COVID-19 is a huge data story in many ways, and food delivery analytics are a big part of that. Online food ordering in 2020 hit $115 billion globally and could reach nearly $127 billion in 2021 according to an April 2021 report. For much of the pandemic, and through to today, diners switched from sit-down dining to takeout and delivery, a decision based as much on consumer access to data (like local infection rates) as local mandates that saw many restaurants unable to seat large numbers of guests.
Restaurant margins have always been thin, and the right understanding of data to make them successful has long eluded many smaller, local businesses. Sure, TGI Fridays and McDonald’s have corporate offices with teams of analysts optimizing ad spend and tracking the performance of each franchise location, but your local pizza joint has almost no idea how to use data to be successful.
However there’s hope on the horizon! Personica and other software providers are making infusing analytics into internal workflows a possibility for restaurants of all kinds. Ordering platforms like Seamless also have an opportunity to increase revenue and improve customer satisfaction with analytics in their offering. RetailZoom’s pioneering work understanding beer “substitutability” also opens countless doors for foodservice companies looking to expand market share and truly delight everyone who comes through their doors. Whichever end of the diner-restaurant relationship a company is on and whatever size it is, buying analytics and embedding them in its core offering is a critical way to get to market faster, stay focused on its core competency, and drive stickiness/product differentiation with target users.
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Personica drives customer loyalty and boosts restaurant conversion rates
You wouldn’t know it from my LinkedIn, but I spent almost a decade working in kitchens and restaurants of all kinds, so I can speak from experience when I say that most small local restaurants are completely at a loss as to how to drive new business or keep existing customers coming back. Sure, some stuff is obvious (good food, comfortable setting, attentive staff), but when it comes to understanding which marketing efforts are having the greatest impact, the teams behind most of your favorite restaurants are left scratching their heads.
That’s where Sisense client Personica comes in. The company helps restaurants roll out professional customer loyalty programs and provide them with the analytics they need to understand which campaigns are succeeding and where they need to change course. For one customer loyalty campaign, guests were rewarded with a free entree after a certain number of visits (with entree purchase) in a given month. This is a great place for analytics to help customers and restaurants: The restaurant checks its analytics to see how many customers visited the requisite number of times (digging into things like average check size tied to these accounts can also shed light on how lucrative these repeat diners are); meanwhile, customers with loyalty apps can also see how close they are to getting a reward or how many past rewards they’ve earned.
Analytics embedded into applications create positive habits for internal teams and external customers alike. Customers like understanding their past behaviors, and linking those actions to rewards just strengthens this pattern. As loyal customers come back again and again, analyzing their buying behaviors also helps restaurants target them with offers that will appeal to them. If you have a customer who consistently orders a certain type of burger, Personica’s personalized email analytics show, emailing them offers for that particular burger will be more successful than plying them with a generalized burger or meal deal. This is a detailed view into diner behavior that’s a real game changer for smaller shops.
All this specialization is made possible by combining the right analytics with deep industry knowledge and personal care. Companies looking to supercharge their core product or service with analytics — a must-have in this era of rapid change — are much better off buying analytics from a third party and embedding them versus trying to build them from scratch. This goes for food analytics or anything else you need actionable intelligence around.
Analytics in ordering apps evolve the market
There are so many ways to order food of every imaginable description without leaving your couch: Seamless, Toast, Yelp, and countless other apps and sites put your local food scene in your hands. But when it comes to educating diners about their past behaviors and driving new business, analytics offer untapped growth potential in this vital vertical.
A perfect example where analytics can drive new business is Seamless, whose recently unveiled “Seamless Unlimited” product offers subscribers unlimited free delivery for a monthly fee. Companies from Amazon to Dollar Shave Club to Disney and other outlets too numerous to name have seized upon the fact that whatever a company does or sells, making it a subscription service is a surefire way to keep revenue coming in and develop a long-term relationship with an audience. One of the simplest analytics deployments would be for Seamless to show users how much they spent on delivery fees in a given month and how much they could have saved with Unlimited. The company could even sum up past months’ worth of delivery fees to make the numbers even more dramatic: Pizza Saturday night, Thai from across town for date night, and Indian from two neighborhoods over when you just had to have it can all add up to a lot of delivery fees.
All those different restaurants also present opportunities for shoppers to diversify their diets, either on their own or with help from restaurants. One user might think, looking into their past orders: I’m eating too many burgers. Maybe something vegan tonight. Or, with anonymized access to trend data through the ordering platform, local joints can vie for a slice of the pie in a given area by sending users specialized offers to buy from them instead.
Infusing actionable intelligence from huge datasets delivers better customer experiences for end users and empowers restaurants to drive new business. Connecting to these disparate datasets and surfacing insights from that data to the right people at the right time are key ways that third-party analytics empower companies with capabilities beyond what they could create on their own.
RetailZoom keeps beer drinkers satisfied
Instant gratification is a cornerstone of the online ordering ethos, but food and beverage data can also help in-person shoppers or people picking up their food find their next delicious choice, either when their go-to option isn’t available or when they might be in the mood for a change. Cyprus-based Sisense customer RetailZoom crunches immense amounts of data from its clients to help grocery stores optimize their supply levels and which items they order. Recently, RetailZoom did a deep dive into the buying habits of beer drinkers to offer supermarkets (and the general public) a glimpse into what people buy when their favorite beers aren’t available.
“We tracked the last two years,” says Data and Analytics Manager Constantinos Mavrommatis. “With millions of transactions, we ended up with a substitution probability for every product — and we did this for 120 categories.”
The insights from such a deep well of beer buying data revealed where beer drinkers turn when their preferred brand isn’t available. This is maybe something of a novelty for the beer drinkers themselves, though the more adventurous out there may choose to try one of the alternatives sometime on a lark. For retailers, though, it gives clear direction: “If you stop carrying brand X but do carry Y and Z, you will likely be all right.” However, it can also show which brands have a higher degree of loyalty that can’t be substituted around. (Strangely enough, while not very high on people’s individual lists, Heineken served as a substitute in almost every circumstance — good to know for shops that want to make sure they have a safe fallback for all shoppers.)
These types of key learnings, drawn from immense, complex datasets, can be infused into point-of-sale devices and personal handhelds to help advise workers on the fly. The results would be better conversions, happier customers, and less stressed workers — so everyone wins. And it’s all powered by industry-leading embedded analytics beyond what RetailZoom (or most non-analytics companies) could build from scratch. Embedded analytics have the ability to evolve almost every business, whether they deal with food and beverage data, marketing data, or anything else. And that’s serious food for thought.
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Jack Cieslak has written for and about the tech world for over a decade, having worked for Amazon, CB Insights, and others, writing on topics ranging from ecommerce and VC investments to crazy product launches and top-secret startup projects.