Placense Ltd

Placense makes sense of places by analyzing the people that visit them.
Based on anonymized mobile location data, we tell where people come from and go to at any time. Benchmark stores' performance against competitors, identify mobility trends or optimize out-of-home advertising strategy. We bring clarity to hypotheses based on trusted data.

Big real estate companies: Cushman & Wakefield, JLL, Union Investment, Nuveen
Retailer: Rewe, Edeka, Levi´s, Ikea
15+ successfully delivered projects with paying customers

Decision makers within the management of retailers, Out of Home agencies, Municipalities, Mobility providers, real estate companies

Retailers & mobility providers lack holistic, representative and up-to-date data to understand what people need where.
Online, every e-commerce shop analyzes which website pages shoppers visit to understand their needs. “Offline”, many retailers and train stations lack this information. Most only have limited information in data silos, e.g. fr om surveys (only a few people), cameras/laser counters (only a few locations in a city) or their own service (only their products/train tickets sold).
For the use case, we help you understand people’s mobility and retail needs - anywhere, any time, no hardware required.
Based on aggregated, anonymized GPS data from smartphones we show you for each train station which other places commuters visit (e.g. a park, a retail store, shared mobility station), the journey of retail customers and wh ere they come fr om. We connect wh ere people come fr om (far=tourists, close=residents) with what most people buy in this area (fashion, electronics, furniture, etc.), to help you understand the preferences of visitors for each individual train station and region. Based on this info, you can attract the best tenants/retailers that match the visitor preferences and you can offer sustainable mobility services wh ere most people need them. Our data also shows you regional trends near real-time and for the past 2 years, so you can identify long-term trends and short-term dynamics. All in all, we help you make data-driven decisions, understand local needs and bridge existing data silos. This is why FAW, Ströer, KPMG and others already work with us.

Our online solution is unprecedented since our clients have so far only been able to rely on costly and insufficient hardware solutions to get the data they need. We extract real-time data insights from millions of smartphone signals accessible via an online dashboard. Our machine learning technology comprises multiple different model types and configurations, creating a highly complex “Meta-model” which analyzes various data angles and generates the final output. The models are constantly tracked and trained, and before the final output is generated, the meta-model weighs all models according to their performance and selects the best-suited one for each specific scenario. Our proprietary privacy algorithm synthesizes state-of-the-art randomization, generalization and aggregation techniques, in order to comply with GDPR. It modifies data records without compromising statistical accuracy at scale, making it practically impossible to track back any data to individuals.

Examples:
- Telefonica: (https://www.telefonica.com/en/home)
- Motionlogic from Telekom: to be shut down according to news reports (https://motionlogic.de/motionlogic/en/)

Mobile market research companies:
These companies are direct competition for Placense but yet have to reach competing levels of data validation capabilities, which Placense could achieve through its various cross-industry projects and trusted investors (innogy, Nielsen).
Examples:
- Placer.ai: Focus on US market with not as strict privacy regulations (https://www.placer.ai/)
- Gyana: Pivoted drastically (https://www.gyana.co.uk/)
- Geoblink: not strong in Germany and privacy not focus topic (https://www.geoblink.com/)

These businesses are competing with Placense but are bound to public regulations limiting their ability to use location data for location intelligence. In addition, their data is not very granular (down to 150m) and purely historic, which is why Placense has already been approached by them and not vice versa. Also Placense initiated provisional patents on our anonymization algorithms to protect this competitive advantage. Placense also has a strong first mover advantage and a historical backlog of data since 2018 that will take time to achieve for newcomers. The domain is still in its infancy in Europe but gaining traction with more and more start-ups entering the market. Due to increasingly strict privacy regulations, entering the market without proper anonymization capabilities becomes increasingly difficult

SaaS B2B
As we are addressing Enterprises we have a high touch marketing approach using the following channels:
Linkedin content marketing and Lead Gen, Direct and targeted email campaigns, Conferences and keynotes, Webinars and competitions.

Product development, expansion, marketing and acquisition of data sources

Joining a CLA with a current €6.7M CAP

Estimated to convert in December 2021 as part of approximately €20M valuation round A

€700K will secure Ströer approximately 10% holdings in placense when converted

There are regulatory, data sources and reseller risks. Privacy regulations tend to become stricter or change drastically. As for the acquisition of data sources, third-party rules create risks in the dependence on these third parties. Therefore, direct data sources are crucial to ensure enduring operations. As for other dependencies, Placense is relying on resellers who distribute the product within their network.

2020 June: Webit foundation investors demo day - winner
2020: South Summit – top 3 startups
2020: Proptech innovation award - top 3 startups
2020: Built-world Israel Innovation contest proptech - winner
2019: Beyond Conventions - winner
2019: Proptech zone – 2nd place
2019: Plug and Play Retailtech Hub – startup batch