Predicting store sales
Every retailer or restaurant has its own recipe for location success. At PiinPoint, we create completely customized and interactive sales forecasting models to help you quickly query a Candidate location and predict mature store sales, right in the app.
While everyone is familiar with a basic regression model, PiinPoint investigates all methods to best detect what patterns lead to successful locations. Using your historical sales data, alongside PiinPoint's proprietary and third-party datasets, PiinPoint sales predictions allow you to identify the locations that hold the highest sales potential and reject those that don’t.
How it Works in the app
Once your custom sales forecasting model has been built, tested, and implemented into the PiinPoint platform, there are two ways you can query sales at a site.
Via Existing Candidate Locations
Candidate locations that you create within the Layers panel allow you to bookmark potential sites to review later on, generate reports from, or run a sales prediction.
If you don't already have a Candidate location list established, create a new empty layer with the Candidate Layer toggle set to YES:
Once you've identified an address or site that you're interested in saving, right click on the map at the location and add it to your Candidate Layer.
Within your Candidate modal, you'll see the option to View Sales Prediction. Click on this button to access the sales forecasting input form:
Via the Sales Forecasting Panel
The second way you can query sales at any site is via the Sales Forecasting feature panel. Any PiinPoint account with a prediction model built out for it will have an additional feature within the left-hand feature panel called Sales Forecasting:
By clicking on the Sales Forecasting panel, you can quickly drop a new Candidate point at any location and query its sales after filling out the input form:
Your Predictions Input Form
The input form shows all the customized factors that go into your sales predictions model.
For example, if PiinPoint identified that square footage is an important factor for store sales, there might be a field to input the square footage at a site. Or perhaps the presence of a drive-thru location will significantly alter sales. Since these are site-specific factors related to the actual real estate you're evaluating, you'll input these for each site:
Once all the fields have been populated, click Predict Sales.
The sales prediction model will run and then show your Mature Store Sales calculation. If you're finished with the prediction, you can then save the forecasted number as an attribute in your Candidate layer's table, by clicking Save.
Evaluating Sites by Sales Potential
With a set of Candidates identified, you can begin to rank the Candidates by any of the fields in your input form or the predicted performance itself.
The candidate location will now have the predicted sales saved to the site, which you can view in a few different ways:
In the context menu of the Candidate location, by clicking on the marker.
In the attribute table for the Candidate layer.
As a label on the map above the location, as shown below:
Services Type: Advanced GIS Consulting
Delivery: 8-10 Weeks following Data Review
Still have questions? Let your Customer Success Manager know via the Chat or send us an email at email@example.com.