Financial Planning & Analysis (FP&A) upholds enterprises’ financial well-being by providing timely and accurate financial analysis. However, traditional planning strategies require FP&A teams to keep collecting and validating data for generating accurate financial forecasts rather than spending time on high-value activities.
A traditional forecasting process that relies on outdated technology leads to inaccurate results and delays the decision-making process. As per an SAP survey, FP&A teams spend just 23% of their time on high-value activities.
In today’s modern financial planning environment, predictive planning can support FP&A teams in taking accurate and appropriate decision-making steps swiftly to meet the organization’s long-term financial objectives.
In recent times, predictive planning has emerged as one of the most crucial FP&A trends. However, the turbulent times brought around by COVID and the unpredictability of world events caused by it showed all of us the apparent weaknesses in the current FP&A processes and systems.
From a business owner’s point of view, planning things based on a single scenario is just not enough. Instead, predictive planning is necessary to understand these uncertainties in a much better manner and prepare for risks and opportunities in the future.
What is Predictive Planning?
Predictive planning leverages advanced data analytics methods to create predictions about future events and outcomes using predictive analytics. It is a powerful, data-driven approach that supports overall planning and executive decisions.
The strategic planners and financial analysts surpass traditional planning methods as they forecast future outcomes by utilizing historical data cycles through machine learning algorithms. This ability to “see into the future” enables FP&A teams to accurately forecast mission-critical efforts that impact the business, such as:
- Sales trends
- Consumer behaviors
- Supply and demand
Predictive planning helps your FP&A teams avoid things that may result in lost opportunities and, instead, remain focused on building better business outcomes. Evaluative capabilities of predictive planning enable you to take proactive steps to survive, sustain, and scale in a demanding and fast-moving business environment.
Predictive Planning Benefits
Predictive planning is a dynamic and versatile planning module that considers all the relevant variables to provide fact-based analysis, enabling you and your finance teams to gain the upper hand in administrative processes and decision-making.
Reduce Time: Automated analysis and advanced machine learning algorithms save a lot of time and resources by analyzing, comparing, and converting data in real-time. Moreover, it allows you to carry out agile finance predictions by utilizing the complete potential of real-time analysis.
Reduce Cost and Effort: Predictive planning approach includes cost-effective planning and sourcing. It also minimizes manual data collection and analysis efforts.
Accurate Forecasting: Predictive planning comprises systematic mathematical and statistical analysis, which provides an accurate picture of financial data without any blind spots. By analyzing historical data and trends, predictive analytics creates fact-based, reliable sales forecast models that support decision-making for financial experts.
Campaign Planning: Consumers these days have more choices than ever before. Analyzing critical customer data such as purchasing patterns, buying behaviors, web browsing, and social network interactions can enable you to identify the perfect time and channel to launch a successful campaign. Accurate forecasting helps in avoiding wasteful spending and elevates the efforts of your marketing teams as well.
How to Get Started with Predictive Planning
Carrying out predictive analytics correctly is a necessity for generating relevant FP&A insights. Below are some of the critical factors to consider before getting started with predictive planning.
- Define the Need
Start by defining the need/requirement for the forecast and required enterprise objectives. Correctly establishing financial goals will help predictive analytics solutions deliver meaningful and actionable insights.
- Determine Data Volume
The second step is to determine the volume of data required to perform the required analysis. Granular data requires more processing time for generating insights. Therefore, it is best to start with aggregated data that includes enough details to predict trends and seasonality.
- Cleanse Your Data
The quality of data directly affects the reliability of forecasts. Therefore, it is vital to cleanse your data and remove duplicates to maintain data quality and consistency. Siyana’s business intelligence services include data cleansing features that help you maintain data consistency.
- Select a Forecast Horizon
When forecasting, it is crucial to choose an optimal forecast horizon for accurate predictions. It is better to start with a forecast for a month and gradually review the accuracy of other months.
- Choose a Reliable Model
The next essential step is to pick a reliable predictive model for accurate analysis. Siyana Info Solutions can help you build your own model or provide you with various advanced tools to automate this process.
- Test and Validate
After Identifying the correct model for your analysis, the next step is to test and validate it to identify weaknesses in the model. Validation methods like cross-validation help in making the required rectifications to balance the bias and variance of the model.
At Siyana Info Solutions, we have extensive experience and expertise in forecasting, progressive and automated planning, budgeting, and reporting solutions. We provide practical and strategic guidance to equip your FP&A teams with tested and validated data strategies that lead your business in the right direction.
