Forecasting with Data Analytics and Crowdsourcing
I plan to discuss an fast evolving-alternate view to
forecasting provided in the article “Cleaning the Crystal Ball”, which proposes
efficient ways of creating forecasting models. The article makes some convincing
points about forecasting when it says that forecasting models competence is not
determined by its accuracy , instead by how well uncertainty has been accounted
for. In order for the organizations to succeed at making better forecasting
models it suggests having multiple people in the forecasting team, using
historical data and embracing an open culture.
Internet-based Organizations these days are a step ahead at
forecasting where they are using data analytics and predictive algorithms for
forecasting purposes. This coupled with crowdsourcing has helped certain companies
keep their inventory low and have zero unsold products.
For example the clothes apparel company Threadless, provides
a platform for designers to showcase their work, which is rated by an active community
of users. Threadless runs weeklong competitions and selects the highest rated
designs to be printed on different apparel. Those who rate the designs, buy the
same products they rated for. Threadless gets a live pulse on the design trends
and gets an exact idea of the no. of tshirts to manufacture thus keeping inventory
costs low. In addition to this Threadless is also present on various social
media platforms where it engages with its customers. It has currently partnered
with a data mining and an analytics solutions provider (JMeter) to integrate
customers in a centralized location. They have gone a step further in their marketing
efforts, by tracking their social interactions through Google Analytics and tying
it on the back end to JMeter. This has helped them track customer interaction
channel by channel, Track how customer preferences have changed over time and
what factors influence them. As a result of which they have seen longer
customer retention rates and increased consumer engagement.
There have also been more examples where e-tailers have used
data mining and analytics to forecast what the customers would want to buy and
when. Amazon, Target, Walmart are some of these who have used these technologies
to better engage with customers and “sell them what they want”. Amazon has
developed a recommendation which suggests things you could be interested in
buying depending on what you search for or what you have bought before.
Applications of forecasting have been extended to beyond just
predicting sales to effectively targeting customers and increasing revenues
using data analytics.
References:
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