“You Can Observe a Lot Just by Watching” – Yogi Berra

Well, in our case more like listening.  While our blog updates have been just a teeny bit well…lacking, we have been hard at work improving our services.  Many thanks to all of our users and potential users who have offered up their input and guidance as we continue to strive to make Sales Temperature as useful and valuable as it can be.  We have been listening, and we are getting ready to roll out a number of new features.  We’ll follow up this post with a series of social media and blog updates to announce specific enhancements, but here are the top three highlights:

1) Launch of a weather impact calculator to determine the historic and forecast impact of weather on a retail location (sample below).


2) Creation of data co-ops to allow users to categorize their locations.  By utilizing co-ops, we can improve the accuracy of the forecasts for all of the co-op users.  We can also allow users to compare their historic and expected future performance versus other members of the co-op.  And don’t worry, we will always maintain strict adherence to data confidentiality among users.

3) Exposure of the Sales Temperature services through an API.  This will allow our users to integrate our services into their existing dashboards and applications.  Want to use our real-time forecast as an input to your scheduling app or quickly see the quantified impact of weather on your sales last week in your BI tool?  The API allows for these kinds of data exchanges and once they’re set up, updates occur automatically.

Check back soon for additional information on our exciting new features, and thank you again for all of the user input and guidance!  Please keep your thoughts and comments coming, we can always be reached at info@salestemperature.com or www.salestemperature.com.




How Does Machine Learning Work?

Here’s a nice quick read about how Machine Learning works.  A few points we’d emphasize: 1) Usefulness of current machine learning tools is a function of data and pattern recognition.  The more relevant data that the machine learning tool can utilize, the more likely it is to recognize patterns to improve accuracy in the assigned task.  2) In order to facilitate the learning, “researchers continually monitor the input data streams and make adjustments if necessary.”

Here at Sales Temperature, we are rapidly growing our user base, allowing us to feed our machine learning tool with additional retail sales data.  We are also constantly testing and adjusting external variables.  As a result, our machine learning tool is getting smarter every day and producing increasingly accurate sales forecasts.  Visit us at http://www.salestemperature.com to sign up for a free trial, and “Know Tomorrow’s Sales TODAY.”

Wall Street Journal Article Re: Employee Scheduling

Welcome to the Sales Temperature Blog!  Here we will try to highlight and discuss articles that we feel may be helpful and informative.  We recently read this article in the Wall Street Journal regarding some of the regulations that are being adopted by state and local governments to address concerns of part-time workers:


The article highlights three primary goals shared by many of these regulations.  First, provide employees with more notice of their schedules.  Second, give employees more access to extra hours.  Third, provide compensation to employees for last-minute scheduling changes.

We believe that these kinds of regulations highlight the need for retail managers to more proactively manage employee schedules.  This may seem like a daunting challenge, but we are here to help!  By utilizing advanced machine learning technology, Sales Temperature provides an accurate, continuously improving seven-day revenue forecast that can be used to predict labor needs and provide the insight and lead times to anticipate schedule changes.  All of which will help retail managers to effectively react to changes in regulations.

Accurate retail sales forecasting at your fingertips.