Do you ever feel like your financial planning and analysis (FP&A) work cycle just keeps getting bigger? It seems like the more you do it, the more work there is. This is understandable, because every time you achieve a goal, new opportunities and challenges open up.
Financial planning teams in particular often feel understaffed because the work simply seems endless, as there is theoretically always a myriad of additional tasks to improve financial forecasting.
However, technology has changed the way we approach financial analysis and planning. Thanks to technology, we can perform deeper analysis, forecast more accurately and make more effective business decisions.
Any FP&A team is at different stages of this journey. Let’s take a look at the four stages of financial planning and analysis:
Phase 1: Focus Only on Monthly Financial Reports
Technology required: Basic accounting software and Excel
In the early stages, businesses often rely solely on monthly financial reports to assess their business performance. During this time, the finance team is often comprised of just a few people who undertake a variety of tasks, with limited depth of expertise. Therefore, Financial Planning and Analysis is no exception.
Typically, this stage will use forecasts based primarily on past growth trends in financial metrics: monthly financial statements, analysis of historical data, and projections of future trends.
For example:
“This year's revenue increased by 20%, so next year it can increase by another 20%.”
“This month the gross profit margin is 55% so it can be reduced by another 50%”
“Operating expenses for this quarter are 500,000,000 VND, so next quarter it could increase to 700,000,000 VND”
The analysis is pretty straightforward: how much did revenue increase, did costs change? This is the traditional approach, and there’s nothing wrong with it. However, it lacks a deep analysis of cause and effect: what happened, why it happened, and what will happen next.
Phase 2: Combine financial reporting data and operational metrics
Technology required: Accounting software + Excel + internal data
An important concept at this stage is “operating index” (operationals) – factors that drive financial results.
For example:
- Customer churn rate (ARR) directly affects revenue.
- Decreased customer software usage may result in service discontinuation.
Integrating operational metrics into financial analysis helps Financial Planning and Analysis (FP&A) teams connect data, explain forecast variances, and provide more detailed analysis.
For example:
- If a customer's software usage rate falls below 50%, there is a high chance they will stop using it within 90 days.
- Based on current data, we forecast 6 customers will churn in the next 90 days.
- However, in reality, only 4 customers stopped using, of which 3 were on the initial forecast list and 1 showed no sign of decreasing usage.
Conclusion: Customer churn rate positively impacted revenue compared to forecast.
This analysis is much clearer and more useful than a simple sentence like: “This month's sales were higher than expected.”
Combining financial data with operational data helps us better understand the causes of fluctuations and make better business decisions.
Phase 3: Integrated planning and forecasting
Technology required: Professional FP&A tools or advanced Excel
This stage creates the greatest value.
At this stage, businesses will leverage financial and analytics tools to create a more integrated planning system that links financial data to operational metrics in a more detailed and accurate way. These tools not only help connect data sources but also automate real-time forecast model updates.
This step is not too different from the previous stage – since no new data appears…
However, the power of this stage lies in the fact that you combine financial and operational data at a transactional level of detail, rather than just at an aggregate level.
In the previous stage, you had to log into two different systems to get the data… Then, in an Excel model or FP&A tool, you would pair the monthly results from each system at an aggregate level (i.e. the sum of each system's monthly results).
In an integrated planning and forecasting system, data fusion is done at the most granular level possible (transaction, customer, supplier, department, employee) so that when there is a change in the driver forecast, it is updated across all financial forecasts.
While the output of this step and the previous step may be similar, using technology to do all the complex math will save you the need to rely on analysts to edit Excel models and ensure that changes in customer usage are accurately reflected in each account in the P&L.
This integration helps the finance team reduce manual errors, save time and increase accuracy in financial forecasts. Further analysis can be performed on the detailed database. This provides valuable information for the company's strategic decisions.
However, the implementation of this tool needs to be carefully considered, especially for small businesses or businesses that are not yet stable in terms of products and markets.
Standardizing your forecast too early can be a waste of resources, especially if your product-market fit isn't yet well-established and your forecast drivers are constantly changing.
Phase 4: Machine learning and real-time analytics
Technology required: Leading planning tools combined with in-house data analytics team
This is an exciting time…
Instead of relying on analysts to send reports, mine information, and update forecasts, you can now leverage technology to do all of these things.
However, this step can only be done effectively if you already have an integrated planning and forecasting system in place (as in stage 3).
When all your data is centralized in a single analytics ecosystem, you'll be able to:
- Apply machine learning to detect patterns in data that are difficult for humans to recognize (with billions of rows of transaction data across thousands of variables).
- Use machine learning to perform multivariate financial forecasting based on hundreds, even thousands of different data dimensions.
- Detect data patterns that do not conform to normal trends (outside the range of standard deviation or normal expectations).
- Provide real-time analytics to frontline employees or leaders as they manage real-world work.
- Deploying AI chatbots allows leaders to interact directly with your data.
This is the ideal level that businesses are currently aiming for in their journey of developing FP&A technology.
However, very few organizations are able to achieve and operate effectively at this stage, and they are actually reaping huge benefits from this process…mainly because it requires a solid foundation and years of preparation.
You can't skip the stages, and many financial leaders still lack the vision to reach this stage.
As a result, many businesses fail to make the necessary investments, and finance teams remain stuck between stages 2 and 3.
Conclude
The impact of technology on your financial planning and analysis journey is clear, in order, but difficult to achieve.
Take time to understand what stage you are in your journey and what you need to do to move to the next level.
Then calculate the technology costs required to make it happen and build a strong enough vision to make it happen.
Source: Edited from Forecasting Performance
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