Twice weekly articles to help SMB companies optimize business performance with data analytics and to improve their analytics expertise.
Thanks to Amazon, Netflix and the like, recommender engines have become synonymous with predictive analytics and in many ways are the benchmark indicators of predictive analytics maturity in an organization, particularly in retail and ecommerce. Its adoption is going to increase to more non-tech type companies and even smaller businesses, thanks to the open source movement in analytics and big data infrastructure. At the end of the day, all content providers (including this website, via Add This!) will make use of recommender engines.
Recently I came across this bit of interesting factoid which went mostly unnoticed by media, because for most people it is not earth-shattering news- "... the operating cost of some robots is now less than the salary of an average Chinese worker". This has tremendous implications for manufacturing in the near future (10 years at the maximum) where most mundane and low level jobs will be taken over by machines - especially connected machines. Typical among this list of jobs which will move away from humans and towards machines are assembly line work (will be handled by robots, mostly) and complex manufacturing (3d printers). However in the non-manufacturing world too, the impacts of connected machines will be clear: taxi drivers and chauffeurs (replaced by self driving cars), delivery men (by drones), pharmacists and even personal physicians (by smart Watson-type programs) and so on. This is as game-changing as the internet was barely 20 years ago.
This article was contributed by Vaibhav Waghmare.
Portions of this article were contributed by Vaibhav Waghmare.
Supplier companies which manufacture commoditized products face constant cost pressures. As a way to separate themselves from the herd, many savvy suppliers are realizing that what may set them apart is the ability to help their customers' buyers make an informed decision. A manufacturer of paper designs which supplies national retailers has realized that in addition to providing the buyer with designs and sourcing information, they can also act as trusted advisors to their customers by identifying and recommending trendy ones. This is something predictive analytics can provide, starting with some basic time series forecasting.
There are many issues that impact customer retention for businesses. The cost of neglecting these can be enormous and in many ways is directly proportional to the size of a business. For example, for large automotive companies which sell millions of vehicles per year, a single percentage point decrease in customer retention can mean millions of dollars of lost business and untold loss in brand image. But this does not mean that small and medium manufacturers do not face this problem. While the absolute scale of the problem (in dollar figures) may not suggest a massive issue, relatively speaking, these same problems could devastate a smaller company.
Time series forecasting is one of the more basic predictive analytics needs of many businesses. There are a lot of data elements which come in as time series: product sales, shipping and transportation costs, commodity prices and so on. From a strategic perspective, managers and decision makers will frequently need to be able to predict trends and seasonal patterns for these elements.
Can predictive analytics be used to forecast fashion trends? This might sound like black magic or worse snake oil. But design or fashion trend forecasting can be a real problem, and one that data may help to simplify.
This article was contributed by John Thiels.
Migrating stakeholders from a minor role to an increasingly major role is a challenge for all organizations. For example, a car company might be interested to know what it would take to move their entry-level car buyers, who are typically 20-somethings into their more upmarket products, as these customers mature.