their orders through this channel as of last April, a number that has surely increased, higher than most loyalty programs.
There’s no doubt that restaurants already understand personalized experiences — from the bartender who knows every loyal guest’s face and drink order, to the host who remembers a couple’s favorite corner booth. But the rise of digital demands personalization at scale. By collecting and leveraging user and transaction data, restaurants will be able to create customized guest experiences that rival the sophistication of digital-only services such as Amazon and Netflix. Imagine marketing campaigns that promote exactly what is relevant to each guest. Menus that organize items based on previous purchases and preferences. And dynamic pricing that can be adjusted based on guest satisfaction and frequency.

Digital ordering is not limited to off-premise dining. While restaurants will be motivated to use in-store digital ordering to control cost as wages continue to rise, guests have come to appreciate the seamless experience possible in a digital environment. Tools like kiosks, tablets, and apps bring the digital experience to the in-store environment – and their integration with loyalty and CRM creates a wealth of guest-centric transaction data.. In-store digital ordering will even bring cash buyers into the data stream with easier, safer ordering and links to loyalty program rewards. Future technologies like facial recognition can also be used to connect in-person experiences – including cash buyer transaction history – with digital guest profiles.

Since delivery and pre-order pick up interaction takes place outside of the restaurant, their growth reduces physical limitations, such as static menuboards, or printed menus that cannot be easily changed. Delivery-only brands are the ultimate manifestation of this trend. With no consumer-observable physical location, delivery-only brands can maximize the potential of online order data to create personalized menu presentation, ordering, and delivery experiences.
These trends will continue as consumers change their habits and try new things. Virtually everyone has started doing something new or stopped an old behavior  in the last 11 months. 75% of consumers have tried a new shopping behavior, and most intend to continue beyond the crisis.

People want personalization – if it improves their experiences

An Accenture study found that 83% of consumers want brands to understand them better, and want brands to know when, and when not, to approach them. Think of the classic high-end hotel concierge or waiter who knew exactly what their most frequent, highest-spending customers wanted and could anticipate their needs. Being personally catered to is a definition of luxury hospitality, previously available only to high spenders. With mass guest databases, even low-priced restaurants can personalize customer interactions, and even low-frequency, low-spending customers can enjoy personalized experiences based on their preferences.

The Walt Disney World MagicBand is a great example of how much information consumers are willing to share in exchange for a personalized experience. Guests know they are being tracked throughout the park, touch sensors during their experience, and even enter their PIN when making purchases, exchanging their data for a more magical experience.

Future restaurant personalization will build on existing infrastructure and efforts

While the challenge of offering truly personalized experiences can be daunting, restaurants have existing infrastructure that makes step-by-step evolution to more tailored experiences possible.

Loyalty programs will continue to form the foundation of CRM data, because of the existing installed base, largest current data sets of individual purchase history, and initial personalization efforts. Personica clients are already seeing higher action rates from simple personalization  such as birthday, join anniversary, or new member activity status messages. Most programs are segmenting members into targetable groups based on factors like visit frequency and key items ordered. With more data and AI-driven marketing automation, loyalty programs will evolve from segment-level personalization to 1:1 communication based on individual transaction data.

Marketing communications will be another starting point. Brands that create personalized messages based on guest purchasing habits and loyalty/CRM data are seeing higher engagement and response rates than those who don’t. Personica clients are driving more ROI with offers using basic personalization, like varying the featured image to reflect a frequently ordered item. By gathering more order data on more customers through POS and online order platform integrations, brands can create increasingly customized messages and truly personalized loyalty programs.

How one Personica client increased campaign ROI by 1200% using personalized messaging – read the Case Study now.

Deeper digital ordering data will also drive personalized acquisition opportunities. Shared digital ordering applications offer brands the potential to identify non-customers who order similar items from other brands. Third party delivery platforms are currently best positioned to offer this type of cross-customer marketing, but online ordering and loyalty platforms are poised to offer it soon.

Menu Item Presentation and Price Optimization will change dramatically, though it will require more new infrastructure than loyalty and communications. With digital ordering rapidly becoming the predominant order channel for brands that offer it, there’s a huge opportunity for brands to personalize menus throughout the ordering process, based on purchase history (including price sensitivity), weather, time of day, etc. Examples of future menu personalization opportunities include:

• Initial page features different products based on order history, time of day, and weather. For example, a guest who most often orders drip coffee but sometimes orders a latte and an egg sandwich could be shown an egg sandwich and latte combo on a cold morning. A guest who sometimes orders lemonade could be shown a combo featuring lemonade at lunch on a hot day. A guest who only has meatless items in their order history could be shown a menu featuring vegetarian item modifications.
• Order process will employ dynamic pricing and upsells. Some examples might include:
  • A guest who frequently orders the same lower priced sandwich and sides could be shown an opportunity to repeat the order with suggested add-ons – such as a discounted cold beverage or ice cream on a hot day.
  • Upsells can be tailored based on an initial order that gives clues to occasion. For example, a guest ordering a whole chicken could be shown options for sides and drinks — coleslaw, a gallon of lemonade, etc — priced based on price sensitivity.
  • Rather than being bound to a bi-annual cadence, price increases could occur at guest-level, based on individual price sensitivity and frequency.

While some of the above examples are in the future, brands can start experimenting today. For a great first step, try varying the initial page of your digital ordering interface by market, based on weather variations and what premium items are selling well. Track your results against a control group, and voila – you are moving into personalization.

Interested in exploring menu personalization for your brand? Contact us today and ask about our Pricing and Menu Analytics charter programs.

Personalization is segment agnostic. Restaurants are already experts in on-site personalization, regardless of type. Think of the coffee shop that prepares a regular’s drink as soon as she steps in the door – and the fine dining establishment that keeps anniversaries on file and surprises couples with champagne. While much of this personalization will come through digital ordering, many customers will still prefer a human interaction. Guest service employees will be prompted to recognize customer preferences and make suggestions that deliver a personalized experience to all customers.

Loyalty programs are starting to enable this as they are integrated into POS systems, suggesting recommendations employees can offer to program members based on their order history.

Ultimately, the service experience will integrate data-driven personalization throughout both  in-store and digital environments. An example from full-service restaurants could be when to bring the check. Based on how long customers took to pay in the past, servers can be prompted to bring the check just as the guest is thinking about it.

Start your personalization journey now

Bringing personalization at scale to restaurants will be a long journey, starting with small steps. By better leveraging existing data, restaurants can already begin improving guest interaction and experience. Future growth will require technology investments, strong partnerships, and a testing roadmap to zero in on the  best strategies and tactics. Brands can start by conducting personalization experiments across existing customizable touchpoints.

The ultimate outcome may be “personalized restaurants” – where each step of the restaurant experience meets individual customer needs, bringing the overall brand promise alive in individually relevant ways.

Chris Miller is a Research Consultant for Personica, leading a project to envision the journey to guest personalization. Chris has led marketing for Raising Cane’s Chicken Fingers, Smoothie King, and Einstein and Bruegger’s Bagels, and earned Effie Awards for marketing campaigns while leading product marketing for Taco Bell.  He led the analyst team at local market research firm Sandelman & Associates and has consulted for start-ups and established businesses.