Applications of Indoor Positioning Systems, a Fluxus Ventures Review

This document continues the discussion of Indoor Positioning Systems (IPS) from the previous document titled “Indoor Positioning Systems, a Fluxus Ventures Review”. The former focused on the various technologies used in creating IPS, leading to the conclusion of a lack of a low-cost, flexible, scalable and accurate technology currently available in the market. This document provides a more in-depth discussion of the applications of IPS.

Let us start with an overview of the various applications of IPS currently in the market and the specialised firms providing them. We classify these applications into four major categories:

  1. Customer engagement: Delivering contextualised interactions with denizens of buildings;
  2. Indoor location and wayfinding: Orientation and wayfinding inside buildings;
  3. Resource management: Management of staff and mobile assets inside buildings;
  4. Analytics and insight: Using data intelligence to drive business decisions.

Our research shows that some firms offer only one application while others offer a combination of applications. The figure summarises observed firms based on their estimated valuation and the applications that they offer.

The figure shows that most firms, regardless of size, offer either customer engagement, analytics solutions or both. It also shows that the two applications are highly regarded by investors, in some cases ably taking firms past Series C and D rounds.

One explanation of the significant interest towards customer engagement and analytics can be found in the size of the opportunities.

Customer engagement seeks to grab a piece of the advertising market worth hundreds of billions of dollars by pushing advertisements to customers and influencing consumers’ purchasing behaviour. It should not be hard to auction spots in customer-facing apps in the digital advertisement market but it is difficult to create apps that get customers’ attention and get enough information on the customer to allow for targeted ads. Customer engagement apps include loyalty apps, shopping apps, etc.

The other investor-favourite application, analytics and insights, seeks to disrupt the retail and real-estate industry worth trillions of dollars by collecting and feeding previously unobtainable information into the decision-making process. The objective for retailers is to gather enough information on customers to be able to have better-selling products in stores, maximise selling space, evaluate marketing campaigns, and optimise store placement. The objective for building owners is to gather information to increase rent, optimise tenant mix, and evaluate marketing campaigns. It is important to note that for a yielding asset, such as a shopping centre or an office building, small changes in net operating income will significantly affect its valuation—which may be realised in the income statement or through a sale.

The main challenge in achieving the preceding objectives is consolidating IPS data with data from other sources to give sufficient context to the data to be useful. For instance, there will be a lot more insight from a combination of traffic and gender information than traffic information alone.

The next application, resource management, seeks to offer a better way (in terms of efficiency or effectiveness) to manage staffs. Staffing is a major cost in real-estate operations. In European Retail, the ICSC estimates 4.2 million people are working in 190 million square metres of lettable area (ICSC Research, 2015) which equates to 0.022 person / sqm of lettable area. Assuming an average EUR 2,500 cost per person per month, staffing cost equates to EUR 55 per sqm per month, comparable to the average cost of rent.

One proposal for optimising staffing cost is implementing capacity matching—making sure staffs are assigned when needed and not assigned when not needed. Businesses should anticipate demand and have ratios of staff to demand. Assuming they are correct, there will still be the additional complexity to the assignment process (creating an on-demand workforce and assigning demand-dependent roster to the workforce) proportional to the frequency of the roster change. It remains to be seen whether necessary assumptions can be made correctly and whether the optimisation is worth the added complexity.

The last application—Indoor location and wayfinding—discussed here deals with the common problem of going into a new mall or a big building: being lost. Whilst the application’s benefit to customers is direct and tangible, other factors will be important for its continued success. The application needs to have enough updated content and features (i.e. recent maps of multiple buildings/venues) to be useful to a great number of people in various settings. It also needs to generate revenue to pay for the provision of service.

We look to Google Maps for an example of a successful model. It has maps of almost the entire world with updated-ish POIs, useful features (i.e. wayfinding, detailed information of POIs, pricing feature for hotel POIs, streetview), and revenue generation avenues (i.e targeted ads, payment for use of APIs). We should also mention that Google Maps also works indoor in some location, so we might see it becoming the dominant player in indoor location and wayfinding soon.

In summary, all the applications of IPS have promising revenue potential but they will need support from other technologies to be successful. Customer-facing applications arguably have the greater challenge of getting many customers to use the applications. They will require a combination of useful features and successful consumer marketing to overcome the challenge. Having judged the complexity and market of various applications, we share the view of prioritising the crowded analytics applications but recognise the possibility of a major gain from betting in less-contested applications.



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