Applications of Predictive Analytics
The following are some examples of how predictive analytics can be applied in financial services, retail and manufacturing. This list is not comprehensive, but it provides some interesting applications.
In the financial services the cost of making the right decisions provides marginal benefits, while making the wrong decisions can have significant costs. Most applications of predictive analytics in the financial services industry help companies avoid making the wrong decisions. For example, credit card companies are able to determine who is most likely to default on their credit cards in the next 6 months by applying predictive analytics to customers purchases and demographics.
In retail understanding customers is essential to success. Retailers have provided customized shopping experiences by using predictive analytics to understand the drivers of profitability, loyalty and activity for each customer segment and develop specific campaigns for each segment. This has allowed retailers to wow customers with personalized services while scaling and keep prices competitive. Predictive analytics have helped both offline and online retailers determine which products to carry, optimize marketing plans, and develop promotional and loyalty programs. Imagine only offering effective loyalty promotions to profitable customers at risk of leaving, while avoiding offering discounts to unprofitable or already loyal customers. Another example is knowing what a customer is most likely to purchase next, so that your staff or website can make informed recommendations.
Manufacturing is about knowing what, how, and how much to produce. Predictive analytics have helped manufactures manage their supply chain and production schedules by accurately forecasting demand, and have helped manufactures produce goods in the most effective way possible by predicting failure of equipment, monitoring workers, and identifying ways to eliminate inefficiencies. For example, CAN has helped companies with tens of thousands of sales each month forecast sales within a 10 to 50 units, so that they can optimize production schedules and supply chains. Also, imagine being able to understand why different managers have different levels of employee turnover, employee injuries, and equipment failure.
Beyond specific applications, predictive analytics has the unique ability to help companies become less wrong, scale decisions and systematized learning.
Less Wrong: The basic idea of Less Wrong is that in business, and almost anything in life, you can never be perfectly right, but you can be less wrong and by striving to continually become less wrong you get closer and closer to being right. By using predictive analytics you will not get the perfect answer, but you can determine what is happening, what most likely happen and most is most likely the right thing to do. For example it might be really nice to know exactly what commodities prices will be in a month, unfortunately this is not possible. However, using predictive analytics you might be able to predictive with 70% accuracy which direction a commodity price will trend and if this is better than before predictive analytics then it most likely will be worth the investment. Another example would be picking which commodity would most likely be the best investment in the next 6 months. The reason that predictive analytics can’t produce exact results is because it is not a simple science, for more read my post on Simple vs. Complex Science.
Scale Decisions: Predictive analytics has the unique potential to allow executives to scale their decision making as organizations and decisions become increasingly more complex with ever thinner margins for error. Predictive analytics can be used to create a model of the business based on the organization’s data and executives’ theories. For example an experienced sales manager will be able to determine which sales leads will most likely respond, purchase and be profitable customers, however he or she does not have time to review every lead for a 200 person sales team. Also, while a sales manager knows what make a lead worth pursuing, he or she most likely finds it difficult to communicate the rules and criteria to their team. Using predictive analytics a sales manager can develop a model to score incoming sales leads. This model can be coded directly into the company’s customer relationship management (CRM) system so that leads are scored as soon as they are entered into the system.
Systematized Learning: In the future, profits will be directly related to your company’s rate of metabolizing new knowledge, as opposed to renting out existing knowledge. Predictive analytics can increase your company’s ability to metabolize new knowledge by continually studying the data produced by your company, customers, non-customers and competitors to find important patterns that will impact your business. For example, predictive analytics can help executives identify when customers stop responding to a certain campaign and why.
While every company can benefit from becoming analytical, like any other tool, predictive analytics can not fix anything. However, it is most certainly the next step in the evolution of business intelligence. If properly applied, predictive analytics has the potential to help businesses work smart. Read a related post on when to and not to apply predictive analytics.
I fully agree with you about models giving you direction (most likely scenario) and not predict the future. Predicting the future is truly impossible and predictive analytics (or any other science) cannot do this.
I argued for something similar here http://www.simafore.com/blog/bid/57259/is-predictive-analytics-a-misnomer. Would love for you to comment.