Enterprises often rely on predictive analytics to assess risks and pre-empt the profitability quotient. While the crusade was initially started by the likes of Amazon and Google, companies, regardless of their size and spread, have grown a liking for predictive analytics for gaining a competitive advantage against their contemporaries. Moreover, the proliferation of Big Data has further contributed towards this inclination.
Understanding Predictive Analytics
Before we move further into this discussion, it’s only appropriate that we clearly understand the basics of predictive analytics while analyzing its impact on varied financial aspects. Predictive analytics and the associated tools clearly segregate and extrapolate the existing data sets while amalgamating the insights with historical data.
This allows companies to make reliable assumptions and predictions from a contextual point of view. Moreover, Predictive Analytics also helps create a highly intuitive business model for enterprises, inclusive of third-party insights, risk analysis and opportunity identification.
How Predictive Analytics Functions?
To begin with, predictive analytics identifies a specific business model while pairing the same with relevant data sets. The next step involves collecting historical inputs, in order to prepare an extensive analytical framework. When it comes to measuring financial performances, Predictive analytics also takes certain organizational variables into account while clubbing the same with statistical strategies and machine learning principles.
What Enterprises must understand while using Predictive Analytics Tools?
Those who consider Predictive analytics as a standalone tool for combating business challenges are in for a disappointment. While the associated tools are extremely powerful in enriching the overall user experience, predictive analytics basically works as a supplement for improving the overall process of decision making. Moreover, it helps financially competent organizations strengthen their foothold by extrapolating certain business moves while contemplating a supposed future for them. Therefore, the underlining principle of Predictive Analytics is to allow companies make more confident decisions.
Analyzing the Business Case
It is important to know that the financial statements are necessary for companies to make informed decisions. However, more often than not, the information sets are only descriptive in nature and limit the entire decision making process. With the marketplace becoming extremely competitive, it is essential that enterprises start focusing more on selective predictive analytics tools.
Based on a report released by the Data Warehousing Institute, almost 86 percent of companies only make use of 52 percent resources and a mere 34 percent of the existing predictive analytics tools.
Although the popularity is increasing with each passing day, there is a lot more to predictive analytics than mere financial applications.
Using Predictive Analytics for Improving Financial Performance
Needless to say, economy is on an upswing and it’s only a matter of time that finances are used for driving the all-important organizational engines. The BFSI circuit, therefore, needs to rely on predictive analytics for accelerating cash-to-cash cycles and accentuating business growth.
According to a survey conducted by Forrester, almost 60 percent of enterprises consider revenue generation as the foremost business initiative.
Defining Case Study and the Associated Goals
The BFSI arena uses predictive analytics in a slightly different manner. Firstly, the concerned firms conduct preliminary predictive analytics tests in order to expose the valuable data sets pertaining to the modeling software. Secondly, the results are then applied to the tangible organizational metrics for offering relevant information regarding customer preferences, behavior and other metrics.
The Concept of Sales Forecasting
Predictive analytics helps companies with their financial performance, especially for improving the accuracy of the sales forecasting process. The associated tools help uncover certain organizational trends which may facilitate increased profitability. That said, the procured insights can easily help predict the revenue model by analyzing the existing sales pipeline.
Startups and small businesses usually have minimal historical data to fall back upon. In that case, the predictive models rely on cohesive industry figures, economic indicators, financial performances of the competitor and even market demographics.
Predictive Analytics help Optimize Strategies
Financial performances are directly related to the company bottom lines and it is important to implement predictive models to them in order to maximize ROI and marketing investments. Therefore, predictive analytics tools can be deployed for communicating better with the marketing specialists by collecting essential data sets. Financial performances can be duly improved if companies start collecting data from website analytics, email campaigns, social media searches and a host of other avenues. Predictive analytics, therefore, makes way for promising leads which in turn allows organizations to publicize their products and services into a more exhaustive market.
Last but not the least, predictive analytics help companies zero in on the best possible campaigns for their business, clubbed with personalized messages, effective communication and better financial outreach.
According to Tom Davenport, Co-Founder, International Institute for Analytics, “Predictive Analytics have the ability to completely transform the concerned industries while improving the financial functions associated with each one of them”
While predictive analytics can indirectly improve the financial performances, it directly assists in the process of customer retention. This, in turn, converts the first time sales into recurring purchases; thereby amplifying the revenue generation process. At first, the predictive model takes all the customer purchases into account while gauging the extracted data regarding products and services. The next step involves taking a closer look at the geographic and even demographic information for formulating a specific score that eventually helps companies with the customer retention process. Successfully deploying a predictive intelligence model also helps an organization with campaign selection which is also instrumental when it comes to improving the financial performances.
Retaining the Employees
Predictive analytics, when applied to the employee recruitment model, helps companies determine the ones which might leave the organization. That said, companies which focus on increasing the employee turnover are better off when it comes to proactively improving the existing financial conditions. This approach involves looking into the socio-demographic insights while amalgamating the same with time-data predictive models.
Maximizing Return on Assets
Companies in the BFSI industry need to focus primarily on the return on assets and strategies to maximize the same. A predictive model, therefore, improves capacity utilization and minimizes the instances of failure and equipment maintenance.
Mercedes Benz used a predictive model way back in 2012 for speeding up the testing process of the engines while optimizing overall production.
Financial performances can be best improved if an organization deploys predictive analytics tools for uncovering the existing synergistic opportunities. A predictive business model also comes in handy if companies are looking for a geographic expansion in order to identify supply chain metrics, competitive challenges and other forms of workforce whereabouts.
Can everybody use Predictive Analytics Tools?
Predictive analytics significantly improves the financial performances of the companies, regardless of the concerned sector. However, the BFSI industry is making the best use of the same courtesy the decision making flexibilities on offer. Every business can therefore reap the benefits of the real-time, analyzed and reliable data sets, derived from a predictive analytics model.
That said, before talking more about predictive analytics as a whole, it is essential to know more about the financial implications and management requirements, pertaining to the existing enterprises. Put simply, financial management is one aspect which offers readable insights regarding an organization’s business operations and processing capabilities. Analytics can therefore improve the processes associated with planning and monitoring while making use of the comprehensive datasets facilitating automation.
How Financial Performance can actually be improved?
With predictive analytics on-board, businesses need not worry about the existing budgeting routines. Instead, anything and everything related to budgeting can be easily automated. Moreover, the algorithm with analytics overriding the same can be used to automate budgeting and other associated tasks. Therefore, predictive analytics can easily be considered as a tool that can amplify the advantages associated with financial management while improving the same in the best possible manner.
Apart from automated budgeting routines, predictive analytics helps create real-time scenarios for the concerned enterprises. Strategy-specific reallocation is one aspect that can be leveraged by marketers for improving the company outlook. Moreover, the analytics tools help improve the existing decision making process while offering an extremely reliable starting point for the finance professionals and managers alike.
Making Use of Predictive Analytics
Companies which are looking to leverage the deployed predictive analytics for financial management must be ready to compare and examine different marketing relationships. In addition to that, the strategies can also be applied to routine budgeting procedures. Additionally, predictive analytics is best used to predict differing decisions and selecting the one, best suitable for the business.
Can Businesses Actually Benefit from these tools?
While we all have different perspectives towards predictive analytics and the associated implementations, it actually has the power and competence to improve the financial management systems of the associated firms. Technologies like simulation and visualization underline predictive analytics, which in turn help organizations measure the existing key performance indicators or the KPIs. Based on the inferences drawn by the static aggregates, predictive analytics tools assist companies in drawing detailed assumptions which eventually determine the profitability quotient.
A practical example of predictive analytics at work would be general managers from select SMBs trying to read into multiple transactional records of customers, depending on the purchase history. Once the delivery dates, delays and other types of analyses are drawn out with a predictive model on-board, it becomes easier for the concerned SMB to streamline exports and minimize the lag time, if any. This in turn, might help increase the on-delivery time by a significant proportion.
How Comsense can make a Difference?
In the age of predictive modeling and analytical approaches, Comsense uses the high-end robust IBM PCI platform for improving the financial aspects of organizational growth. The existing PCI module takes the interactive data into account while adding the necessary descriptive sets to the same. Although the comprehensive PCI model involves predictive analytics, campaign automation and even multi-channel interactions, we would primarily concentrate on analytics with the sentiment models, cross-sell models and price sensitivity models in the frame. With Comsense on-board, enterprises can acquire a personalized perspective towards marketing while being able to retain customers in a highly organic manner.
Predictive analytics, when paired with functional data availability, can readily help organizations with better decision making. While the cost of making calls or other marketing endeavors might be slightly hard to presume, analytics can easily contribute towards higher organizational efficiency and improved profitability. Apart from that, majority of commercial issues and real-time problems can be easily dealt with if companies start making the best use of predictive analytics.
However, enterprises must also realize the fact that even though predictive analytics tools help improve financial performances, they should not be the only metric for gauging customer behavior and other company-specific variables. A predictive model with multiple case studies is probably one of the most innovative actuarial models which helps fine-tune the technicalities of financial management.
Predictive analytics allows businesses to zero in on the most important organizational factor for improving business potential. Once the factor is determined, businesses streamline the approaches and come through with more concrete plans for improving efficiency.