Turn paths into insights
Path Analytics is our innovative way to track and classify visitors based on their individual walking patterns. sensalytics uses high-precision 3D sensors to calculate the dwell time and path of each individual visitor - accurate to the nearest centimeter and second. With just a few clicks you can define your customer groups and analyze the dwell time, number of visitors and cross-zone funnels with dynamic motion-heat maps.
Object-Rule-Engine: The intelligence behind
The Object Rule Engine (ORE) is based on raw data from our smart 3D sensors, which can be stitched together to cover and monitor an almost infinite area. These sensors record all movements of your visitors - to the nearest centimeter and second. Logical zones and entry lines are used to structure these movements. The object rule engine operates on this data.
With simple path and duration values, you can now individually and at any time - also retroactively - determine how a visitor should be classified or grouped:
- “Lost potential customer“: A person who spends a significant amount of time in a particular department and then leaves without proceeding to the checkout area
- “Employee“: A person who enters the store and immediately occupies a cash register or proceeds to the staff-only area
- “Actual customer“: A person who enters the store, takes a look around, and then proceeds to the checkout area
The ORE by sensalytics turns the walking paths of your customers into reliable and meaningful figures. So you get clarity in the usual “visiting noise“. Detailed information can finally be clearly assigned.
The analytical beginning
The Path Analytics cockpit provides you with the ideal entry into the path analysis. Organize, filter & rotate your desired data with just a few clicks. With sensalytics you will get dwell times and number of visitors in all illuminated spots of your premises. You will not only get the average length of stay, but also the number of visitors with specific dwell time clusters. Target visitors with low or high dwell time. Take a look at which hotspots exist and show or hide specific groups of visitors.
“Where were those who were there too?“
How many people with a dwell time of more than three minutes were in section A and then in section B? What percentage of all buyers were in both section A and section B? These and many more questions are answered by the sensalytics zone funnel. There are various fine adjustment and filter mechanisms available. Limit the analysis base to specific visitor classes or dwell times, and look at how, when, where, and how long the filtered visitors spent with you.
Be the director of your own heat map
When it gets more detailed, you may rely on crosstabs and charts. For a visual and geographic orientation, on the other hand, a ground-plan heat map is required: At sensalytics, we go two steps further than is the case with otherwise conventional, rather clumsy representations. Our system displays dwell times, occupancies and the number of visitors on a structured heat map. These data can be individually filtered according to visitor classes and dwell times. So you finally see in the “rush of the movements“ what you always wanted to see: the actual shopping behavior of individual customers and not just a general presentation of many people.
This finally turns a heat map into an analytic diagram. The complete tracking history will be played here as a heat map movie. Be the director and get the insights you've been waiting for.
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The sensors for Path Analytics
3D sensorHigh-precision tracking with overhead installation
The 3D sensor is the allrounden in real world tracking. It allows people tracking down to the centimeter and second. People paths can be tracked along thousands of square meters.
3D sensors are the raw data source for your Path Analytics solution. Obviously they can also count people and passer-bys. In addition, the 3D sensors - without further hardware installation - also determine the gender of the visitors, which allows more in-depth analysis.
The 3D sensor is 100% GDPR compliant.