Lisbon, September 19, 2023: Casafari, a leading European platform for real estate data, today announces the acquisition of Targomo, an expert in location intelligence. This move works to strengthen Casafari’s capabilities in the real estate and proptech market via the integration of comprehensive AI-based location analysis.  

Berlin-based Targomo has developed AI-based software which provides assessments of quality of locations for a range of purposes, working to model characteristics and forecast predicted performance. The intuitive SaaS tool enables companies to find the best locations for their needs, optimise entire networks, and increase overall profitability. Targomo offers a powerful API suite with access to comprehensive location-based data and reachability information. The transaction unlocks obvious synergies by enhancing Real Estate Search and Analysis with Location Intelligence on one side, and Powering Targomo LOOP with complete Real Estate Data on the other side.  

Targomo clients will benefit from direct access to CASAFARI’s proprietary real estate database, the most complete and accurate data on all properties in Europe. CASAFARI clients will benefit from complete real estate market information enriched by location intelligence. The agreement addresses a crucial pain point for customers in both the commercial and residential real estate sectors, providing businesses a competitive advantage via access to the most up-to-date and complete data on the market. The market insights enabled by real estate data and AI-powered location intelligence will accelerate deals for all participants of the real estate transaction. 

Targomo has received around 10 million euros in total with the last Series A round in 2020 to support the development of Targomo’s cutting-edge technology. As a part of the acquisition, Targomo’s existing team continues to work in key strategic roles from their Berlin headquarters. 

The agreement between Targomo and CASAFARI will deliver significant benefits for both companies, with Targomo expanding its customer base in CASAFARI’s core markets (Spain, Portugal, France, and Italy), while CASAFARI gaining access to Targomo’s enriched socio-demographic information and location intelligence for its more than 50,000 real estate customers in Southern Europe. 

CASAFARI and Targomo are committed to providing businesses with the tools they need to succeed in today’s data-driven world. By combining CASAFARI’s real estate expertise with Targomo’s location intelligence, this partnership will create a powerful solution that helps businesses and investors make informed decisions about where to invest and grow, whether they are in real estate, retail, finance, or any other sector that relies on data for success. 

About Targomo

Targomo helps retailers and restaurant brands make better and faster location decisions. It offers an analytics platform with 400+ high quality data sets for 25+ countries and develops customised Geo AI prediction models that enable businesses to forecast relevant sales metrics for any potential location – instantly. The location intelligence specialist has been awarded as one of Europe’s Top 5 Deep Tech scaleups and serves customers in more than 20 countries, including McDonald’s Germany, RSG (McFit, Gold’s Gym) and Søstrene Grene.

About Casafari

CASAFARI is a leading real estate network that connects 50,000 professionals through its innovative data and collaboration tools. With proprietary technology to index, aggregate, and analyse 310 million listings from 30,000 sources, CASAFARI builds property history, property sourcing search, CMA, market analytics, market reports, APIs and CRM to serve such clients as Cerberus, Kronos, Vanguard, Masteos, Casavo, Sotheby’s International Realty, Coldwell Banker, Century 21, Savills, JLL, Engel & Völkers, Keller Williams among others on the real estate market.

Note: CASAFARI is an exclusively B2B real estate data platform,  serving real estate professionals and not a real estate agency. 

When expanding a business, especially opening a new store in a new region, the biggest questions to be answered is whether the store can be expected to perform well and how much revenue it could potentially generate. Revenue prediction is very important in supporting site selection and decision making for ensuring a successful expansion, and there are several approaches that aim to deliver accurate forecasts.

Two machine learning algorithms that are commonly used to predict revenue are Random Forest and XGBoost. In this revenue prediction case study we compare the results from both these popular models with the results generated by Targomo’s Geo AI model. Geo AI is a location-based model, aiming for a better understanding and evaluation of the characteristics of the shop location.

The basis for this case study is the publicly available data for liquor sales data in the US-state Iowa. For the prediction, we selected the brand Hy-Vee and treated liquor sales of other brands as competitor data.

Who is Hy-Vee? Hy-Vee is an employee-owned chain of supermarkets, mostly located in the Midwestern and Southern US. Its headquarters is in West Des Moines, Iowa. Hy-Vee has over 285 locations in 8 states in the US, including among others Iowa, Illinois, Missouri. As all data of liquor sales in Iowa, Hy-Vee’s data was publicly available.

The three models Random Forest, XGBoost, and Targomo’s Geo AI were used for the revenue prediction. The Random Forest and XGBoost models are often applied in prediction as their advantage is their efficiency in dealing with high dimensional data. In addition, both random forest and XGBoost are decision-tree-based models and easy to work with – there’s no need to normalize data and Python/R packages are readily available to use. It can create a relative robust model even with outliers and missing values. The drawbacks, however, are also obvious: Potential over fitting issues and low interpretability.

Geo AI is a locational-based method. The foundations of the model were developed as part of a research project conducted by Targomo in collaboration with the Hasso-Plattner-Institut and funded by the Deutsches Zentrum für Luft- und Raumfahrt.

One major component of Geo AI is a gravitational model. It follows the idea of Newton’s law of gravitation applying the concept to market models. According to Newton’s law of gravitation, high masses (attractiveness in market models) and short distance lead to strong attraction. The attractiveness is measured by the store’s characteristics and environment. These factors could be characteristics like store size, parking space, product range, or location factors like complementary shops, competitors or other points of interest close by.

In our model, these factors are called attraction strength. The distance, in Geo AI, is calculated based on travel time with catchment areas defined by travel time and travel mode including car, bike, public transit, and walking. A travel-time-based catchment area provides a more accurate and realistic assessment of population than more naïve distance-based catchments.

To begin, Targomo’s data team first needed to compile data from various sources to calculate the general demand and attractiveness of stores:

Data cleaning is one of the first and most important steps in modelling as cleaned data is the foundation of a correct model and its output. The process of data cleaning involves detecting inaccurate, incomplete, and duplicated records, and then fixing or removing them.

Furthermore, stores with extreme high or low annual sales have also been carefully reviewed, noting that some were beverage and liquor retailers. Since these stores cater to different customer groups – namely other stores – these outliers were removed from the data set. Casinos, hotels, inns, and distilleries were also removed.

In countless iterations, the Geo AI engine is fed with data to continuously learn which data composition represents Hy-Vee’s store performance.

The training consists of two main components:

There are two relevant values measured during the learning process that indicate the prediction quality: The training error and the testing error.

When it comes to prediction results, the testing error can be considered to be more meaningful because it gives us an idea of how well the model will perform on new, unseen data – like the address of a potential new location for a shop opening. A low training error is a precondition for, but alone not a sufficient indicator of a successful project.

In our revenue prediction projects we usually go through two phases during the model training:

The table compares the model accuracy and detected success drivers of three liquor sales prediction models for Hy-Vee stores. The training error of Geo AI model is 16% and the testing error is 19.8%, while the testing errors of Random Forest model and XGBoost model are 38.1% and 30% respectively. Compared with the two commonly used models, Geo AI yields the highest model accuracy.

The identified attributes in the Random Forest and XGBoost models are rather limited and are restricted to the number of items, the number of visitors, and one or two socio-demographic attributes.

In contrast, the Geo AI model consists of a wide range of features, including among others store features (item count, shop type), surrounding environment (footfall at the location), and socio-demographic characteristics of potential customers. Item count, indicating the variety of liquors offered in store, shop type, and shopping footfall in the close neighbourhood are used to measure store attractivity in the gravitational model.

The scatter plot of the Geo AI output: The x-axis is the actual liquor sales, and the y-axis is the predicted liquor sales. The dots are nicely distributed along the diagonal line, which indicates a good prediction performance of Targomo’s Geo AI.

Income is a major driver of the liquor sales in Hy-Vee shops. 25.8% of the sales are from households with income less than 40,000$, and 14.5% are from households with incomes greater than 150,000$. Many studies have found that low-income individuals are at higher risk of engaging in heavy and hazardous drinking, and higher income is associated with a higher frequency of light drinking.

In addition to income, certain occupations have also a positive influence on liquor sales in Hy-Vee stores in Iowa. The results show that people working in construction, wholesale trade, and art industries as well as those who are self-employed are strong drivers. According to Statista, the top 5 high alcohol consumption (15 or more alcoholic drinks per week) occupations in the US 2016 are construction or mining, installation or repair, farming, fishing, or forestry, manufacturing or production, and business owner.

Among them, construction is one of the identified drivers in the model. The other identified occupations do also correlate strongly with the groups identified by our Geo AI, e.g. installation or repair, farming, fishing/forestry, manufacturing/production are often self-employed, as business owners are as well. So we can see that some drivers from Geo AI align with empirical results from research studies.

Integrated in the platform TargomoLOOP, the Geo AI model delivers immediate prediction result for any potential address.

Insight #1 Good estimate of attraction strength is key 

Attraction strength indicates how attractive our stores are and how attractive/competitive our competitors are. A good estimate of attraction strength can better estimate the market size and potential customers, and thus predict the revenue more accurately. In estimating the attraction strength, information about the store is key, for example, store size, sales area, store opening hours. In this case study, due to the limitation of public data, the information we have is not enough to capture the whole picture of the store attractiveness, such as the missing information about the store size. To circumvent this limitation, the building roof top data is used instead, but it is not possible to obtain this information for all stores.

Insight #2 An attractive shop location is defined by its surrounding business environment AND access to its target group

When selecting a new location to open a store, investigating the surrounding businesses is critical. What products/services do they offer? What is their vibe? Are they competitive or complementary? However, the geographic distribution of target demographics can be missed in the decision-making process. It does not matter how well suited a location is to a store if the target customer group are unable or unwilling to travel to it.

Using Random Forest and XGBoost models, it is hard to control what variables are used in the decision tree. In this case study, these two models mostly focused on the nearby environment while giving only minor consideration to the access to target demographics. The Geo AI model both put a greater emphasis on demographics, and took into account a wider array of demographic and environmental factors. We have abundant data to measure various aspects of demographic structure to support the model and future decision making.

Insight #3 Expert knowledge could be a double-edged sword in key driver analysis. 

When selecting variables in the driver analysis, expert knowledge can provide insights on what key drivers can be expected based on experience or existing literature. At the same time, relying too much on expert knowledge and excluding data at the beginning that we thought is unrelated can fall into a bias trap.

Instead, by letting the model and data speak, we can gain new or unseen insights. We may find some variable we thought could be important have little to no significance, and vice versa. What we should then do is to investigate it, figure out why and explain it. Our assumptions should not interfere with the model.

The feature selection mechanism in Geo AI ensures the model suffers less from overfitting even with large number of features as the input.

This case study tests and compares the performance of three models, Random Forest, XGBoost, and Geo AI, as revenue prediction methods, using liquor sales data from Iowa, USA. Geo AI has the best performance among all 3 models in predicting liquor sales for Hy-Vee, having the least overfitting, the lowest testing error, and the important features in the model are more comprehensive.

Compared with Random Forest and XGBoost models, Geo AI takes greater consideration of shop feature factor variables, shop surrounding environment, local demographic structure, and mobility data making the model more reliable. In addition, Targomo’s Geo AI, a locational-based model, offers better understanding and evaluation of the characteristics of the shop location. It is possible to track the model and retrieve the prediction of each store to identify key revenue drivers. Due to its rate of overfitting and testing error, Geo AI could better predict the revenue of a new location and analyse revenue drivers.

Liquor sales prediction is one example of a Geo AI model. Geo AI can be applied in various industries, including among others retail, delivery services, food industry, and health care. Are you interested to learn more about Targomo’s Geo AI forecasting for retailers and restaurant brands, and see dynamic analytics of the Hy-Vee Geo AI model? Book a demo here

Authors: Yue Luo (Spatial Data Scientist), Gideon Cohen (Software Engineer), Luisa Sieveking (Marketing & Communications)

The Geo AI model was developed by Targomo’s Data Science Team that is dedicated to evaluate which location factors influence the business success of brands and companies, and to what extend they contribute to revenue.

In previous articles we have already discussed what to consider when choosing a new location for a branch and which pitfalls to avoid. One key aspect in determining the success of a potential new shop is the environment. Do points of interest that attract visitors exist- if so, what kind of visitors? What other shops can be found here, are there competitors or complementary shops?

It’s no coincidence that retail activity is usually clustered in city centers and secondary shopping areas. On the one hand, locating close to competitors leads to the so called “competition effect”, meaning higher price competition and hence lower revenues. On the other hand, competitor and complementary shops nearby increases the overall attractiveness of the area and therefore capture more consumers. This is known as the “agglomeration effect”.  

The predominance of one effect over the other mostly depends on the type of business and especially on the possibility to differentiate the product that the business offers. For instance, gas stations offer a standard product where the price competition plays the most important role. Clothing shops offer instead a different range of products and can benefit from the presence of similar shops nearby. 

This is not surprising, those who want to buy clothes will find a wider choice in a shopping area with several clothing shops. The other shops act as traffic magnets, attracting more potential customers who might not have visited the store otherwise. This makes clothing brands the prime example of retailers that benefit from the proximity of competitors. They not only help increase footfall in the area, they even attract the right clientele who are looking to buy. 

This explains why the concept of “business clusters”, or more specifically, “shop clusters”, is extremely relevant in the retail sector. Shop clusters can be defined as a group of shops with similar or different characteristics that occur closely together.  We at Targomo use four attributes to distinguish between different types of clusters: 

The cluster size is determined by the number of shops that constitute the cluster. Usually, the more shops the cluster can offer, the more visitors it is expected to attract. 

How many different categories of shops constitute the cluster? How many different categories of shops make up the cluster? Is it a fashion strip, do furniture outlets congregate here? Or is the cluster perhaps defined by complementary shops such as a pharmacy, supermarket and drugstore? Industry clusters in particular can have a very positive effect on shops in the same category. 

What is the average price of the shops that form the cluster? As the saying goes, ‘Birds of a feather flock together’, and this can also apply to retail if they attract the same target groups. That’s why you usually see classy designer stores next to each other, but a jeweller only rarely next to a discounter. 

Do most well-known and popular brands compose the cluster, or independent shops and alternative designer labels? Here, too, like-minded shops often come together to define the character of a shopping street.

Investigating the impact of different types of clusters provides retailers with highly valuable insights into which characteristics drive their business success and what they should look for when selecting their next site. In fact, it is not only a matter of the quantity of footfall, but more importantly about the quality of the footfall around a store.   

To increase its profits, a business should locate itself in an area that attracts the right people. In other words, it should locate itself as close as possible to shops from which it could profit because they have the same target audience.  

At Targomo, our ‘success driver’ analysis looks at whether, and to what extent, being part of a particular shop cluster has a positive effect on sales. If we can determine correlations, retailers not only know which location attributes they should look for when opening a new site, we can also determine the “weight” (e.g. importance) of these attributes. The weighted attributes can be integrated into our sophisticated Geo AI model – a prediction model developed to forecast relevant sales metrics for any potential site.  

Our approach consists of three main steps: 

The Geo AI prediction model then defines exactly which characteristics lead a location to success and to what extent the respective location characteristics are contributing to success. To give an example: The results of a recent case study showed us that for a home furnishing retailer, price was the most important cluster characteristic. The brand particularly benefited from having many high-priced shops nearby. With this in mind, the brand has additional criteria when looking for a profitable area to open a new shop, specifically which are its best neighbors and hence the kind of stores that help making its business more successful.  

Author: Marta Fattorel is Spatial Data Scientist at Targomo. After graduating in Data Science at the University of Trento, she joined Targomo in Berlin to research the contribution of the various location factors to the turnover of the respective brands. 

The old adage “by failing to prepare, you are preparing to fail” has never been more apt than when choosing your next retail location. With so many factors to consider, as well as the pandemic creating shifts in how people shop, and a host of other trends to take into consideration, doing your research is vital.

Start your research journey here, with the help of this list of mistakes to avoid when choosing a new retail location.

While it makes great economic sense to expand your bricks-and-mortar business into multiple locations, it’s important you watch out for ‘retail cannibalisation’.

In the first quarter of 2018, Starbucks – the popular coffee chain with a store on every corner – was suffering from market saturation. With so many stores available, and a burgeoning competitor market, its stock plummeted 11.38% at a time when the overall market was up 4.1%. As a result, it started shutting stores in the United States.

Of course, healthy competition is great for any business, but in the retail industry, cannibalisation occurs when branches of the same chain that are near each other end up competing with one another for the same business.

The same is also true of competitor businesses. If too many similar businesses are located in the same catchment area, customer loyalty and preference is going to favour one business over another. An oversupply of locations also leads to higher operational costs, as Starbucks found to its chagrin.

So before you decide for a location, check how your branches influence each other. A good technology solution lets users immediately see whether the new store would “steal” potential customers from existing shops in the same area and how many. They can also see how many customers they could potentially win from competing shops nearby, and how a competitor’s new location might impact the catchment area of their retail stores.

In 1920, American mathematician Harold Hotelling came up with a theory called Hotelling’s Model of Spatial Competition. His model shows that when competing for locations, every business wants the “central point” as it is the most strategic spot to be as close to as many customers as possible. But because every business has ultimately the same intention, stores become clustered around the same location and end up competing with one another.

Putting that theory into practice, Marc Smookler, a United States retail expert, conducted a study in Austin Texas in 2015. He concluded that CVS and Walgreens pharmacies were, on average, only 1.5kms apart, and Walmart and HEB (a grocery chain) were 1km apart.

So sometimes you’re drawn to an area because that’s where the market is. But you should also know who your competitors are, what they specialise in, what their USPs are and how your business is similar or different. And most of all: where they are located. Because this gives you the chance to identify “whitespots” with the highest market potential.  

We’re all familiar with the concept of the strip mall (in Germany they’re also known as Fachmarktzentren): an out-of-town shopping area characterised by a centralised parking area and a parade of shops or big box stores clustered together. Over time, and even during the pandemic, these areas have out-performed city centre locations.

According to JPMorgan, “The pandemic had a major impact on retailers in city centres heavily reliant on office workers and tourism. But service-oriented strip mall retailers in densely populated urban and suburban neighbourhoods performed well throughout 2020 and 2021. These properties have consistently performed well regardless of market conditions.”

This successful recipe stems from considering other nearby businesses not as potential competitors, but as opportunities to draw the right target market to the location.

Complementary businesses offer products that relate to or complement yours:  a pharmacy near a doctor’s office, a bar near a restaurant near a hotel, a sports shop near a gym, a pet supply store near a veterinarian, a cafe next to a bakery.

We recently interviewed a retail expansion manager who said “for my client who rents out deposit boxes to store people’s valuables, I analyzed how many banks are located around a potential new site”.

So if you are looking for the ideal location for your business, you should also analyse which other shops in the area complement your offer and are beneficial to your business.

Understanding how people travel to your store is vital. Are you in the middle of a city where parking is at a premium? Are you out of town and far away from public transport? Do customers have to pay to park near your store? Do you sell large, bulky items that require a car?

It’s important not to overestimate how many people will travel to your store by car, and consequently underestimate how many will use public transport or other forms of mobility.

A recent study in Berlin, Germany, discovered that retailers often make the mistake of overestimating the amount of people who travel by car when they go shopping.

The report, which surveyed 145 traders about how they thought customers got to their shops, and interviewed 2,019 shoppers on two shopping streets in Berlin, discovered that shop owners overestimated how far customers travel to visit their businesses.

“Over half (51.2%) of shoppers lived less than 1 kilometre from the shopping street. In contrast, traders on average estimated that only 12.6% of customers live within this distance.” The results appear to show a big discrepancy between the perception of traders about customers’ mobility patterns and the actual reality.

Furthermore, the study appeared to show that traders often misjudged how customers travelled to their shops, underestimating public transport and overestimating car use.

“While only 6.6% of shoppers travelled to the streets by car, on average traders estimated 21.6% of their customers use this mode; a discrepancy of 15%,” says the report. “Further they underestimate transit, pedestrian, and bicycle travel by 8.1%, 6.2% and 3% respectively.”

Before deciding on a location, analyse it for accessibility, considering different modes of transportation such as walking, biking, driving, and public transportation.

Foot traffic is one of the hallmarks of retail. At its most basic, it means the number of people walking past. It’s one of the key metrics for retailers, as the pedestrian activity near a shop influences sales volumes and increases the chance of spontaneous buying or “impulse purchases”.

So if you’re deciding on a location for your new business and benefit from spontaneous purchases or visits, you should take a closer look at this figure. But be careful: often a general figure is not enough. You should also consider whether there are fluctuations throughout the day and how foot traffic behaves on weekends compared to weekdays.

Furthermore, you should also check what causes the traffic. Are vehicle data included, or are only pedestrians counted? Just because a location has high frequency doesn’t mean that people have the time for a spontaneous visit to your store. Therefore, Foot Traffic should only count visitors who spend at least a certain amount of time in the area, not just passing through.

But while foot traffic is important, it’s not the only consideration. During the pandemic lockdowns, foot traffic in some areas went down significantly as people preferred to shop close to their homes because of travel restrictions. Suddenly, hyper-local shopping became popular.

Because of this, it’s crucial to understand the demographics nearby, what the average household size is and how many children live there children, for instance. If you really understand the catchment area, you’ll discover how many potential customers can reach your location. This can give you a good understanding of whether the site is attractive or not and whether it will appeal to your target customers.

As a business owner, you probably have a fair understanding of who your target customer is. But defining who they are and locating them is not so easy. What problem is your business trying to solve for them? What is the benefit of your product? Do you serve a particular niche market, and do you have enough potential customers in your catchment area? What other companies nearby offer the same or a similar product or service as you? According to Marketing Donut, “successful marketing relies on understanding your target market. Who are you selling to? Why should they buy your product? What do they stand to gain?”

In a blog on WordStream, the writer Dan Shewan, said: “If you run a small business, maybe you have an idea of your target market. However, a vague idea is not enough to compete in today’s ruthless business environment. Without detailed knowledge of your target market, you could be losing business to your competitors or missing out on opportunities to increase sales.”

Ultimately, the right location is the place where your target customer visits or lives. With powerful location intelligence, you can unlock the door to compelling insights that could be the difference between business success or failure. With TargomoLOOP, you can analyse data such as population age groups, household size, spending power and lots more to help you understand how you can find and reach your target group.

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.

Thanks to the global pandemic, the business of micro-fulfillment is booming. Here, we take an in-depth look at the trend, how it works, how it expanded recently and if it’s possible for those living outside of urban metropolis areas. 

Short answer? According to DHL, ‘Think of it as AirBNB for logistics.’ Long answer? It’s the accelerated last-mile delivery of goods, thanks to the efficient positioning of small-scale distribution warehouses that utilize location intelligence systems to fulfill orders at a fast pace. Shorter distances between retailer and consumer ensure online businesses can operate in a much more productive (and profitable) manner. 

Over the last couple of years, this particular business model has grown exponentially. Take grocery delivery for example—during the pandemic, newcomers like Gorillas, Weezy, Flink and Gopuff gained serious market share, and are now expanding across Europe and the United States. And they’re picking up traction in more ways than one. This year alone, US-based Gopuff acquired British-born Fancy, and Berlin-based Gorillas was valued at over $1 billion. As a result, we expect micro-fulfillment to grow rapidly across other continents as well. 

If you’re wondering how these companies make money, it’s quite simple. Let’s compare it to the traditional grocery store: While the supermarket needs a larger space in an area that is also visible, a micro fulfillment center or dark store can be as small as 300sqm because the product range is limited to mostly premium brands. This leads to lower rents on average for the quick commerce players. 

Of course, there is other overhead to be considered, like riders, pickers and the overall number of dark stores that are needed to serve a city. However, if all these elements are well planned, there is a very high probability to run the business net positive. 

It’s been proven that micro-fulfillment can work seamlessly in urban settings with high population density. That’s where the micro-fulfillment centers can get close access to consumers and retailers can offer faster delivery at a low cost. The key is to actually ensure that a critical number of potential customers can be reached within the promised delivery time. Tools like Targomo Loop help to analyze the areas around the micro-fulfillment centers, and to create an overview of the catchment areas.  

The analyses take demographic data such as population density, household size, purchasing power, and consumer profiles into account, which allow the delivery services to precisely calculate their potential markets. 

By combining location and demographic data, Targomo Loop can visualize not only how far an e-bike can travel from a central micro-fulfillment center in 10 minutes, but how many people it can serve. However, retailers may need to specifically look for certain target groups and identify the cities and regions with the highest target group density. 

In this example, we are targeting people with age between 20 and 50 years and with medium to high income in the city of Berlin. 

As you can see, the people reached by that micro-fulfillment center are 109,138. 

A sufficient number of orders and the size of the shopping cart are crucial to the success of the business model. This is the reason why the service mainly benefits residents of a region with a very high population density. But would this system be successful and profitable in a more rural, less densely populated area? Let’s find another place, far away from the city, where the same target group density can be guaranteed. 

As expected, this isn’t an easy feat. With Targomo Loop we checked the town of Brandenburg an der Havel. The business model of Berlin city would not work, as just 11,208 people could be reached within a 10-minute e-bike ride. If someone wanted to roll out the quick commerce business in this area as well, they would need to adjust the conditions. From a location intelligence point of view, it would be possible if: 1) you swap e-bikes for cars as a mode of transportation, 2) increase the delivery window from 10 to 25 minutes, and 3) extend the target group to other demographics. 

Let’s take a look at the numbers:  

With these adjustments, it’s necessary to rethink the entire model. Swapping e-bikes for cars would significantly increase the overhead for the start-ups. Furthermore, an increase in delivery time from 10 to 25 minutes will lead to riders delivering fewer orders a day. On the other hand, with cars covering larger distances, provided consumers order around the same time, more households could potentially be serviced on the same trip, saving on delivery times and labor costs. 

Our take? The last-mile revolution seems to find its most fertile ground in big urban areas and may not come to rural residents anytime soon. 

What’s clear is that, to work, the micro-fulfillment industry needs precise location analysis. Businesses need to integrate their own data to analyze and predict order volumes. They need to fully customize their catchment areas: select means of transportation, speed and travel time. They need to calculate how many households they will be able to reach from a specific location. They need to anticipate which products will be in high demand in specific areas. And if they have a location network, they need to learn where they should open new centers to serve as many customers as possible.  

SERIES: RETAIL TRENDS THAT ACCELERATED DURING THE PANDEMIC

Hyperlocal shopping and services have gotten a tremendous boost during the pandemic. Ever more people are favoring to visit neighborhood stores and order products for immediate delivery. In this third article of our retail series we’ll discuss why hyperlocal has become more important, and how retailers are responding to this trend.

The term hyperlocal has been around for some time, but it has gained a lot of traction recently. In the context of retail, hyperlocal shopping refers to consumers buying products in their neighborhood. The coronavirus outbreak has supported hyperlocal shopping in two ways. Firstly, the pandemic has limited the mobility of citizens, forcing many of us to work from home. Even as lockdown measures have been eased or lifted in large parts of the US and Europe home office remains a reality for many. When life returns to a new normal, many employees plan to keep working from home, at least part time. Moreover, Businesses are considering to implement hybrid models. This reinforces the importance of local offerings around peoples’ s homes like shops, cafes and restaurants.

Secondly, the pandemic has changed the attitude towards local shopping. People see an increased value in local offerings and they have come to realize that their local spending keeps neighborhood stores alive. An international study by Adyen and Opinium found that two-thirds of those surveyed want to continue buying from local retailers to help them stay open, even after the pandemic.

Sales figures clearly point to increased demand in local stores. Last year in the UK, when citizens endured two lockdowns, total consumer spending dropped 7.1 percent versus 2019. However, local shops saw revenue growth of 29 percent, according to Barclaycard payments data.

Local purchases expanded even stronger this year. UK shoppers spent an extra 69 percent at local food and drink retailers, such as butchers, bakeries and convenience stores, in May 2021 compared with May 2020, according to Barclaycard figures. This happened even as shopping restrictions had been lifted in the country a month earlier, and outpaced overall consumer spending growth of 7 percent in May.

For consumers, local shops offer clear benefits. Neighborhood stores are easy to reach, and take less time to enter and exit compared with their big-box peers. Similar to on-demand delivery of groceries or meals, these hyperlocal shops offer instant fulfillment, and therefore convenience. These benefits can outweigh the disadvantage of running a smaller product assortment, which is often the case for local stores.

Retailers are aware of the need for hyperlocal shopping and are resetting priorities. Even before the pandemic hit, businesses such as supermarket chain Target, sports brand Nike and fashion store Nordstrom had been opening smaller, local shops. This trend has accelerated. A few months ago, French supermarket chain Système U announced plans to open almost 400 new locations of its U Express convenience stores by 2024, up from 800 currently. Ikea, known for its car-friendly locations on the city’s outskirts, is opening 50 new small-format stores in urban areas around the world, including Queens, New York City. Customizing products and services to local tastes and reaching new target groups are among the prime reasons for Ikea to open these smaller, local shops.

In a research note, consultancy BCG sums it up as follows: “Where consumers value ultra convenience, retailers must evolve their physical footprint from large stores spread over wide areas to a dense network of smaller stores hyperclose to the point of consumption.”

Hyperlocal also refers to services and products fulfilled and delivered locally. Often, people order them online, such as groceries and meals, and receive them instantly. This is also known as hyperlocal e-commerce, quick commerce, or on-demand delivery. We’ve discussed examples in our previous articles about grocery delivery and ghost kitchens.

Home services such as appliance repairs, cleaning and personal grooming can also be classified as hyperlocal. Companies offering these services typically hire or contract a team of employees to work in specific geographic areas, where demand is expected to be high. The hyperlocal services market, which also includes food and grocery ordering, and logistics services providers, is expected to more than double to reach $3.6 trillion by 2027, according to Allied Market Research.

The higher importance of local shopping represents a big shift for retailers. Traditionally, footfall has been the key metric to determine where to open new branches, while neighborhood demographics mattered little. But when local shoppers are a retailer’s focus, data on residents become crucial in addition to the footfall in the micro-environment. Location selection needs to take this into account.

Furthermore, it has an effect on the product assortment. As Ikea’s example shows, knowing the local makeup of neighborhoods and residents’ preferences is essential to create the right offering in each area. The hyperlocal trend is closely linked to catering to local tastes. The focus on neighborhood communities allows retailers to trim down their product mix, and only offer what is in demand in a specific area.

Combined with people’s changed consumption and working behavior following the pandemic, hyperlocal shopping and service delivery may continue to be an important trend for many retailers in the future.

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SERIES: RETAIL TRENDS THAT ACCELERATED DURING THE PANDEMIC

The pandemic has forced restaurants and bars in Europe to close their doors for months on end. Only recently have they been allowed to serve customers again. The only way to keep cooking and serving during the lockdown was to deliver meals to people’s home. And this has indeed happened exponentially, driving the sales of Just Eat Takeaway.com, Delivery Hero, Grubhub and similar order and delivery services across the globe.

In this second article of our retail series we’ll have look at the meal delivery industry, which has propelled the existence of so-called ghost kitchens, or dark restaurants. These cater to people ordering online, have no seats, and often combine different types of cuisine under one roof.

In the first article we’ve discussed how convenience has become front and center for many consumers. This was already a big trend before the pandemic, but the coronavirus outbreak has accelerated it. People not only want to buy books and electronic goods online and have them delivered, but also food. For many during the pandemic, the only way to get to get a meal without cooking it yourself was to have it delivered. And they did in droves.

Figures from the big players proof the point: DeliveryHero, which operates in 50 countries around the globe, saw the number of orders jump by 96% to 1.3 billion orders in 2020 (this figure also includes orders for grocery and non-food items). The orders were worth €12.4 billion ($15 billion). Other delivery companies, such as Glovo, DoorDash and Uber Eats, have reported similar double-digit growth figures.

Exact figures about the global industry vary widely, but according to a study by Research and Markets worldwide sales are expected to reach $127 billion this year, up 10% from 2020, and grow further to $192 billion in 2025. Interestingly, the study says that “cost of supply chain and logistics will be the key restraint for the online food delivery services market.” Businesses could even “lose up to 26% of their profit if they fail to upgrade their logistics system to ensure on-time delivery”.

The boom of this meal delivery industry has created a new type of restaurant, so-called ghost kitchens. They’re also known as dark, cloud, or digital-only restaurants. These establishments usually only cook to deliver. They typically have no seats and no waiters. They take orders online (some perhaps also by phone). They may also combine different types of cuisine under one roof, for instance pizza and pasta in one part of the kitchen, and burgers in another part.

You usually can’t find ghost restaurants in shopping malls or Class A locations, and may not even recognize them from the outside. Instead, they are located in residential neighborhoods or on parking lots close to their customers. The proximity to customers is crucial: This helps the kitchens quickly bring the meals to the home or office from where the orders are placed.

Virtual restaurants typically only exist in the apps of meal delivery companies, such as FoodPanda, Deliveroo or Wolt. Some are owned and operated by real restaurants, while others are set up by delivery companies themselves. For example, Deliveroo operates three dozen sites, which house 220 ghost kitchens on four continents. Some of them are housed in shipping containers on parking lots, or in warehouses. Delivery Hero’s brand FoodPanda operates its own ghost kitchens in Asia and also plans to open them in Germany.

Some of these delivery companies rent out kitchen space to existing restaurants, which can then expand their reach into new neighborhoods. The establishments benefit from new orders, which their physical locations could not fulfill. There are also companies specialized in setting up these ghost kitchen sites and renting them out, such as Reef and Cloud Kitchen in the US.

There are three clear advantages in operating ghost kitchens, says Thomas Primus, CEO and Co-Founder of FoodNotify, which helps restaurants digitize operations, such as real-time supply management and digital recipe management.

“In many cases, there are additional cost savings to be had by sharing space and locations. Additionally, ghost kitchens don’t need to spend money on the interior, such as tables and seats, staff waiters and printed menus. Secondly, there are many kitchens whose utilization can be increased by serving customers online, for instance by offering lunches, or a completely new line of products, for example, wraps and burritos in addition to pizzas. Thirdly, ghost kitchens offer a low-cost option to expand into new neighborhoods or cities, and reach new customers,” explains Thomas.

He thinks that especially smaller restaurant owner could benefit from the concept, because they don’t have to invest heavily into a new, full-fledged establishment if they want to expand. They can simply set up a kitchen, or rent space in a ghost outlet to reach new customers. People seeking to launch a new restaurant could also benefit, because they can first test their idea as a virtual kitchen and see what runs well or what does not.

Thomas finds it difficult to give predictions for the ghost kitchen market, but he does believe it will continue to grow. In Europe, the market is still relatively small and considered a niche market, but in the US and China it is already reaching size: More than 1,500 ghost restaurants exist in the United States, and more than 7,500 in China. In her Food Report 2021, the Austrian food scientist and expert Hanni Rützler expects the trend of ghost kitchens to gather pace, even when the pandemic has been overcome.

The fact that the delivery companies themselves continue to set up new ghost kitchens underlines the market’s potential. Deliveroo itself plans to double the number of ghost kitchen sites to 64 this year, from which hundreds of different cuisines will operate. Supermarket chain Walmart has teamed up with the company Ghost Kitchen Brands to set up dozens of dark kitchens in Walmart’s stores in the US and Canada. Kroger, the biggest US grocery chain, is also expanding its ghost kitchen operations.

Phantom-like as they may sound, ghost restaurants will likely be as normal as buying cars or fashion online.

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