Building Virtual F&B Brands by asking Data-Driven Questions

Photo by Jason Briscoe on Unsplash

This article is a playbook guide to formulating better data-driven questions for creating virtual brands and positioning them. Listed are a set of targeted questions that can help materialize abstract concepts into actionable insights that can help grow your business, and align your operations teams with strategy. At Dotlas, we specialize in assisting F&B companies, including cloud kitchens with the insights they need to gain a competitive advantage. Find out more at:

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What is a Cloud Kitchen?

A cloud kitchen is like a restaurant, but does not have the dine-in service component. Customers interact with the offerings of a cloud kitchen using food-delivery apps, or custom brand-specific apps. This allows a cloud kitchen business to focus on takeout-style food, food-quality, cuisine selection, etc. This business model is beneficial for increasing profit margins since the overhead of managing a dine-in setup with staff is reduced. However, the optimization can be improved threefold with some innovative strategies.

A cloud kitchen can essentially become a “house of brands” where a single kitchen could span the size of a food-truck to the size of a warehouse. Consequently, a single kitchen can host tens or hundreds of brands. As far as the customer is concerned, they’re ordering from a different brand, but all of them fall under the umbrella of a single business to the owner(s). These type of brands are called “Virtual Brands”, as they do not possess a physical presence like a standard restaurant, and only exist as logical separations in the cloud kitchen and as separate entities in the food delivery app. This opens up certain degrees of freedom for cloud kitchens that are otherwise not available to a brick-and-mortar (traditional) restaurant.

1. Static Backend & Cycling Frontend
   Same staff, same equipment and same ingredients in the kitchen deliver different kinds of cuisines, brands and food to the customer. This enables cloud kitchens to “kill” a brand that does not perform well in favour of a new brand that fixes the shortcomings of the predecessor in an effort to “keep their market” without an overhaul of their infrastructure and capital.
2. SKU optimization. SKUs are the ingredients used by different food items, such as Tomatoes, etc. A burger brand and pizza brand can share common SKUs, in this case the tomato. This creates common “buckets” or “channels” of SKUs that can be shared resources across multiple brands.
3. Increases Flexibility. Since everything can be an experiment or a process of trial-and-error, owners are free to be flexible in trying new marketing strategies, new cuisines (such as fusions), targeting different demographics, use different promotions and so on. They can keep rotating ideas until they find the one that just sticks with the market, at a very minimal cost of production.
4. Ease of Expansion. A cloud kitchen unlike a restaurant doesn’t have to be on Main street. One can move a cloud kitchen to a less expensive area and still serve the same target market based on proximity, traffic and other parameters. Due to less strict location constraints, cloud kitchens can expand quickly and take the brands that matter to new locations or new markets faster.

The Competitive Advantage

A cloud kitchen performs better than a competing kitchen:

1. If its virtual brands generate more revenue than a competitor’s brand.
2. If its infra, capital cost & spillage are less than a competitor across all brands.
3. By reducing the time taken to put a new virtual brand on market. This includes everything from marketing the brand to preparing menus, cuisine selection and more.

The Big Questions

• How can a cloud kitchen create a specification for a brand that is statistically likely to perform well?
• What is the definition of a good brand? How can 2 brands be compared to each other.
• What kind of indicators need to be measured that can result in brand discovery?
• Where should a brand be positioned with respect to other brands in the city, and in the world at large with respect to cuisines, categories and price offerings?
• What are the hottest trends where demand needs to be served?
• How can a brand stand out with respect to the competition, in the eyes of the customer?
• How can a brand induce customers to order. How can demand be created, or trends be set?

The Key Ingredient 🍜

Revenue is the objective optimized against the variables of Average Order Value and Order Volume. Every brand, based on the drivers of Revenue will sit in a different point of AOV and OV distributions.

When optimizing for Order Volume, the main focus should be on creating brands around the concept of Revenue Frequency, which we can categorize through the following high-level brand archetypes:

Daily consumption: Brands serving low-cost, high-frequency items such as coffees that ensure a reliable and consistent client-base.

Multi-weekly consumption: Lunch-focused cheap brands with quick and fast meals. These could be anything from brands serving salads, wraps, simple burgers.

Weekly consumption: Any type of brand whose consumption pattern is not frequent enough to happen several times a week, but seen more as an indulgence by consumers. These brands would be serving cakes or premium dinners such as high-end burgers.

Monthly consumption: These are high-end pricy meals, focused on special occasions. These could be expensive items such as sushi or seafood, or event-oriented meals such as items servicing 10+ people.

Conversely, in order to optimize for Average Order Value, a brand could be sub-divided as follows:

Single Meal: Typically simple, fast meals for working professionals during lunch, or one-person dinner combos.

Group Meal: Meals oriented at couples for special occasions such as weekly dinners or menus serving whole families including kid’s meals.

Lastly, there is yet another important sub-division based on the brand’s potential for revenue generation throughout its lifetime:

M Brands (Marathon): Long-Term brands that continuously serve a regular order flow over a long period of time. A go-to lunch / dinner brand that behaves as an everyday eatery.

S Brands (Sprint): Short-Term sprint (or seasonal) brands that serve during a particular season or to serve a culinary trend at its peak, but dies out later.

This means that an S brand serves a shock revenue over a short period of time, whereas an M brand serves a sustainable revenue over a long period of time.

Factors Influencing Brand Revenue 💵

Key Factors influencing revenue of a brand, which in turn influences a brand’s rank in a cloud kitchen portfolio include:

Positioning 🛍

Marketing the brand to customers

• Influencing perception of the brand by USP, attraction, offers, etc.
• Building long-term consumer trust in the brand.
• Identify all points in the flow of order to consumption that can hamper relations.

Deciding on the theme of the brand (different from cuisine and category):

• Is the theme of the menu and delivery retro-like, classic, festive, etc.
• Under what occasions does the brand offer discounts or craft special items

Customer Targeting

• Segmenting consumers of other brands in the cloud-kitchen based on behaviour, eating habits, etc.

Location 📌

What are the needs of the consumers around the Kitchen

• What is the purchasing power of demographics across varying contours of delivery time?
• What is the distribution of other restaurants around this area?
• Finding a brand that works to satisfy underserved consumers.
• Adaptation of the brand to neighbourhood trends (on short and long scales)
• Can the brand upsize or downsize its menu offering based on consumption patterns?
• When to offer discounts and when to squeeze the customer, while maintaining retention.

Geospatial Site-Selection Analysis Using Cosine Similarity

Experience 📲

Food Quality

• Does the food disintegrate easily. The balance of flavour compared to competition.

Food Selection (Cuisines and Categories)

• What is the cuisine served by the brand?
• What are the categories served in this cuisine and average price of category?
• What is the distribution of menu items per category?
• How does each menu item differ from what the competition offers?

Delivery Experience

• Is Unpacking the food a bad / rewarding experience
• Is the food prone to lose structure / form over delivery time
• Is there a food satisfactory level that changes over varying contours of delivery time

The Brand Portfolio: An Ecosystem of Brands 💼

The true power of cloud-based brands comes from the selection and management of a universe of brands, that can together serve a certain amount of demand.

Failover 🔄

• When a customer switches from a current brand to order on a new brand, how likely is it that they will choose a different brand served by one of the cloud kitchen’s own sites. I.e, how can we keep customers retained within the same brand universe where there is something for everyone?
• How can the company tactically recommend brand hops that always lead back to our brands and never keep the customers’ orders a few hops away from our selection

Coverage & Redundancy 🍰

• How can you reach maximum amount of coverage with n different kinds of categorical brands.

Is there a need to have more than one brand of a specific category? How can this demand be justified.

Cannibalization 🤺

• How can we keep our brands in a most stable and non-compete form to avoid cannibalization
• How do we build a brand portfolio where every brand complements the next in the overall selection, but is capable of sustaining its own orders independent of other brands (at a customer level and at an ingredients (SKU) level)

Here’s an example where Subway setup stores too close to each other that each store started eating off of the other’s revenue

Is Subway Taking A Bite Of The Sandwich From Both Ends?

Agility 🏎

• How quickly can you switch brands in and out of our own kitchens
• What is the turnaround time to put up a brand on a food aggregator and take them off. What are the related costs?
• How can you incentivize food aggregators (food delivery sites) to ease your experimentations?

Discovering Underserved Demand 📉

User Studies 🧪

Consider the following scenario: There exists a number of restaurants serving cuisine X in city A of country M. Assume that that the highest standard of cuisine X in city A is only half as good as the average standard of cuisine X in city B, perhaps in a different country N. Consumers in A have only ever known cuisine X as good as A is providing it, whereas a better version of X exists elsewhere. How can these gaps be identified? This repressed acceptance of X in A will not normally show up online in reviews / ratings.

“A lot of times, people don’t know what they want until you show it to them” ~ Steve Jobs

“How can we identify any underlying or inferred psychological indicators to serve, capture and even monopolize on cuisine service to underserved areas”?

Tailored Experience 🪡

When we reach sufficient order volumes and have the requisite customer information on their frequency of ordering, likes and dislikes of items, etc. Can we tailor a delivery experience that’s different for each customer, or even for a group of customers? Does this make sense to do cost-wise, and what is the gain.