300 hours of video are uploaded to YouTube every minute! Almost 5 billion videos are watched on YouTube every single day. This is one example of the pace of data growth and overall size of data is piling into zetta-bytes faster. Where am I going with this is relevance of rapid analysis of data measured in hours or days rather than the stereotypical months of traditional data mining. The result is an opportunity to derive meaningful insights to business with more emphasis on predictive analytics.
Predictive analytics is nothing new. Predictive analytics is a way to identify the probability of future outcomes based upon historical data. For example, from customer perspective, companies can predict a likely lifetime customer value or the probability of either loyalty or churn. Let us look at few use cases to examine the relevance and importance of predictive analytics in the world of enormous data.
A fashion retailer story: Predictive analytics helped in analyzing the campaign spends and predicting incremental campaign impact of spend. This helped the retailer in understanding where not to spend: Having a close look predictive analytics helps in understanding the relationship between customer segments and the marketing campaigns being interacted with. This retailer predicted the probability of a particular channel influencing online or offline purchases within specific customer segments, and while this was enormously useful in understanding how to spend budgets targeting more personalized digital campaigns, it was equally insightful into identifying spend that wasn’t contributing to incremental value. The power of predictive analytics came in determining, should the retailer pay for this ad, or will a sale happen organically through another channel or communication that might cost nothing or next to nothing? This allowed the marketing team to choose the right channels to most effectively and efficiently reach different groups of prospects and customers, and second, it provided the information required to personalize by sending the right content and message to the right segments at a very granular level.
Preventing hospital readmissions: Hospitals are turning to predictive analytics as they began to feel the financial pinch of high 30-day readmission rates. Real-time EHR data analytics helps hospitals cut readmissions by five to seven percent. This demonstrates how predictive analytics in real-time can analyze EMRs data to automatically identify and target patients at the highest risk of readmission early in their initial hospitalization when there is a lot that can be done to improve and coordinate their care, so they will do well when they leave the hospital. Notably, Kaiser Permanente has been working to refine its readmissions algorithms in order to better understand which returns to the hospital are preventable and which are not, a crucial distinction for value-based reimbursements.
Lifetime value analysis of a subscribers of communication service providers: CSPs are lately realizing that not all customers are the same. It is important for CSPs to assign a quantifiable dollar value to each customer, in order to prioritize various sets of customers. The Lifetime Value of a subscriber provides the predicted yield from each customer over the customer life. This helps CSP in offering high priority customers loyalty bonuses, preferential treatment through personalized service, better credit norms etc.
We are living in a world of data overloading. Predictive analytics can be a true human partner from elections to sporting events to the stock market on what the future will bring. Predictive analytics elevate human kind from an educated guess to data backed decision.