How an Underachieving Retail Outlet Turned to Location Intelligence to Transform Its Performance

With tenants moving out and poor financial returns, a client sought the help of PlaceSense’s enhanced SaaS self-serve platform to find out why their retail outlet was performing poorly and use the insights gained to turn the business around. 


The German city of Wiesbaden, located slightly west of Frankfurt, features a vibrant shopping district among its gorgeous architecture and cultural hotspots. However, a retail outlet in the heart of the city was struggling. Tenants were moving out and the owners knew the outlet was underperforming financially in comparison to other businesses located in the busy city center location.

Already familiar with PlaceSense’s ability to generate insightful data about any location, by accurately analyzing the people who visit it, the client wanted to use location intelligence data to find out the reason behind the poor performance.

Visitor Trends Visualized In an Instant

The retail outlet, situated between two busy streets — Langgasse and Wagemannstraße — had plenty of shoppers passing by. So, the question was, why were they not being attracted to the retail unit in sufficient numbers?

PlaceSense’s enhanced platform gave access to the deep, multilayered location-based intelligence needed to gather the answers via its sleek, visually powerful dashboard. Within 15 minutes the client was able to visualize a weekly visitor trends graph for the outlet in Wiesbaden, and understand the most popular day and hours. 

Access to this highly sophisticated visitor data was fast and allowed the owners to understand how people were behaving in and around the location at a granular level, and how that behavior changed over time. 

The data highlighted that visitor traffic on Langgasse, on the left side of the retail outlet, peaked on Saturday afternoons between 3-4pm. This is typical for a retail highstreet and was easy to view within the PlaceSense dashboard. The visitor traffic to the outlet also had the same peak in visitor traffic. This made sense as its tenants were also retail stores.

However, when Wagemannstraße, on the other side of the retail unit, was analyzed, it displayed a different visitor pattern. Its peak visitor time was between 7-8pm on Saturday. So, even though it was only ten meters away it registered a four-hour difference in peak visiting time. 

Deep Data, For a Deeper Understanding 

When analyzing this visitor behavior and the businesses they visited, the reason for the difference became very apparent. While Langgasse is a retail highstreet, Wagemannstraße is filled with bars and restaurants. Thus people shopped in their droves during mid-afternoon on Langgasse, but ate and drank later in the evening on Wagemannstraße. 

PlaceSense’s dashboard instantly gives users the power to deeply analyze locations and perform location-based calculations on the fly. This meant visitor duration and cross-visitation data could be displayed in an instant. The data showed visitors to Langgasse spent an average of 51 minutes there, whereas those visiting Wagemannstraße stayed 78 minutes, almost a half an hour longer. This confirmed the hypothesis that people tended to sit and spend more time eating and drinking on Wagemannstraße.

Visualize Your Customers’ Shopping Habits 

The cross-visitation graphs viewable within the PlaceSense platform confirmed the outlet unit was not optimized to take full advantage of its location between these two very different streets. This cross-visitation analysis measured the percentage of people that visited the outlet and then went on to visit Langgasse (77.5%) and Wagemannstraße (37.5%). As our outlet was 100% retail, when visitors wanted food and drink they left and went to Wagemannstraße. It could be possible to capture some of the 37.5% of visitors that went to Wagemannstraße by opening a bar or restaurant within the retail outlet. 

The data and the insights derived using PlaceSense quickly allowed the owners of the asset to understand they could not rely on having it dedicated 100% to retail if they wanted to optimize its usage. The insights showed them that they should consider adapting the tenant mix of their retail outlet to service the broader visitor traffic between the two streets. But what else did PlaceSense data tell us about this outlet?

Understanding Retention Rate and Catchment Areas

Optimizing this outlet makes sense as the site shows good potential as the dashboard report on return visitors shows it has a solid retention rate, with just under 59% of visitors returning. This compares favorably to Langgasse street (57%) and Wagemannstraße street (60%). The majority of this foot traffic comes from the immediate catchment area of central Wiesbaden, which delivered almost a quarter of its visitors. The remaining visitor traffic is spread evenly throughout surrounding areas (within a radius of around 12km). 

However, even though Südost is a borough in the heart of the city of Wiesbaden and just under 1km away, it provides less than 2% of visitors. Compare this, for example, to the town of Taunusstein, which provides 3.35% of visitors and is 9.17km away. This is the type of detailed catchment area data can help transform marketing and advertising campaigns.

Location intelligence beyond counters, or surveys

When utilizing PlaceSense, each metric, such as visitors trends, duration, cross visitation, catchment areas, and so on, can instantly be compared with other locations and competitors. You can analyze all locations, not just highstreets. It doesn’t matter if these locations are right next to each other or far away. You can just as easily compare two retail stores in the same or different cities. 

PlaceSense users can quickly pick their own locations to analyze and within 15 minutes have a host of visually powerful graphs and charts allowing them to quickly develop deep insights. This provides them, for the first time, with an affordable, scalable and easy-to-use location intelligence software tool. It allows users to quickly change and add locations, and to perform a broader analysis using near-real-time data. 

PlaceSense is very fast and very precise, so decision-makers no longer need to rely on outdated data or spend large budgets for highly limited insights. PlaceSense data will reveal the full, up-to-date analysis all the time, any time… in an instant. 


Request a demo here to experience the power of PlaceSense.


About Jan Barenhoff

Jan is Director of Business Development Europe at PlaceSense. He has a track record across multiple industries working to develop and build client relationships and opportunities. Jan holds a master’s degree from RWTH Aachen University.

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