Just as we discussed previously How Data Analytics impacts the decision making power of CEOs. Similarly, Predictive Analytics will help redefine CXOs make decisions in their firms to gain business success and become more efficient.  The use of machine learning models and statistical techniques is the first step in making an informed decision about where they will be spearheading their organizations. Rather than relying on intuition, they will able to visualize the impact of their past decisions and set better quarterly and yearly goals.

 

Eric Siegel came up with this concept in his book “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” and he said: “As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.”

 

The most important thing to keep a note here is that machines soon will be able to predict what you will buy, like and the time you will be living on earth. It’s predicted that this segment alone will disrupt the market and by 2024, predictive analytics will reach $22.5 billion in value.  

 

One of the examples of where predictive analytics will play a major transformation role is in the retail sector where you can use transactional data from the Point of Sale (PoS) to make decisions regarding the growth of your product. Retail stores house a wealth of information that can be used for marketing, branding and placement purposes by FMCGs.

 

CXOs need to understand the impact of their previous decisions, and that can only be done if we have well-defined metrics on which we are benchmarking our goals. If a company was projected to achieve a 5% growth in revenue, but it didn’t, that means we need to outline the decisions which prevented this from happening and not retake them in the future. The existing data and information in which each organization has a need to be curated and put in a form for analysis and visualization for future strategies.

 

Here are the steps needed to be followed by CXOs to start a predictive analysis of their organizational targets:

Collect Data and Observe Patterns

The first and foremost step of starting in any organization is to start collecting data from each vertical and note down their conclusions step by step. The compiled datasets will help CXOs make an informed decision once the data is sorted and observed for patterns. For instance, analytics data of a website can be used to make predictions regarding which time there will be maximum readership on a site. When this time has been figured out, we can deploy various strategies to maximize revenue generation from the users on the domain. Similarly, different strategies can be applied as per the needs of the organization.

Predict outcomes to decisions

Just like we have A/B Testing of our software development cycles or marketing campaigns, we can also visualize the outcome of a certain decision that we take in our organization. Thanks to Machine Learning algorithms, we can devise a strategy to train a model to make predictions regarding the type of decisions that are expected to be taken by CXOs. For instance, a machine learning model could predict the impact of raising the price of a product on revenues and customer satisfaction. 

 

The advantage of using this is that instead of going into the wild and not knowing about the outcome, we can have hindsight about the impact of our decision and take appropriate measures in case there is a chance of a rebuttal or loss. There have been many cases where machine learning models have helped improve the organizational revenue and workflow simply because our everyday computers can process more data and perform more computations than human beings.

Conclusion:

Machine Learning, Deep Learning, and Big Data are the future for all segments in this competitive landscape. Only those organizations will survive who make accurate predictions and informed decisions through the use of these advanced models like predictive analytics regarding their yearly or quarterly targets.