Predictive Analytics Importance Of Business Industry
In business, the data we collect and the information we work with mostly relate to events that have already happened. We have accounts of sales after they close, of expenses after the spending, and employee records concern the people we have already hired. But naturally enough, we want to know the future. How many sales will we have next year? And how many products should we have in stock to meet that demand? To answer these questions about the future, we can look for patterns in our existing records about past events and project them forward. We call this process predictive analytics consumer behavior.
What are different applications for predictive analytics?
This forward-looking form of predictive analytics has many different applications.
- Manufacturers can analyze past failures and predict when to service equipment to prevent future breakdowns.
- Marketers can analyze their best accounts and create campaigns that target similar people in the hope of gaining new, high-quality, potential customers.
- E-commerce sites which tell us people who bought this also bought that: We have all been tempted, have we not?
How does predictive analytics work?
At its best, predictive analytics can appear to be deeply insightful about future events. On the other hand, badly implemented systems can quickly lose our confidence. Nevertheless, all systems have some processes in common, and some common keys to success and failure. Here’s the basic process:
- You analyze our existing data to learn statistical patterns.
- From those patterns, we create a set of rules a model which describes how to apply the patterns to new data.
- You pass new data through the model and the rules make predictions about what may occur in the future.
For example, you may analyze existing customer data and find that younger people like products with more features, but older buyers are willing to pay a premium for products made with higher-quality materials. From these patterns, we can apply rules to new customer responses as they register in our system. If younger, you may successfully offer them more features and if they are older, higher quality products. In this way, we hope to optimize your sales.
Four important keys to successful predictive analytics
- Most importantly, good predictions rely on good data. If your current records are incomplete or inaccurate, you can’t really expect predictive analytics to make good projections. For example, do you have demographic data about your customers, and if so, is it thorough and up to date?
- Good future outcomes rely on choosing the best predictive analytics modeling techniques when looking for patterns. There’s a certain art to this, which forms part of the data scientist’s expertise. But today, predictive modeling uses automated machine learning which can run quite complex statistical modeling experimentally on its own to find the best practical results.
- Ambiguity is inevitable in predictions, and we need to learn to work with imperfect results. We cannot predict the future with certainty especially when it comes to customer behavior. You need to understand the accuracy of model and with how much confidence we can use its results. All this may sound challenging, but you do it all the time, for example, with the weather forecast, which is generally accurate enough to be useful, but rarely perfect.
- The predictions made should be actionable insights. That is to say, you should be able to do something useful with the prediction analytics and also be able to test in the future if the prediction turned out to be accurate enough to be helpful.
What is new in predictive analytics?
Predictive analytics perhaps sounds very new. Not really. Some of the statistical techniques Bayesian analysis and regression have been around for over 200 years. Nevertheless, contemporary predictive analytics really took off with the development of digital computing from the 1950s when modern algorithms, including neural networks, started to be developed. Today, businesses can collect data along with every point of the customer journey. This information might include mobile app usage, digital clicks, interactions on social media, and more, all contributing to a data fingerprint that is unique to its owner.
We have accounts of sales after they close, of expenses after the spending, and employee records concern the people we have already hired. But naturally enough, we want to know the future. In recent years, however, there have been very significant improvements, leading to both simpler everyday analytics and advanced artificial intelligence.
Where is predictive analytics used?
With this new power and these new capabilities, predictive analytics can be found in an ever-growing range of use cases and industries. Here are some examples.
Financial services. Predicting stock prices and other financial indicators is an important practice. However, banks, mortgage lenders, and credit card companies also want to identify fraudulent transactions, offer the best rates to their best customers, and sell new financial products to new customers. In all these cases, predictive analytics proves its value.
