5 ways CIOs can use BI predictive data analytics to stay ahead in 2022

There is a vast amount of data created every day Estimate suggests that there will be 175 zettabytes of data in the global datasphere by 2025. While data on its own is meaningless, when combined with the right business intelligence solutions, it suddenly becomes a goldmine of insights that can reveal trends impacting businesses and our daily lives. As data becomes easily available, it behoves organizations to tap into these insights and turn them into actionable results. However, harnessing large amounts of data to forecast trends is easier said than done.

In the past, such tasks were handled by engineers and data scientists. But with the advent of different BI tools and increasing data literacy, business leaders can easily interpret data and bring predictive analytics into their daily decision-making processes. Here’s how predictive analytics can be used in Business Intelligence to stay ahead of the competition in 2022.

The nexus between business intelligence and predictive analytics is no secret. On their own, business intelligence tools help you understand what happened in the past or is happening now. When combined with predictive analytics, it suddenly provides a glimpse into the future and lets businesses better position themselves to achieve their goals based on historical data.

Several companies use predictive analytics in Business Intelligence to study future trends in their industry. For instance, the electronic industry is constantly changing and generating a huge amount of data on what customers are purchasing, what are their likes and dislikes, or what they are responding to. Such data then empowers businesses to understand where the industry is headed and better position themselves to become the “next big thing”.

For others, discovering trends is just about applying the learnings of the past and combining them with the most recent behaviour. It lets you consider multiple factors such as recent performances and seasonality to achieve competitive differentiation and further optimize processes. Let’s look at some use cases of predictive analytics in Business Intelligence.

1. Reduce Customer Churn

Get unmatched insights into customer behaviour and proactively identify those likely to churn. Picture this, your product has implemented a predictive analytics model that identifies who is most likely to renew or abandon their subscription based on their interaction with your product. The tool then suggests incentives backed by this data and prompts the sales team to send bespoke incentives or discuss how the user can continue their subscription. In other words, predictive analytics can promptly rectify customer churn even before it happens.

2. Maintain Business Integrity

The rise of IoT has resulted in astronomical growth in digital transactions, which are often prone to fraudulent activities. To fight fraud and stay compliant (especially relevant for highly regulated industries such as Banking and Healthcare), enterprises need a robust solution that overcomes this challenge. Business Intelligence predictive data analysis could probably save companies by identifying potential fraud and proactively monitoring their service delivery channels to avoid such transactions. Scoring online transactions with a predictive model that uses the company’s previously reported fraud dramatically increases fraud detection. The more fraud is detected, the more losses are recouped or prevented.

3. Gain Competitive Advantage

Predictive analytics provides a unique and powerful differentiation to organizations with proprietary business intelligence that helps them compete for sales and customer retention. Not only that. It can help gain a competitive advantage by knowing the weakness of your leading competitors even before they do. Since the model leverages your information of previously made sales to customers who are also potentially exposed to your competitors, the modelling process easily creates customer micro-segments to distinguish who chose your product/services and who defected to competition.

4. Customer Servicing 

Improve customer service by providing the original value proposition of your solution. Predictive analytics, when combined with BI tools, can provide a competitive advantage like that of Netflix, which claims to provide quality entertainment to its viewers 24/7. The company has significantly improved its service and remained the industry leader by having its software identify the likes and dislikes of its customer based on their previously watched history. The OTT service provider then recommends shows and movies to the users and other viewers with similar preferences.

5. Improve Core Business Capacity

Predictive analytics in Business Intelligence can significantly improve your product and the rate at which it is produced. Whether industrial manufacturing or offering services, it can help advance core business capacity by building and delivering solutions with increasing effectiveness. That’s not all. Predictive analytics can also advance central enterprise functions such as supply chain, Human Resources, etc. It can optimize the supply chain with accurate inventory demand prediction, application processing, etc. Similarly, when applied in Human Resources, it can support decisions in hiring and human capital retention with a customized performance and attrition model.

Combining It All Together

Irrespective of the way your organization uses predictive analytics, the result is almost always similar. Businesses can leverage historical data to predict daily decisions such as next week’s schedule, what the users may buy, or even mitigate risks. Predictive analytics in BI always leaves you with the same outcome: better, quicker, and more efficient enterprises that offer even better customer service. To put it differently, it influences business outcomes and ultimately improves the bottom line by keeping customers happy.



Views expressed above are the author’s own.



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