Business owners usually work with information that pertains to events that have already happened, such as sales accounts after they close, expenses after spending, and employee records.
But that is not enough to make the best decisions. It will help if businesses can draw out a plan for the future. And, for that, businesses should know the future. So, for example, it will be of great help to businesses if they can figure out things, such as how many sales will be in the next year. Or how many products should be in stock to meet that demand?
Businesses should look for patterns in their existing records about past events to get the answers to such questions concerning the future, which can help them project things forward.
The process is called predictive analytics, which has many different applications, such as:
- Manufacturers can analyze past failures to formulate impactful strategies
- Manufacturers can know in advance when to service equipment to prevent breakdowns
- Businesses can analyze their best target customer group to create impactful marketing campaigns to get new customers.
Working Methodology of Predictive Analytics
Predictive analytics and forecasting can give impactful insight into future events. However, businesses can lose their confidence in badly implemented systems.
Again, there are some common processes in all systems, which are as follows:
- Businesses analyze the existing data to figure out statistical patterns.
- From the patterns, businesses create a model, which is a set of rules that shows how to apply the patterns to new data.
- Subsequently, businesses pass new data through the model and the rules
- Finally, the model and rules can predict possible future events.
Let us examine an exemplary situation to explain the matter more clearly.
Suppose you find from your existing customer data that younger consumers like products with more features, and older ones are willing to pay a premium for products made with higher-quality materials.
With this information, you can apply rules to new customer registration on your websites. For younger consumers, you can offer products with more features, and for older consumers, you can offer higher-quality products to optimize your sales.
It is essential to have high-quality data sets for good predictions. Therefore, you should check whether your data sets are accurate and up-to-date.
If your data sets are incomplete or inaccurate, you cannot expect good forecasts from predictive analytics. For example, if you have demographic customer data, you should check whether the data is up-to-date.
If you find that the data is not up-to-date, you should try to source the best data. But, again, effective predictive outcomes depend on selecting the best predictive modelling techniques. And that is the expertise of data scientists.
But things are automated today.
Automated machine learning allows data scientists to run complex statistical modeling to find the best data patterns.
Again, you will find ambiguity in your predictions. It is difficult to predict future scenarios, especially in customer behavior scenarios.
But, you need to understand the accuracy of the predictive model to use the results confidently. For example, you can find weather forecasts useful, but the predictions are rarely perfect.
You should ensure that you make the prediction with highly actionable insights.
Despite predictive analytics being a new domain, the associated statistical techniques — Bayesian analysis and regression – are more than 200 years old.
However, modern predictive analytics started after the development of digital computing in the 1950s.
From the late 1950s, modern algorithms, including neural networks, started to be developed. And in recent years, there have been notable developments in predictive analytics and artificial intelligence.
Data storage has become cost-effective due to the availability of the cloud. In addition, data has become more complex, constituting not only structured records but images, sound files and documents.
Moreover, the computational power has become more, meaning handling complex tasks has now become easier.
Also, the notable developments in the software can leverage all these developments to render building, testing, deploying and using predictive analytics more reliable than ever before, apart from the simplicity element.
Therefore, businesses can gain significantly from predictive analytics. If you have not yet used predictive analytics, you should use it.