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. 

What are the challenges expansion and real estate specialists are facing in retail and hospitality? What future and solutions do they see for retail? What opportunities does artificial intelligence offer? The location intelligence startup Targomo and the online trade magazine The Retail Optimiser get to the bottom of these questions in the new report Next Level Location Planning.

The report is based on in-depth qualitative interviews with top experts who are responsible for the location management of companies such as McDonald’s Germany, Peek & Cloppenburg Düsseldorf, McFit, Jysk, Bio Company and Gustoso. As partners, IZ Research, the analysis platform of the Immobilien Zeitung, as well as the Nymphenburg Group provide deep insights into current market developments.

Today, for example, companies are faced with major tasks to adapt their branch networks to the strongly fluctuating consumer behaviour. Mobility and purchasing power of the target groups are clearly decreasing, the customers’ demands on quality and service are rapidly increasing. And the continuing triumph of e-commerce calls for clever omnichannel strategies, which in turn significantly change the demands on locations.

“The experts agreed on one thing: consistency really only exists in a persistent volatility,” says Björn Weber, publisher of Retail Optimiser, who conducted the interviews with his editorial team. After all, in addition to consumer behaviour, city centres and the structures of important retail locations are changing, which in turn requires an agile optimisation of the distribution network.

On 40 pages, the report shows how the experts tackle the challenges and what solutions are found. These include the efficient and intelligent use of data and AI, which offer new ways to identify success drivers, arrive more quickly at an assessment of potential locations and create reliable forecasts.

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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. www.targomo.com

About The Retail Optimiser

The Retail Optimiser ist he trade magazine for retail decision-makers in technology, logistics, supplier cooperation, shopfitting and market research. It reports in German and English on outstanding projects and innovations that improve the customer shopping experience and optimize retail processes. As analysts, journalists and consultants. The authors have been analytically following the development of new retail technologies for over 20 years – always very close to what moves the retail trade and its service providers. www.retail-optimiser.com

Henning Hollburg, Targomo’s Founder and Managing Director, explains what predictive analytics is and how businesses with physical points of sales can benefit from it.

“Put simply, it’s the application of sophisticated machine learning algorithms to learn from the past and predict future scenarios.

We’ve been working on predictive analytics for years and now we can offer this innovative technology to all the businesses that need it. Enabling technologies have given the field a huge boost in recent years, from better hardware to more powerful models. In addition, the emergence of modern web technologies allows us to develop such solutions in a browser-based environment.”

“Location matters to all businesses that require physical interaction with their clients. It’s the reason why our models can predict as much as 80%-90% of the success of a retail chain branch. It also means that we can tell our clients, with this level of accuracy, what sales they can expect if they want to open a new store. This gives them tremendous planning certainty.

But the potential of the predictions goes beyond new openings. Changes in sales area, opening hours, store type, etc. can also be predicted. And not only for the changed store itself, but for the other stores in the network as well. For example, one customer of ours wants to know which location they should rebrand. Another is concerned with which products they should offer where.”

“At Targomo, it’s a three-stage process. First, the client provides us with their business data. We then train our model with that data, before integrating into TargomoLOOP a custom model for the client with predictions about their business expansion.

The clients’ business data comprises their store locations combined with a number of attributes, so called location-based data. It is typically store size and type, opening hours, last renovation and so on. Clients also provide us with their KPIs like overall revenue, revenue of a specific product, or number of visitors.

Then we analyse the surroundings of each store location to find patterns. Here mainly three types of data come into play: mobility related data like public transit schedules, road networks or movement data, socio-demographic data, and points of interest data. The patterns help us understand which factors cause success or failure. With these metrics/success factors we train a model. And this model allows us to forecast most KPI for given locations.”

“We integrate the model into TargomoLOOP, the intuitive browser-based tool we developed, and they can simply type in any address to see forecasts about revenues, number of visitors and other important considerations for store planning. Ultimately they will be able to understand if putting a store there will be successful or not. 

And the great thing is that our model isn’t just useful for retail expansion but also to understand if an existing store is over performing or underperforming. We simply check the business performance of existing stores against the predicted performance using our model. With these insights, the business can optimise its network. It can adapt pricing, product offering, store hours, to replicate success or avoid low performance.”

“Of course, the possibility stands and falls with the available data. For example, we sometimes saw that certain stores had lower sales due to renovation work and the resulting closures. They then first appeared in our analysis as underperformers because we had no information on the closures. The more internal data we receive from our client, the better.   

But, especially if only a few stores are available, it becomes difficult to identify patterns. Ideally, a network should consist of more than a few dozen stores to develop a meaningful model. In addition, there are also factors that influence performance for which we have no data or that have nothing to do with the location per se. For example, if the staff are particularly friendly and have built up a good customer relationship.”

Targomo has many years of experience in mobility and human movement analysis and have also done a lot of research in this direction. This expertise is incorporated into our analyses and predictions, as the success of stores depends crucially on human movement patterns: it’s all about people getting to businesses, and businesses getting to people. It’s the essential ingredient when looking at forecasting. 

Another advantage is that we integrate this prediction model, which is individually developed for the customer, into the analytics platform. This is quite easy to use, so all team members can access it without being a GIS expert or data scientist.

Thanks to Henning for the introduction to predictive analytics. Look out for more stories about this revolutionary technology on Targomo soon.