Expansion needs to be well planned, and that’s exactly what McDonald’s Germany will be doing in the future with Targomo as its new partner. The location intelligence startup was able to convince the burger giant of its technology and the expertise it has gathered, and signed the global fast-food chain as a new customer. McDonald’s uses the platform TargomoLOOP to plan new restaurant locations in Germany.

“In the future, TargomoLOOP will make it much easier for us to evaluate new restaurant locations in terms of the relevant catchment area,” says Andreas Weber, Head of Real Estate at McDonald’s Germany. “The sound data basis and the intuitive interface were decisive factors in favor of a decision for TargomoLOOP.”

“We are proud to support McDonald’s in their German expansion,” said Niklas Gossel, Head of Enterprise Sales at Targomo. “Our analytics platform is designed to provide brands of large branch networks with a reliable data-driven decision-making basis. Locations contribute decisively to value creation but are also associated with high investments. We can use our technology to significantly reduce risks of bad decisions and strengthen the value creation of physical locations.”

McDonald’s serves around 1.6 million guests a day in around 1,450 restaurants in Germany, making it the market leader and largest employer in the system catering sector.

Interested people can register here to test TargomoLOOP for free.

Finding the right retail location doesn’t happen by chance. With so many factors to consider, from location scouting to customer analysis, an expansion manager can be the driving force to find the perfect place.

As retail experts, expansion managers have the industry contacts, the expertise and the technology to help store owners make an informed decision about where to put their business, whether it’s their first shop or your next. They can also take care of all the contract negotiations, providing retailers with a seamless turn-key solution that works around their business’ needs.

To bring more light to the work of an expansion manager and find out how they discover the best retail locations for their clients, we spoke to Samuel Vogel, owner of retail and real estate consultancy The Bird.

“I work in the field of real estate search,” explains Samuel. “I’m scouting for the right retail objects. Additionally, I examine a building’s technical specifications. I make a full location analysis. I also take care of branch network expansion. Everything really, up until the handover of the keys.”

Samuel Vogel advises clients such as shopping mall manager Unibail-Rodamco-Westfield and safe deposit box company Trisor. He has worked for several other retailers, including VIU Eyewear and bakery chain Zeit für Brot.

For Samuel, knowing his client’s customers is key to helping them achieve their retail goals. “My first question is always ‘who is your customer’,” he says. To give us an example, Samuel mentions one of his clients, Trisor, who rents out deposit boxes to people who want to secure their valuables away from home.

“This service is about the feeling of security, because the service is deposit boxes. Therefore, locations should be reachable 24 hours a day and have good parking. Furthermore, you want to know where there is a high density of potential customers, what their incomes are and what kind of products they buy. This helps me to find the optimal locations,” says Samuel. “The more questions you ask about customers, the closer you get to identifying the optimal locations, and the more you map their needs, the easier it is to find the right location.”

What the retailer sells and how it sells it is another important factor, whether that’s a product or a service. Some companies, like jewellers, benefit from a premium façade to convey the feeling of luxury, whereas a fitness chain would benefit from a solid structure that can handle heavy gym equipment, while a restaurant would seek an acceptable level of sound insulation and air ventilation. In the case of Trisor, its security deposit boxes are very heavy, so Samuel needed a building that could handle this weight. Again, with Trisor, the front of the building has to convey a feeling of security if a customer is to trust placing gold bars or other valuables in there.

“A visit to the places is a decisive factor,” explains Samuel. “I have to see the building and develop a feeling for the place. An image in a brochure can be perfect, but if something near the place doesn’t match the brand and product, that building could go off the list.”

The customers’ needs and the brand’s image determine the requirements that a location should meet. Samuel uses this list to request real estate options from his network of property brokers, and make a preselection of suitable locations. When retail space could be a good match, he will always have a visit in person.

“One of the important tasks of an expansion manager is to clearly explain to the agency or owner who might not be familiar with the product, what we are looking for,” says Samuel. “The landlord wants a reliable tenant with long-term plans. It is therefore vital to give the landlord a good introduction to the retailer’s business. If they don’t understand the product, they won’t accept me as a tenant.”

Once Samuel receives the real estate offers, he will rank them in terms of best fit for the retailer based on their needs and of course their customers’.

Knowledge is power, and when it comes to retail, accurate geo-referenced information and location data is crucial, from demographics of potential customers to footfall, as Samuel explains. “Firstly, I take a macro perspective: I look at the whole country and cities that could be suitable for my client. If it is a new business setting up locations, I still have an ‘empty’ map, so to speak. The logical step is to look at urban areas and big cities and see where the biggest potential is. Secondly, after I have identified specific locations, I examine these places with location data to see whether that site truly is a good match for the retailer.

“With Trisor, for example, I analyzed how many banks are located around a potential new site, and more specifically, how many deposit boxes are likely to be on offer there. We can’t get exact figures about the number, but I can make an analysis to estimate supply and demand. To do this, I used Targomo’s technology.”

With Targomo, Samuel was able to see how many banks and gold shops were located in this area so that he could make a supply and demand analysis to see if there is a need for this service. “The exciting thing about Targomo’s analytics platform is that it allows me to combine external data with internal data,” he says. “The more location-referenced data I have in the platform, TargomoLOOP the more accurate the analysis becomes. The more I know about my business, the better I can position myself in the market. TargomoLOOP allows you to scientifically compare different locations based on data and evidence.”

FREE TRIAL: Learn how technology supports retailers with site selection and expansion in TargomoLOOP.

Luukas, what exactly is Huuva’s business model?

Huuva offers a turn-key service for restaurants that want to expand and grow their business. We manage delivery and takeaway-only kitchens which are powered by our own proprietary software and technology. Our partner restaurants pay us a commission fee, already including delivery and rent costs.

This sounds like a shared kitchen?

Partly. In general, the restaurant brands get their own sub-kitchen inside the larger venue. What is being shared is the dispatching part. Huuva provides a shift manager who helps the restaurants in the packaging and dispatching the food to the delivery couriers. This operation is supported by our technology.

Compared to other ghost kitchen companies, what is different about Huuva?

We work to make the food delivery industry more enjoyable, convenient, and profitable for all parties involved: consumers, restaurants, and the delivery partners. For consumers, we bring top restaurants to their favorite food delivery platform. When ordering on a platform – whether it’s UberEats, FoodPanda, or whatever – they are able to combine menu items from different restaurants into a single delivery. You can think of this like having a food court in your pocket. Also, our customers can rely on hot meals and excellent quality, as the kitchens are optimized for delivery. From the restaurant perspective, our model is a low-risk and low-cost way to grow. Huuva doesn’t demand any multi-year rental agreements, we do not require any heavy upfront renovations or other investment cost, and we take care of regulations and bureaucracy that comes with setting up a new kitchen. With us, restaurants can start cooking and serving new neighborhoods in a matter of days. With respect to delivery companies, we are able to improve their KPIs and add quality restaurant offering to their platforms.

When you say that your kitchens are optimized for delivery, what does that mean precisely?

Optimization starts already with site selection. We choose locations that support high volume delivery operations, as they are frequently visited by couriers on bikes and scooters. We also plan a layout that minimizes the number of steps people have to make inside the kitchen. Besides the physical aspects of the kitchen, our software helps the different brands to schedule their cooking under one roof, so that multi-brand orders are getting done at the right time. This way, the cooks don’t have to do any tedious cross-organizing.

How are the restaurants reacting?

Restaurants faced enormous challenges during Covid-19 and the lockdowns. Additionally, we see that consumer demands and trends are changing quickly, especially among younger generations in large cities. Restaurant owners also see that change. As with startups, many restaurant brands don’t live more than 5 years, so you need to innovate constantly and ideally stand out from the crowd. We see that cloud kitchen approach can help the ambitious restaurant owner in tackling all of these challenges and support them with rapid concept development and testing alongside getting most value out of their existing brands.

As head of expansion at Huuva, how do you decide where to move your business next?

We have very ambitious growth plans over the upcoming years. Naturally, we started in our headquarter country Finland, but are already expanding abroad. We want to be present in the fast-paced markets with enough volume in the delivery business already today and an exciting, lively food and restaurant scene. One of the best parts of our job is to learn and immerse ourselves in neighborhoods, city and country-specific consumer trends and plan how Huuva would fit there.

Can you give an example of how these trends differ geographically?

It’s crucial for us to find out how common food delivery is in the specific markets. In the Nordics, a consumer orders a meal delivery twice per month on average. As a contrast, in Greece, the average number is 15. And in Central Europe it’s something in between. And the consumption profiles can differ quite a lot. For example, Indian is super popular in London and the whole UK, it is not as popular in all of the Central European countries. But there are also differences from city to city and even from neighborhood to neighborhood.

 What is your strategy when entering new markets?

We have two approaches: The first one is bringing top quality restaurants to underserved neighborhoods. We believe that the trendiest and hottest restaurants shouldn’t be the privilege of city centers or certain hot spots, so we want to bring them to areas where we see customer demand which is not currently met with the existing restaurant supply. The second approach is to work in these city centers and hotspots by helping the restaurants there to separate their delivery business from their brick-and-mortar kitchen. When the dining hall is full, brick-and-mortar kitchens quickly reach their capacity limit, making them miss out on potential revenue.

 How do you assess the potential of new sites?

We do our own scientific location analysis with Targomo’s platform TargomoLOOP. It provides us with all the relevant sociodemographic data that is key to understanding different possible delivery zones. Combined with its analytics functionalities, the tool allows us to explore geographic areas that are new to us and objectively compare locations. The second step is to then evaluate how the prospective venue supports our layout and operational requirements.

 Where do you see the delivery market moving within the next few years?

McKinsey & Company released a topical and on-point analysis on the food delivery market in September 2021 which highlights the fact that the restaurant industry requires new business and operating models now and in the future. The cloud kitchen space has already seen different models, from virtual restaurant brands, optimizing the use of existing restaurant kitchen space to simple kitchen rental models. At Huuva, we see that you need to consider the challenges of all parties involved in the restaurant and food delivery industry to drive real, sustainable change and growth. Nothing else is certain than change and we are committed at Huuva to delight consumers daily and enable restaurants and food delivery companies to do sustainable business.

Thank you for the interview!

If you want to use TargomoLOOP to expans your location network or create delivery zones, learn more about the location intelligence platform or directly book your demo!

They look beautiful and can be incredibly powerful when added to interactive map applications: Isochrones. Whether you want to show which areas are within walking distance of an office building or analyze the accessibility of a school by public transportation, isochrones offer a quick and easy solution. In essence, they show how far you can travel from a certain starting point, and often the individual travel time segments are also color-coded for better clarity. Here we give a brief introduction to the different ways isochrones can be used.

Targomo offers a wide range of geospatial tools that can be used to perform powerful location-based analysis, visualize detailed statistics, and solve complex logistical problems. However, here we will focus on one of the simplest, but most useful and therefore most popular APIs: the Isochrone API. The Isochrone API is used to visualize reachable areas from a source or a group of sources as polygons. 

So what is an Isochrone? Merriam-Webster defines an isochron(e) as “a line on a chart connecting points at which an event occurs simultaneously or which represents the same time or time difference.”. The shapes returned by the Isochrone API represent this by connecting all the furthest reachable points in the routing network from the given sources into a polygon. We refer to these as “reachability polygons” or “isochrones”. The definition is somewhat broader in our case, as we can visualize both time and travel distance, and also provide the ability to define multiple sources and aggregate the resulting isochrones. 

This can be used in a variety of useful applications, all of which fall into one of the 3 following scenarios: 

Whenever you want to intuitively visualize the areas that can be reached from a starting point within a certain travel time or distance, isochrones offer a quick, easy and relatively simple solution. You can implement them in such a way that users can choose between car, bike, e-bike, walking and public transport.  

The travel mode is crucial, because the results will vary greatly. This is due to the underlying routing, which represents realistic travel scenarios:  When traveling by train, your journey is largely determined by the location and number of bus and subway stops. When traveling by car, speed limits, the road network and traffic lights determine how far you can travel. 

Since realistic routing always takes into account obstacles such as lakes, rivers, bays, bridges, and mountains, isochrones provide a much more accurate representation of reachable areas than a radius ever could, regardless of the mode of transportation you choose.   

Once you start adding source locations, you can visualize and analyze much more complex reachability scenarios. One of the industries where this is becoming increasingly important is real estate search: On many real estate portals, people looking for apartments can create their individual search area based on several personal addresses (e.g., the workplace and child’s school) as well as the preferred mode of transportation. In a related use case, companies looking for new office buildings are starting to consider their employees’ addresses and commute times.  

It is easy to create polygons with more than one source, and you can define how these sources interact with each other. In terms of Targomo’s Isochrone API, we distinguish between three intersection modes: 

When displaying a group of locations to a user (such as a client, a business partner, or simply as part of a larger presentation), you may want to filter them by their accessibility from one or more starting locations. This is particularly useful for creating lists that take into account the user’s travel time constraints – for example, displaying workplaces that can be reached within 30 minutes by car, or vacation homes that are a 15-minute walk from the beach.   

With isochrones, it is easy to display only the places that are accessible according to individual travel preferences. The algorithm only needs to check  

By intersecting the point with isochrone polygons, you can determine which time-band the points are within (i.e. “< 10 minutes by car”) and sort them accordingly. This way, you can conveniently filter locations that are either 15, 30 or 45 minutes away from the source and help your users make sense of the data displayed.       

If you want to display or analyze the exact accessibility of places in terms of travel time, you should check out Targomo’s Travel Times API.   

Are you interested to dive deeper into isochrone creation? Our developer Gideon Cohen created a detailed step-by-step tutorial showing you how to create your own polygon maps with JavaScript. You could also jump directly into the code in the corresponding Github Repository.    

 “Find out how we used Isaac Newton’s Theory of Gravitation to answer a complex location based question: Why are so many Berlin parents struggling to find a Kindergarten place for their children? We found that Kindergartens throughout the city are unevenly distributed.  

Since 2018, every child in the German capital is entitled to free child care from their first birthday onward. Still, this new law has not solved the problem that has become very common among young parents. The threat of “Kita-Notstand” (“Kindergarten Emergency”) has recently made headlines. It threatens the attractiveness of Berlin for young parents and newcomers. And here at Targomo we have also felt this problem. After all, we are a young company and some of our co-workers just had children and some are struggling to find a place for their child. 

We were wondering: What is the story behind the Kita-Notstand? Is there maybe something we are missing here? As a first step we decided to look at the demand and supply of Kita places and it looks like there are enough places for children in the German capital. In total 139.856 kids are enrolled in Berlin’s Kitas today and the whole city offers 152.559 Kita places (destatis.de, kita-suche.de). 

So we asked our data magician Jacopo to further examine the problem from a location intelligence perspective. We assumed that the locations of Berlin’s Kitas were suboptimal. To test this idea he used a concept based on Newton’s law of gravitation particularly helpful in solving this puzzle.

Newton’s law of gravity says: “every mass attracts every other mass in the universe, and the gravitational force between two bodies is proportional to the product of their masses, and inversely proportional to the square of the distance between them.” In short: The gravitational force between two bodies gets smaller, the further apart they are. And larger bodies have a stronger gravitational force than smaller bodies.

Our model treats Kita locations as bodies that attract people. Larger Kitas attract more children. The longer people must travel to a Kita, the less they are attracted to it. With our gravitational model we can predict the number of kids applying to each Kita and thus their degree of overcapacity.

Very early in the process we decided to follow two strands of analysis. First, we wanted to see what would happen if parents chose the closest Kita to their homes? Second, what if all kids eligible for a Kita spot applied for it? How would this change the current picture?

But in order to get the full picture we needed to focus on two crucial categories: Age groups and location. A Kita in your neighborhood is not helpful if it only serves kids older than 3 years, but your child is 2-year old. A Kita with a free space for your child that is situated at the other end of the city is similarly unrealistic.

To understand the two factors better, let’s take a look at the following maps. The visualization of our gravitational analysis reveals the problems in the distribution of Kita places across the city. Black dots represent Kitas that are over capacity, meaning there are more Kids in its surroundings than the Kita can service. The size of the dots signifies the degree of overcapacity; how many children a Kita would have to accommodate to serve the demands of the parents living close by. Red dots equal 75% occupancy. Comparing these maps shows that the picture varies drastically between age groups.

Kids younger than 3, especially in the South-West of the city, face a dire situation. For kids older than 3, there are enough Kita spots but there is a slight mismatch. Kitas in the East are on average only 75% occupied while Kitas in the West and at the outskirts of the city cannot serve all the children in their proximity. We calculate that 52 % of Kitas serving children between 1 and 3 years old are too small for the demand in their direct proximity. Or to put it differently, 48% of Kitas do not have enough children living in their proximity, that means that parents have to travel to these Kitas to find a place for their children. This situation is less dire for older children but still 30% of Kitas are over capacity.

In Berlin, every child over the age of 1 is entitled to a place in a Kita. The question we were asking is: what if every family in Berlin claimed their right to a Kita place? The result is dramatic.  According to our census-based analysis, today about 35.000 kids in this age group who have a right to a Kita, don’t have access to a place.

The picture that emerges makes the problem crystal clear:

Children are the future. Providing equitable access to Kita benefits the entire community.

The reality on the ground probably resembles a state that’s somewhere in the middle of the two cases we considered. Giving easier access to Kitas in close vicinity to parents homes would incentivize more parents sending their children to a Kita. On the other hand, we know that not every parent will send their child to the Kita for a variety of reasons. But nobody wants a world where parents are forced to quit their jobs because the only available Kita is too far away from their home.

We understand that there are many more layers of complexity to any public policy decision that make running a city that much more complex. But we believe that visualizing the scope of the problem is incredibly powerful as a starting point for policy. With our Location Intelligence solutions, we give decision makers a platform to make decisions that are informed by data and state-of the art technology.

The study was presented by Jacopo Solari at the Urban Mobility Sympoisum – Karten, Daten, Geovisualisierung on 11. Oktober 2019 at CityLAB Berlin, organized by Technologiestiftung Berlin, supported by Senatskanzlei Berlin.

As we support the public sector, we participated in the Smart Country Convention Berlin last week. This forum for the exchange of strategic advice provided the opportunity to learn more from experts about the current state of affairs. While most cities are facing unique situations on a granular level, many places struggle with similar challenges. This year’s partnering country – and role model – was Lithuania. For them, becoming smart was the most pragmatic choice: “We’re not big, we’re not rich, so we have to be smarter”, their Foreign Minister Linas Antanas Linkevičius explained.

The impressive Baltic state has been so successful in fact, that its capital city Vilnius is overwhelmed by the incoming UK tech companies looking for new places to run their business. The city’s recipe for success consists of both top-down and bottom-up approaches. By creating the optimal legal frameworks and infrastructure conditions, the government paves the way for young companies to thrive with new business models.

Some of these companies are working towards new transport solutions, with good reason: “Regardless of size, all cities have mobility issues”, the mayor of Vilnius, Remigijus Šimašius said on stage. Once citizens establish routines, adjustments are difficult because of inherent resistance to changing habits (individual car ownership for example).

The mayor reminded us that the main beneficiaries of mobility are neither the municipalities nor the service providers, but the people. Mobility should not be an end in itself. Instead, mobility solutions should focus on the benefits they bring to their users.

The starting point of innovations should be micro-decisions of citizens as opposed to some overarching goal. But how to tackle all these challenges? One way, Vilnius decided, was a radical open data policy. Vilnius claims to be one of the first in Europe to create a public city data platform that provides open-source data for all businesses and citizens – “without excuses”.  As a consequence, since September 2017 it’s the home of Trafi, one of the most advanced mobility apps in the world. Trafi bundles all public mobility solutions, making it easy to navigate the city without a private car. This is not a one-way success story tough. Trafi provides the city administration with so much new data that it’s fundamentally changing traffic planning.

Other cities are also impressed: Recently, Trafi and the BVG (Berlin’s transit agency) launched Jelbi, to bring the same mobility experience to the citizens of the German capital.

But if igniting the spark is so easy, why are many city governments struggling to follow that open data approach? In our daily work with open data sources from all over the world, we encounter a wide range of problems. Two of the most significant ones: Coverage and quality.

For many of our customers, precise travel time analysis with public transport is key for answering their mobility questions. Integrating public transport data for Germany alone means using data sets from more than 80 federal transport authorities, many of which have to be contacted personally and may be reluctant to share their data. Consequently, it’s almost impossible to create a complete picture of Germany’s public transport landscape at this moment.

On the other hand, good intentions on their own aren’t enough, the correct execution is equally important. Large-scale data sets need homogeneous structures. Lina Bruns, a research assistant at Fraunhofer FOKUS, demonstrated at the Smart Country Convention how minor syntactical inconsistencies in data descriptions – such as putting a phone number as either 040/9595 or 040-9595 – can cause considerable additional effort, while incomplete data sets can quickly render all available information unusable for the majority of analytical purposes.

Bruns argued that agencies lack strategy, processes, tools, or resources to make sure their data solutions fulfill the necessary quality requirements. To help them, FOKUS released new guidelines this week for the improvement of data and meta-data quality, focusing especially on the necessary setup and structure of data.

While this is certainly a great help for those who already have an open data strategy, we hope that events like the Smart Country Convention convince more government agencies that they need one in the first place.

Already in the 1960s, business and marketing professor David Huff developed a mathematical model aimed at identifying the trading area surrounding a retail location, potentially freeing companies from conducting extensive surveys or field studies. He borrowed the main idea from Newton’s law of gravitation: the attraction between two bodies is proportional to their masses and inversely proportional to the squared distance between them. By treating retail locations as bodies with mass attracting customers, the law of gravitation can be effectively applied to the realm of branch network planning.

But why do we think it is worth taking a look at this decades-old idea again? While it never fell completely out of fashion, it didn’t experience a real breakthrough either. Today’s achievements in the science-driven discipline of location intelligence and the modern computational power allow for an effortless and effective application of the Gravitational Model that was unthinkable previously. For retail companies, new insights are becoming available at the push of a button, without having to consult geographers or releasing large budgets.

How does it work in practical terms? Well, it all starts with travel time calculations, because this provides a measure of the distance between a branch location and its potential customers. What’s even more interesting is that you can apply the Gravitational Model to your own branch network, effectively addressing cannibalization effects and your competitor’s network. The results display an objective representation of catchment areas with visiting probabilities for both entities:

From these basic steps, the application of the Gravitational Model can and must be fine-tuned in accordance with the individual business strategy. Retail companies with already existing branch networks can leverage their proprietary data on sales, transaction and customer numbers to test the first predictions of the Gravitational Model against real numbers. This allows for the optimization of multiplicative coefficients and exponents in its formula, resulting in the most realistic outputs for potential new locations.

Likewise, the sharper the image of one’s own target group, the more accurately statistical data can be leveraged, and the more efficiently transport and attractiveness variables can be determined. It makes a significant difference if your customers are teenagers traveling mostly by public transport looking for cheap groceries or seniors with cars and a demand for luxury goods. Once this framework fits, Gravitational Models will most likely be one of the tools of your choice when planning a new branch location.

Fascinating, isn’t it? If you’re interested in learning more about Gravitational Models for retail businesses, you can download our free Whitepaper. It’s where you can find a much more detailed description of the science behind these models and also see them in action in another real-life example.