Predictive Analytics for Business Forecasting and Planning

Table of Contents

Introduction
What is Predictive Business Forecasting Analytics?

Growth forecasting
Demand forecasting

Use Cases of Predictive Business Forecasting Analytics
Metrics Used for Predictive Business Forecasting Analytics

Classification Metrics
Regression Metrics

Examples of Predictive Business Forecasting Analytics
Final Thoughts
Topics in Predictive Analytics

Introduction

The strength of the business entity lies in its proficient planning and effective execution of the same, but what serves as the basis of planning is of utmost importance. Over the years the planning process is dependent on past experiences and forthcoming objectives of the organization, but this does not stand enough in today’s highly competitive world. Today, the business ecosystem relies majorly on understanding the patterns and deriving accurate predictions for supporting the planning process. As a result, predictive analytics has become an integral part of business operations in leveraging progressive results and challenging the existing threats and pitfalls in the ecosystem.

What is Predictive Business Forecasting Analytics?

Predictive business forecasting refers to the process of estimating and predicting future results based on time-series data. The predictions are inclined towards major areas of business operations such as sales, revenue, resource mapping, and inventory. As a result, business forecasting has been divided into two simplest categories to fetch better analysis.

Growth forecasting

Predictive growth modeling is a way towards accurate corporate planning. It includes budget planning, resource allocation, and revenue forecasting, and goal attainment strategies. The use of big data with countless metrics makes the growth forecasting process completely worth the resources invested.

Demand forecasting

Since resources such as inventory and staff add healthy life to the business vision. Therefore, it becomes crucial to predict and understand these requirements at the earliest to ensure seamless production or rendering of services. The concept of demand forecasting helps to sincerely focus on inventory management and workforce planning. As a result, reduces inventory cost owing to delay and enhances customer experience at large.

Reportedly, 57% of the executives forecast predictive analytics to be saving 15% to 26% resource wastage of an organization in the next 5 years while serving with accurate results.

Use Cases of Predictive Business Forecasting Analytics

The potential of predictive analytics still remains unexplored in various business segments allowing only few enterprises to outshine. Thus, it is the time to understand the diversified perks of business intelligence to stand firm in the competitive and dynamic business sphere.

Use Case	Description
Predict Customer Behavior	Predictive business analytics helps to determine the changing taste and preferences of the business king, the customer. It becomes possible with the use of business intelligence, AI-driven software, and the previously available data too.
Financial Budget Planning	Accurate data-driven forecasting allows organizations to coordinate well with each department. Being followed by the planning process, proper budget allocation ensures the seamless working of all organizational segments. Thus, predictive analytics help to define healthy budget scenarios.
Workforce Planning	No organization works in isolation with skilled staff, therefore it is crucial to map their requirements in advance. It helps to remove disguised employment and shortage of skilled resources too. Certainly, it all begins with the right prediction and such predictions come from Predictive Analytics.
Inventory Management	Oftentimes, inventory goes underutilized or overutilized in an organization leading to the need for streamlining the overall inventory management. Thus, a prediction drawn from data analytics clearly defines the inventory quantity that fetches quality making it the cost-efficient tool.
Establishing Goal-Oriented Strategies	Every business operation revolves around some pre-determined goal backed by accurate forecasting. Business forecasting analytics serves as the basic tool to frame powerful strategies and add strength to business operations. As a result, each healthy step towards operations leads to long-term goal achievement.

Metrics Used for Predictive Business Forecasting Analytics

Generally, organizational performance is evaluated based on two broad categories – classification problems and regression problems. The choice of any of these metrics depends on the organizational type, work models and goal of an organization. Some of the widely used metrics under the two-mentioned categories are as follows.

Classification Metrics

These metrics define the category where various business activities fall into.

Metric	Description
Percent Correction Classification (PCC)	It helps to measure the overall accuracy of the business operations considering all types of errors to be held equal weight.
Area Under ROC Curve (AUC-ROC)	The widely used evaluation metric works on the ranking model. Here, the positive predictions are ranked higher than the negative. Most of all the performance of the ROC curve remains unaffected by the change in the proportion of responders.
Confusion Matrix	The matrix measures operational accuracy by differentiating between the types of errors. Hence, this technique helps to summarize the performance of classification algorithms in Machine Learning.
Lift and Gain Charts	Being considered as the most prominent method, it measures the effectiveness of the business model. It is done by comparing the ratio between obtained results, with or without the use of the performance evaluation method.

Regression Metrics

These metrics focus on predicting the precise quantity based related attributes.

Metric	Description
R-Squared	It indicates all quantities of variables predicted out of total variables. Furthermore, it does not act partially towards the data type. As a result, a good model holds a lower R-squared value whereas an unfit model bears a high R-squared value.
Mean Square and Median Error	These error results provide the difference between the actual and predicted value and balance out the outliners present in the data.
Average Absolute Error	Like average error, it refers to the use of absolute value for establishing a balance between the differences that arose.
Median Absolute Error	It represents the average of all absolute differences between the prediction and actual results. Here, the differences have equal weight and bigger outliners making the final evaluation highly effective.

Examples of Predictive Business Forecasting Analytics

Predictive analytics is emerging as the hot commodity in the business ecosystem owing to its extended operational use cases in all industrial types. As a result, it enhances organizational efficiency while improving the speedy penetration into the market. The widespread adoption of predictive business forecasting analytics has revolutionized the business ecosystem multiple folds more than it was predicted.

Fintech, being the early adopter of Artificial Intelligence has managed to respond rightly to the changing market conditions and consumer behavior. Alongside, the improved demand forecasting ensured the incorporation of easy transactional protocols into the system. As a result, it helped to avoid the excessive dependency of customers on cash.
Puma has put esoteric algorithms and techniques to use and revolutionized itself into a digitally advanced brand in a short span of time.
An Irish Bank has increased its campaign response rate by 50% and observed a reduction in customer acquisition cost by 14 %, only with the use of predictive analytics. Thus, overall 20% of campaign ROI was earned.
Telco is again an early adopter of AI-driven technology and has experienced a higher degree of accuracy in developing market insights. As a result, it strategized its own innovative marketing and business plans to ensure customer retention.
MIT Center for Transportation and Logistics incorporated the cause-and-effect technique of Machine Learning and was able to transform business forecasting and analytics into a progressive direction.

Final Thoughts

Recently predictive analytics has become a prominent choice of big industrial giants and helped them pave the way towards better business opportunities by addressing the needs of major stakeholders. A future-focused agile approach has increased the success rate and made handling cost a pocket-friendly one. Predictive analytics not just forecast the future trend but let organizations understand their complete potential and possibilities too. Thus, it assists in developing a notion of brand identity in the near future.

Topics in Predictive Analytics