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Financial Planning and Analytics

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Our Forecasting solution provides qualitative and quantitative analysis of time series data and external factors, using econometric techniques to accurately predict future outcomes. Our experienced data and analytics team can help you understand the trends and patterns that will shape the future of your business. Senior consultants work closely with your business experts to understand your market and market dynamics, model key factors as parameters in our algorithms and ensure a highly custom and accurate forecast for your needs.

Forecasting is the process of predicting future outcomes based on past and present data. It is an essential aspect of modern business that helps companies make informed decisions, mitigate risks, and plan for the future. However, forecasting can be challenging, especially when very little data is available or when external factors influence the outcome. This blog post will explore some of the most effective forecasting techniques used by experts to overcome these challenges and provide businesses with accurate and reliable predictions.

When very little data is available, combining qualitative and quantitative forecasting can help produce more accurate predictions. This approach involves using expert opinions, surveys, and other qualitative data to fill in the gaps in the quantitative data. By combining these two methods, businesses can gain a more complete understanding of the market or situation they are forecasting, which can lead to better decision-making.


Time series analysis is a widely used method of forecasting that involves analyzing historical data to identify patterns and trends that can help predict the future. Exponential smoothing is a variant of time series analysis that focuses on identifying trends in data that may not be apparent at first glance. This technique can be particularly helpful when dealing with data that has a high degree of noise, making it difficult to see long-term trends.


Moving averages are often used to smooth out issues in data reporting and collection that can arise due to seasonality or other factors. This technique involves calculating the average value of a variable over a certain period and using that as a predictor for future values. By smoothing out short-term fluctuations, moving averages can provide a more accurate representation of long-term trends.


Modeling of causal factors involves identifying the external factors that may impact the outcome being forecasted and modeling how they will influence the result. For example, if a business is forecasting sales revenue, they may consider factors such as changes in the economy, new competitors entering the market, or changes in consumer behavior. By modeling these factors, businesses can gain a better understanding of the potential risks and opportunities associated with a particular outcome.


Basic principles or bottom-up model building involve starting with the details of a specific situation and working up to a broader forecast. This approach is often used in complex situations where many different variables may impact the outcome being forecasted. By starting with the details and working up to a comprehensive forecast, businesses can gain a more accurate picture of the potential outcomes.

Econometric analysis is a statistical approach to forecasting that involves analyzing economic data such as GDP, inflation rates, and interest rates to predict the future. This method can be useful in situations where external economic factors play a significant role in the outcome being forecasted. Econometrics principles also assume that the output of an activity can be measured by understanding the varying weight each activity or task has on the outcome. Promotional response analytics is one such way of applying Econometric analysis methods to estimate the sales outcomes of promotional efforts & investments.

Bayesian priors involve using external data and assumptions to model the outcome being forecasted. For example, if a business is forecasting sales revenue, they may use data on consumer spending habits, population demographics, and other factors to help predict future sales. By incorporating external data and assumptions, businesses can produce more accurate forecasts that take into account a broader range of factors.


Forecasting accurately and iteratively is an essential aspect of modern business, but it can be challenging, especially when there is little data available or when external factors can impact the outcome. By using a combination of techniques and strategies, businesses can produce more accurate and reliable forecasts that can help them make informed decisions and plan for the future. 

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