Over the past decade, social media engines like Facebook and
Twitter have used up much of our free productive time. So much so that it has
now become a huge blob of data, most of which is just sitting on a server, or
“the cloud” as most of us fancy it today, providing no additional value in the
future. Does this really have to be the case? Can we actively use the data
sitting in these servers to affect our business capabilities in the future?
Social media analytics is proving to be an effective tool that decision makers across the globe are using increasingly to improve their daily business. And unknowingly, all
of us, as social media contributors are taking an active part in this process.
A particular area in which social media is still under
utilized is in providing demand forecasting for a product or a service. Two
properties of social media specifically can be exploited to our advantage in
providing forecasting. Firstly, it is current data that we can analyze in short-term
time intervals. Secondly, there is a lot of data available. A lot of data! Both
these characteristics make it a perfect platform for us to conduct a forecast. Let
us see how we can put it into action.
We are all excited about the new Apple iPhone 6 releasing in
a week from now. We are right? Well I am, and so are millions of people across
the world. Everyone is tweeting about his or her anticipation and views about
the next iPhone. So what happens with all this data about the iPhone that
people share? Can Apple use this data to forecast user demand for their product
even before the product is launched? If social media analytics gets it right,
this is very much a possibility. So how exactly does this information translate
into useful data for supply chain forecasters?
The huge amount of data uploaded by the users, in the form
of tweets, likes and comments can be analyzed using social media analytics and
provide a rating through a process called “Sentiment Analysis”. A rating of
“positive”, “negative” or “neutral” is associated with a particular post,
comment or tweet depending on the sentiment that it carries relating to that
particular product or service. This rating is then turned into a numerical
factor and plugged into a formula to forecast the product demand.
The method of sentiment analysis can be classified as a
Causal method, as it assumes that a specific variable, in this case the
sentiment factor, drives the forecast. Enterprise Resource Planning (ERP)
software giant, Oracle, is seeing this method of analytics as a game changer in
providing companies insights about product launch strategies, marketing
strategies and sales forecast. Oracle, along with companies like IBM has
already released social media analytic tools that have the capability of
providing sentiment analysis aiding their traditional forecasting capability.
Where else can this concept of analyzing social media trends
for forecasting demand be applied? “Google Flu Trends” was one of the
initiatives taken up by Google to analyze and forecast flu trends across 25
countries in a time series analysis. The tool collected historical data of the
total number of flu cases reported and created a forecasting model that could
predict the outbreak of flu before it actually occurred. Applying our knowledge
on social media analytics to this model we can analyze how sentiments on having
flu can be used to provide additional information and help improve forecasting.
Suppose I am catching flu and I tweet "Ugh, I am falling sick". This
information will be analyzed and assigned a sentiment rating. Similar to me if
there are millions other tweeting about catching the flu, it is not difficult
to see how this information will be translated to generate a forecast. This
information can come handy for organizations like the Centers for Disease
Control and Prevention (CDC) or a pharmaceutical company to stock up enough flu
medication supply before the flu season begins.
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