Top 5 Areas where Predictive Analytics making an Impact..!

If you can predict the future, you can capitalize on it. This is the adage being followed by the organizations making the cultural shift towards becoming more data-driven. Of course, now it is not just about being data-driven. The need today is to be able to capitalize on the future by leveraging predictive analytics.

Let us explore the areas in the modern enterprise where predictive analytics has already made an impact.

1. Planning & Budgeting

One of the primary areas where predictive analytics is being used widely is Planning, Budgeting & Forecasting. Since the financial accounting book has been rediscovered as a strategic tool most organizations have been using this to strategize. These records have a lot of data hidden in them which can be looked into and used to predict the future. For e.g. a simple analysis of the Accounts Receivables can provide a sneak peek into the cash flow of the next quarter. Data can also be used for demand forecasting. Manufacturing firms have started doing just this for production planning based on demand forecasting driven by financial data. Similarly, in the banking and financial sector, data is being used to increase profitability through better planning. For example, identifying probable bad loans or NPAs can be done by using predictive analytics. Banks are hugely affected by the direction the economy or industrial landscape takes. Using predictive modeling they can understand which situations to stay away from, the conditions to prepare for, and when to get their skin in the game to maximize chances of success.

2. New product research

Predictive analytics is behind many of the new products of product features organizations are building today. Knowing what the market is likely to need, even before it says so, helps these companies tweak, break, or disrupt their own product or service offerings to stay relevant. In an example relevant for these COVID-hit times, look at how the pharma industry leverages predictive analytics to build new drugs. Drug research and testing is a core expertise needed in the pharma industry. Companies have to come up with newer drugs with better efficacy. But this process is mired in its own set of problems including a high failure rate, extreme cost, and long cycles. But predictive analytics can help in working around these issues. One major concern is how will the molecules behave once inside the human body. Algorithms and mathematical simulations can give a fair idea of that to the researchers. Similarly, for every drug trial, one needs to identify a type of potential subject who can be tested upon for the human trials. The algorithm and also the subjects’ past medical history can help in identifying the optimum fitment of the subject for the drug to be tested.

3. People processes

Now predictive analytics has made its way into talent management for companies. Retaining and motivating the right talent has become the key to success. Predictive solutions can help identify the right talent from the market and also retain them once hired. Based on the kind of people doing well in the organization, a behavioral chart can be created which will help identify and predict which kind of candidates will fit in, stay, and grow in the organization. Similarly, those tools can also identify which candidates might jump ship from the organization to a competitor organization. Text and sentiment analysis is done to understand which employees were super engaged and may have become less so. Steps can be taken based on the analysis to engage the now disinterested employee to retain such top talent.

4. Resource Utilization

Asset management & monetization is another important aspect in the operational success of organizations. Doing this well delivers double benefits. It can not only save costs for the organization but also open up newer avenues for revenue generation. Predictive maintenance is already widely applicable in this space. For eg., many manufacturers are not only using it on the shop floor but also on the products they manufacture. Intelligent systems, based on predictive algorithms and health data, can determine which equipment might need some maintenance. This can drive proactive maintenance. That saves time, effort, and money. Of course, reduced downtime is also a key benefit. The same can apply in the context of equipment being used by customers. For instance, if an elevator manufacturer can detect that an installation might be on the verge of a breakdown then it can go ahead and repair the part beforehand. These are also interesting real-life use cases emerging involving asset utilization. Organizations that own a lot of heavy machinery like cranes, earth movers, etc., have started applying analytics to predict the idle time for those assets and then leasing them out to other organizations. This ensures a revenue stream even during the lean period.

Customer analytics

Business strategy is driven by data. The critical aspect of customer segmentation is now driven by predictive analytics. There is a lot of data available internally (like CRM, etc.) as well as externally (social media) which can be pulled in and analyzed. This helps not only in predicting how the customer will react in a certain scenario. These insights are also becoming the input for new messaging and marketing campaigns that are more likely to resonate with the prospects. Lexus, in fact, went a step ahead and created the world’s first AI scripted advertisement designed to appeal to the target audience. At the macro and micro level, targeted campaigns with the correct customized content are improving conversion rates and impact.

There is little doubt that predictive analytics has already become a mainstay. Functions from finance to marketing and operations are leveraging the predictive possibilities of data to great effect. Needless to say, this is playing a crucial role in impacting the bottom-line. This is the future. And the future is already here.