Why Crowdsourced Data Matters In Retail Forecasting

CrowdSourcedData

With the recent unveiling of the new Google News app, we, like many other users, had to re-implement many of our prior preferences, favorites and saved searches.  Re-establishing these preferences at first seems like a mildly annoying chore but it forced us to think about the preferences and screens that were really important to our business.  Along with the obvious searches, such as retail, sales forecasting, Sales Temperature, weather impact calculator, restaurants, ML/AI, predictive scheduling and minimum wage laws, we also added a search for Crowdsourced Data.

You’re probably familiar with crowdsourcing.  As defined by a quick Google search, crowdsourcing is “the practice of obtaining information or input into a task or project by enlisting the services of a large number of people, either paid or unpaid, typically via the Internet.”  Crowdsourcing became well-known with the emergence of popular services such as Wikipedia and Yelp but according to Wikipedia, the first known example of crowdsourcing was the Longitude Prize in 1714, when the British government offered a monetary award for the best solution to measure a ship’s longitudinal position.

But what about crowdsourced data?  This may be a less familiar term, but the idea is reasonably simple.  Crowdsourced data refers to the creation of data sets utilizing a large number of data providers or sources.  Similar to crowdsourcing, when executed correctly, crowdsourced data produces a type of network effect; as the number of contributors and the data set itself expands, that specific data set becomes more valuable for all of the users.

For an illustration of the power of crowdsourced data, think about the Waze traffic and navigation app, now owned by Google.  By using the app while driving, individual users contribute to the traffic information collected by Waze, allowing other drivers to benefit from the aggregated, real-time traffic conditions.  The application wouldn’t be nearly as powerful if the data set was limited by some meaningless constraint like drivers of a certain brand of car or users of a specific model phone.

Crowdsourced data is becoming increasingly important with the emergence of artificial intelligence.  Most of the applications that utilize AI rely on and greatly benefit from large amounts of data that allow the AI platforms to continuously “learn” and improve the accuracy of their answers.  Crowdsourcing data is a very logical method to develop and maintain the types of large, growing data sets utilized by AI.

What is crowdsourced data utilized for today?  Our search for crowdsourced data on Google News returns a range of topics that span from the mundane to the fascinating.  Crowdsourced data is being used to help counties locate and fix potholes, allow cities to address dog mess problems and enable scientists to study the human microbiome.

What do all of these examples have in common?  First, the data contributors benefit from the resulting data set that is crowdsourced by all of the contributors.  Second, the universe of contributors is as inclusive as possible to enable the creation of a large useful data set.  Similar to the prior Waze example, would the dog mess data be as useful if it only included poops from pekingese dogs or if the American Gut Project to study the human microbiome only included samples from vegetarians?

At Sales Temperature, we are committed to the belief that retail sales forecasting benefits tremendously from utilizing crowdsourced data.  That’s one of the reasons that we recently released our new data co-ops that allow our users to join groups that share data based on brand, category or location (while of course keeping each individual user’s data confidential).  By sharing data, we can produce a more accurate forecast for all of our users and provide greater detail about the historic and future impact of weather on their business.  All without erecting artificial constraints that hinder other forecasting tools, such as POS systems or scheduling apps.  After all, shouldn’t the information you use to run your business have the same advantages and benefits as the apps you use to avoid traffic jams?

 

 

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