The success of any organization depends on its vision and its ability to shape the future course of action with unmatched accuracy and timeliness. Wouldn’t it be great if this prediction part is made efficient, easy, and streamlined for your employees which could ultimately lead to better fortunes!!
Predictive Analytics is one such technique that holds the potential to raise your business up-ahead from the market competition.

Basic Building Blocks

Predictive Analytics is a mechanism by which we can predict the outcome of unknown events pertaining to our business.
Speaking of the analytic process at a high level, it starts by training the model by identifying patterns and trends on the business historical data. The best-fit model will then enable us to predict human behavior on existing dataset by generating predictive(or probability) scores of the variables under consideration.
An important point here is that the level of precision in prediction highly depends on the amount of data and quality of assumptions.
There is one more term – forecasting, which is mistakenly used interchangeably with prediction. However, there are few noticeable differences when compared to each other which is covered in the following section.

Prediction vs Forecasting

On the one hand, Forecasting is the calculation of future values of various target parameters through numerous mathematical formulas and techniques. It is useful in finding out answers involving large sets of data i.e. forecasting can let you know:

How many active users your company website will have at year-end.
On the other hand, Prediction helps us find the probability scores of target variables based on patterns and trends observed by the prediction model. It is primarily performed on the individual level rather than subject groups. For e.g. we can predict:

What an active user is more likely to do on a website when he sees a particular advertisement.

Techniques of Predictive Analytics

The analysis can be conducted using techniques which can be broadly categorized into:

• Regression Techniques: These are used to establish the relationship between a dependent(target) and independent (predictor) variables. Here, the best fit model is the one that has a maximum overlap of the target data points on the predictor curve.
• Machine Learning Techniques: Machine Learning is considered as the ability of the computer to learn and process data in a certain manner based on past experiences.

Areas of Application

Although there are endless scenarios where Predictive Analytics can be fruitful, we are just scratching the surface by listing down few day-to-day use cases:

• Improvement in Sales Process A more predictive approach in the Sales process can be beneficial in many ways such as quick closure of New Opportunities enhanced focus of efforts by reps on more likely to convert deals, etc.
• Customer Relationship Management (CRM)  Build a solid connection with the customers by predicting their behavior trends towards a certain line of products or services.
• Fraud Detection Certain patterns found in organizational data can prevent fraud in the future transactions.
• Stock Market Analysis  Build your portfolio based on market behavior in the future.
• Medical Diagnostics Find out what could be the potential areas of importance in years to follow based on consumer behavior.

How can you adopt predictive learning for your organization?

We at Mirketa have been consistently involved in shaping the lives of our customers for more than a decade. We have successfully transformed and implemented business solutions using big data and cloud technologies, including Salesforce.com, Microsoft Dynamics, Marketo, and many more. We also provide customized CRM solutions utilizing Predictive Analytics and Machine Learning as we understand the relevance and impact of such advanced techniques in this fiercely competitive era.