Predictive Analytics in Business

Predictive analytics can help businesses analyze human elements starting with the HR department. According to a recent study conducted by Deloitte, 51% of businesses have established a direct correlation between their business impacts and HR programs. As a result, 44% of companies use predictive analytics to gather and analyze workforce data. As time goes on, companies will use predictive analytics to improve their hiring processes based on analyzed data sets.

Also, predictive analytics can be used to reduce employee turnover rates. HR departments will be able to go through exit interview data, performance, compensation and engagement data in order to have a clearer understanding as to what made an employee quit or why they were fired. Such data can be leveraged throughout the hiring process to ensure that the company hires the right type of employees in the future, thus saving the company money, while at the same time reducing costs associated with high employee turnover.

HR departments are not the only beneficiaries of predictive analytics, there are benefits for the customer service department as well. Regardless of the industry, predictive analytics can help improve customer interaction as well as the customer’s experience. For example, Netflix employs predictive analytics to determine what movies customers enjoy watching before offering suggestions for new movies. Amazon uses data from predictive analytics to determine what a customer will buy so they can provide “anticipatory shipping”, thus streamlining the delivery processes by sending packages to the relevant geographic regions before the customer even completes their purchase.

If you are looking for ways to enhance your marketing efforts, advertising and marketing database software combined with predictive analytics can be used as a powerful decision-making tool to help influence manufacturing optimization, increase the number of up-selling opportunities and new product development. It provides insights into how massive amounts of data can be transformed into powerful knowledge which can predict events before they happen, reduce risks, simulate potential; “what-if” scenarios and determine the best course of action in real-time. Armed with these powerful insights, businesses can now redefine their marketing moves and decisions to properly align with the business’ goals, objectives and strategies.

Predictive analytics can make the supply chain management more accurate reliable, accurate and cost-effective. Supply chain management is a continuous and cohesive process, which means that any delays or failures at any stage can cause a domino effect throughout the system and which can lead to inefficient execution. This is why predictive analytics should be applied to each step such as looking at historical demand data and try to calculate future demand and converting this information into forecast production requirements as well as procurement and production requirements.

Predictive analytics software development allows us to calculate future inventory development based on the customers’ demand and the capacity situation of the supplier. It notifies both parties regarding potentially problematic situations that lie ahead. In an ideal world, these types of situations will be so far ahead in the future, that the problem can be resolved using regular planning procedures, thus avoiding costly special measure, production downtimes, and delays.

Predictive analytics can help you optimize your campaigns and processes, but the more data you use, the more you must feed into the analytical machine. It’s a circular process. Since predictive analytics learn from new information, it should get better and better with each cycle.

Always keep in mind that every increase in outcome has an upper limit. While your efforts may improve with certain datasets in the short term, new data will be needed to continue the growth of your business. Be on the lookout for new data to add to your predictive models as you move from one cycle to another.

Rely on yourself and your analytics team when building and implementing models. Even though computers can create pictures of customer behavior, they are working with a limited number of factors. You must find a balance: you understand your customers better than the computer, but the computer can eliminate some of the guesswork. Check your model against industry knowledge and test it before making any sweeping decisions.

The bottom line is that predictive analytics actually does not have to be all that accurate to provide great value for your business. For example, business development software can use predictive analytics to identify a customer segment three times more likely to defect than the average. With this information, you can target retention efforts, such as providing a discount offer to this particular customer segment and thereby avoiding incurring revenue losses by providing discounts to customers have no intention to defect.

This game of numbers can translate into higher returns and does not depend on super-accurate predictions. If your overall defection rate is 5% and we have discovered a segment with a 15% defection rate, we are not fully confident in any particular customer defecting. The value comes from detecting particular customer segments which are predicted in aggregate to behave much differently than the overall customer base.

Predictive analytics can be viewed as a means of making ever more accurate and educated guesses based on the data available, which means that your predictions will better fit into your statistical models, but at the same time, just because all of your data points at one particular outcome, it does not necessarily mean that this will happen in reality. Your models are built with a finite number of variables at play, whereas an infinite number of variables can compound with actual human behavior. No model can predict behavior with absolute certainty, but the more data you use, the better your models will be at predicting. That’s the power of predictive analytics.