Your competitors are investing in Predictive Analytics.
Are you still using your gut and intuition to predict future outcomes?
Predictive analytics allows decision makers to anticipate future states based largely on advanced statistical modeling, giving companies a chance to act (e.g. offer a discount to retain a profitable customer) or to change an anticipated future state (e.g. prevent a customer from defecting to a competitor).
Proactive intervention strategy works to prevent customer attrition. There are two components of predicting churn: knowing the customers most likely to quit, and knowing when those customers are likely to quit. Most models that predict churn aim to answer both these questions by building a propensity-to-quit model. These models provide the probability of a customer defecting at a particular point in time. To prevent customer churn, companies should intervene when customers show a strong tendency to defect. Another key aspect of any effective intervention strategy depends on the amount of resources being spent on each customer, which should be directly linked to the worth of the customers or their lifetime value.
For example, if the customer has decided to spend X $ per customer then there is no business sense in offering this promotion to a customer with a lifetime value of minus X $.
If we were able to predict the future, we would be able read the minds of our customers and know exactly what they wanted.
The closest we can get is utilizing predictive analytics to tap into the hidden treasures that reside in the data that every organization is wrestling with nowadays. Predictive analytics, is a proven technology which mines data for repeatable patterns that are reliable enough to use as a basis for predicting future events.
Predictive analytics does not provide guarantees. Instead, it is all about increasing the likelihood that a desired outcome will occur - at the right time, the first time. These concepts of increased likelihood and timeliness are what make applying it to decision making so enticing.
Imagine if you were given the insights that would increase your accuracy of knowing the outcome of a coin toss by 25%. That is a substantial accuracy increase. Suddenly, you would be right 62.5% of the time. The other side of that coin, though, is that unfortunately you would still be incorrect 37.5% of the time.
It is important to understand and keep in consideration both of these points. Making faster, educated decisions, and taking quicker action should be at the heart of every business strategy
Providing today's organizations with the ability to utilize predictive analytics takes robust technology, as personnel alone cannot begin to even tackle such an objective. Research has shown that by 2018, North America alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, (1) as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
As Gartner (2) recently estimated, only about one-third of global demand for big data-related jobs will be met. Outsourcing is a path companies need to consider.
Benefits of Outsourcing (3):
- By far the top benefit of working with an outside organization is speed
- You can get the results faster, and don't have to hire hard-to-find and expensive data scientists
- A subtler advantage of using an outsider is the chance to examine the data with fresh eyes, without old assumptions or bad habits. And unlike the inside groups, which might be bogged down with ad-hoc requests, an outsider has time for "exploratory analysis and focus on the task at hand"
- A final plus of outsourcing is the opportunity to scale the analysis workload up and down, depending on unique conditions.
- (Big Data the Next Frontier - McKinsey
- Gartner Predicts that by 2015, big data demand will reach 4.4 million jobs globally, but only one third of those jobs will be filled.
- Should you outsource your Data Scientist