There are many metrics that apply in predictive analytics, especially in business. When incorporating platforms like Cognos BI software, it's essential to know what particular measurements a company should look at closely and better forecast. In the case of the consumer and packaged goods sector or any other business-to-consumer industry that sells products, quality will be the primary concern. However, it's often difficult to assess how good a product is overall because of differences in customer's opinions. With this in mind, people's overall response to goods can influence predictive analytics and in turn help businesses better design the items they make.
Making informed bets
To consider the potential of customer response in relation to product decision-making, one doesn't have to look further than Netflix, according to Data Informed. Now, the recommendation engines do a considerable job in informing customers what kinds of movies they may like to see based on what they previously watched. What many businesses don't realize is that the media company likely uses that same information to create and greenlight original programming.
Netflix's runaway hit "House of Cards" is very much the product of predictive analytics. The company saw that many people loved political thrillers. It witnessed a particular cult following develop for the 1990 BBC miniseries "House of Cards." That was its first hook. It then found that David Fincher, a director well-known for thrillers such as "Seven" and the American adaptation of "The Girl with the Dragon Tattoo," had a high degree of popularity among subscribers. The company found its producer in the same way. Finally, when reviewing potential lead actors, Netflix found Kevin Spacey, an actor who worked with Fincher previously and has a reputation for portraying complex and interesting characters, was extremely popular among users as a leading man. These three data-informed decisions made the TV series the success it is today.
Of course, customer response isn't only good for making decisions on movies. It can help ascertain simpler concepts. For example, establishing the ideal price point for a product can be possible with predictive analytics, according to Information Week. Recognizing what customers are willing to pay for particular products and goods can be found in prior sales history. By making adjustments, businesses can find the sweet spot that will not only guarantee a high level of sales, but also increased profitability. Such measures can make a difference in the long term viability of products overall.