April 29, 2021

How to get started with Market Forecasting

Market forecasting is an integral part of every business decision. To make the best possible decisions, you need to have access to accurate market data. Even though there are many tools that are available for this purpose, most people are still reluctant to use them. This article will guide you through the steps that are required to get started with Market Forecasting.

Many people are looking for ways to make more money, and they often turn to Market Forecasting. Market forecasting is a way of predicting how much demand will be in the future based on recent trends. Market forecasting can help you predict what products will sell well at different times of year. 

As a business owner, you need to be prepared for the future. Market forecasting is one of the keys to staying ahead in today's competitive marketplace. Market forecasting can help businesses determine what products they should produce, how much inventory they should have on hand, and who their target customers are likely to be. Market forecasting helps take some of the guesswork out of your sales forecast process by providing useful data that can be used as part-of your decision making process.

So what is Market Forecasting and Why do you need it?

If you are looking for a way to make more money, Market Forecasting might be the answer. Market forecasting is a technique that allows business owners to predict what will happen in their industry or market with high accuracy. Market Forecasting can help businesses take advantage of future opportunities and avoid potential problems before they arise. 

A common definition of market forecasting is the collective effort to predict economic phenomena in the short, medium or long term. These phenomena are typically represented by a series of data that can be either financial or nonfinancial in nature. However, most people think of market forecasts as predicting price movements over a given time frame. This is most often expressed as a prediction of where the market is headed. In other words, future prices are estimated or predicted by an analyst. These predictions can be made by looking at historical prices and establishing patterns that have been useful in predicting future price movements. Historical data may include inventory levels, sales history, future projections, current economic conditions and related externalities such as weather forecasts or political events.

Why do you need it?

People typically do market forecasting for one of three reasons. 

  1. The first is to make important business decisions, such as where and how much you should invest in new products or markets. Market forecasts are especially helpful when they point out changes that will affect your company’s position relative to competitors. This kind of information can be vital if the competition gets into new product areas; it may change which customers you want to target with promotions or price reductions, among other things. For example, let's say Company A knows their competitor has just introduced a line of very similar household cleaners at half the price of what Company A charges for its own brand—the forecast would show this development so management could decide whether or not to lower prices also (and by how much).
  1. The second reason is to aid in marketing decisions, such as how many units of a new product should be produced. Other important issues that demand forecasts include finding out whether or not you can afford to introduce the product at all, deciding on production capacity for future periods, and establishing inventory levels. By knowing what kind of demand they are likely to face if prices change (or other conditions shift), managers will have more realistic expectations about their companies' capabilities when planning budgets.
  1. The third reason is that Market forecasts give companies an idea of what the market looks like now and in the future so that there aren't any surprises later on when making decisions about things like product development, pricing, etc.  Market forecast data shows where costs may increase or decrease if certain changes occur such as new regulations/taxes being put into place or environmental factors changing (i.e., oil prices). 

As with all forecasts, remember that these figures come from historical trends rather than exact predictors of what will happen in the future. Market forecasts are often used for making decisions about things like production, pricing, product development and more so these figures can give you an idea of where your business stands now as well as how it might change depending on certain variables over time.

How to get started with Market Forecasting

Market forecasting is an important part of the business, but sometimes understanding how to get started can be quite challenging. Market forecasts are never perfect and they will always change over time, but there are some steps that you can take to make sure that your forecast is as accurate as possible.

First let's talk about the tools and techniques used in Market Forecasting

Most market forecasters use spreadsheet programs such as Microsoft Excel or Apple's Numbers for basic data analysis and charting. More sophisticated analyses may be carried out with software packages that provide econometric modelling capabilities such as EViews, MiniTab, MATLAB or DataDesk (to name but a few). 

However, most people who attempt to forecast markets either rely on technical analysis i.e., analysis of market data based on the theory that past prices can be used to predict future price movement or they follow the fundamental analysis approach, which tries to assess future movements by studying economic trends and news announcements.

How to Market Forecast

One of the first steps to marketing forecasting is determining what you are good at. What can your business do better than any other? This will be important later when it comes time to make decisions about how much money should be spent on various activities. You want to know that you've got a leg up on all the competitors out there, and this step helps with that knowledge.

You then need to determine whether or not marketing forecasts are needed in your industry. This can vary from field to field, but many businesses have found success using them as part of their strategies if only because they help keep everyone working together for common goals rather than disparate individual efforts towards different ends.

How to achieve Forecast Accuracy

Forecast accuracy is important to minimize the required inventory investment for supporting forecast error tolerance levels. Forecasting accuracy can be improved with better demand predictability which in turn can be achieved by using any one of the following approaches:

1) Better data quality - Data quality is the most often overlooked area when discussing performance issues. Bad data will give bad results no matter what type of algorithm you use for forecasting or how fancy your software is. Many organizations complain about lack of "real" data and that they work with "estimates". If you don't have good data, fix it.

2) Use more than one forecast method - Using different methods for forecasting helps to validate the results and increases the likelihood of obtaining the most accurate forecast. When different methods show conflicting results they can be combined into a single result with some sort of weighted average or consensus approach. Such an approach is called fused forecasting and has been demonstrated to outperform any individual forecasting technique alone under certain conditions.

3) Combine forecasts from multiple sources - Combining forecasts from different sources will help reduce overall forecast error as long as those sources are independent of each other. In manufacturing companies those can be sales channels, warehouses, distribution centers or even overseas offices that operate independently of each other and thus they collect and report data independently. If they are not independent of each other, their forecasts will probably be correlated and thus they won't add any benefit to your forecast accuracy.

4) Apply time-series analysis - Time-series models such as the auto-regressive integrated moving average (ARIMA) model can be used to fit historical demand data in order to generate better forecasts for future demand. This type of analysis is most effective when there is a trend in the data which can be controlled using regression algorithms. It requires minimum human interference if implemented correctly which makes it very popular among business users who are not experts in statistics or have limited access to statistical experts who can implement this technique. 

5) Use multiple forecasting methods simultaneously - Combining forecasts from different methods reduces overall forecast error. More advanced techniques are able to combine forecasts from multiple sources in order to increase accuracy further. Hybrid methodologies often outperform individual methods by themselves, however they require more work and resources which makes them prohibitive for many companies that try to do forecasting on their own with no or limited statistical expertise.

Get started with Market Forecasting

There are many possible ways to do forecasting, but here we will deal with one particular method called Exponentially Weighted Moving Average (EWMA), also known as moving average forecasting or rolling averages.

Here are the steps required to do market forecasting using rolling averages methodology:

Step 1 - Obtain data about past demand history, often referred as "historical facts"

This step is often forgotten or just skipped. It is important to go over historical sales reports, inventory levels etc. in order to establish patterns of demand fluctuations. There are various methods for collecting this sort of data such as using reporting applications like Cognos TM1/TM2 which can pull sales transactions from SAP or any other ERP system. Another common source of data is transactional systems themselves which store sales information by product category so it's easy to quickly gather that kind of information either through BI tools or by querying the database directly. Historical facts should be collected for at least six months, better one year so that no important patterns would be missed and thus influence future forecasts.

Step 2 - Define business drivers which determine demand pattern changes

Most of the forecasting methods rely on collecting data about past demand in order to estimate future demand with various levels of accuracy depending on how well we understand what drives demand fluctuation. 

The only way to improve forecast accuracy is to establish relationships between observed fluctuations and particular events or circumstances such as: 

  • Seasonality (e.g.: peak shopping season before Christmas) 
  • Events (e.g.: product launch or promotion) 
  • Economic conditionse.g.: recession, inflation, oversupply)

Step 3 - Establish target forecast accuracy level

Target forecast accuracy should be established based on different factors such as: 

  • Business strategy (e.g.: expanding markets to new regions) Whatever forecasting method is chosen, better performance usually comes with increased cost so it's important to have a clear understanding of what type of forecasts are needed in your specific business situation before you start doing any serious work on building them up. In many cases the ROI on better forecasting is directly linked to the business benefits achieved.
  • Marketing goals (e.g.: maximizing sales, minimizing inventory) 
  • Production costs and capacity (e.g.: manufacturing capacity or warehousing capacity) 
  • Regulatory compliance (e.g.: production quotas for eco products and hazardous chemicals) 

Step 4 - Collect data about past inventory levels, sales orders and returns

Data needed for building rolling averages is also referred to as "feed forward data". It includes historical demand history, historical stock level, order/returns history etc. Usually feed forward data contains more detailed information than forecasting data since it's easier to collect this kind of information, which makes it better suited for what we are trying to accomplish. Feed forward data is used both as an input into calculations and as a testing set for forecasting models to ensure that they are built on solid foundation.

Step 5 - Collect data about future demand and stock changes

This is called "forecasting data" and most commonly consists of future sales orders, inventory levels etc. Most companies usually roll forecasts by setting either monthly or weekly targets for each product category which can be manually entered in planning systems or generated automatically through planning applications such as Cognos TM1/TM2 Planning workspaces, MS Forecast Server etc. Effort spent on rolling forecasts directly corresponds to benefits achieved so it's important not only to have accurate raw data but also build forecasting models using right techniques for your specific business type. Otherwise you would spend too much time trying different methods without actually achieving better results.

Step 6 - Measure forecast accuracy using test set (historical data)

Test sets should be used to make sure that forecasting models built are accurate enough for actual use. Test sets typically include the same raw historical data as feed forward data, but it's always tested against different datasets than those used during the building process in order to avoid overfitting (i.e.: finding too many patterns which actually do not exist in the real world). For example, if we are trying to predict monthly demand for individual product categories, the test set usually contains last month forecasts made by our planning systems instead of true past demand history. 

Step 7 - Decide on target forecast horizon and update forecast model Targets may vary

considerably between companies so "forecast horizon" should be set accordingly. For example, if you want to build daily forecasts, testing and rolling processes would be done monthly since we already have daily historical data for testing and historical demand history is probably available on a monthly basis as well. In this case the forecast horizon would be "monthly".

Decision about updating forecast models should also be based on different business factors such as: 

  • Accuracy achieved during testing/record keeping process 
  • Number of products or product categories 
  • Availability of accurate sales history 
  • Amount of data used for building models (i.e.: number of years) etc. For planning purposes it's common practice to update rolling forecast models weekly but many companies use different approaches based on their specific needs.

Step 8 - Update the rolling forecast model on a regular basis

In order to ensure that models are updated regularly, these kinds of processes usually go hand in hand with demand forecasting. In fact, records from steps 6 and 7 should be used to make sure that historical data which goes into calculations is always up-to-date. 

There are several approaches for updating forecasts:

  • Using short term forecasts as an input - This approach assumes that short term forecasts will not change much over time and using them for the rolling process has relatively small impact on accuracy achieved. However if long term trends or seasonality changes significantly it may cause serious deviations when compared to actual results so this method would require careful evaluation before implementation. 
  • Updating forecasting models directly after they are built 
  • Updating rolling forecasts every x number of months 
  • Using specific events as triggers (i.e.: new product release, promotion) etc. The most commonly used method is updating the forecasting model on a regular basis using short term forecasts as an input, but it's always recommended to experiment with different techniques and figure out which one works best for your company under certain circumstances.

Step 9 - Monitor forecast accuracy over time and make corrections if needed 

Accuracy of rolling forecasts depends on many factors such as quality of original demand data, complexity of products/markets, availability of accurate sales history etc. So the first step would be to measure forecast accuracy over time based on sets from previous steps in order to find out which factors are influencing it most. For example, if accuracy is dropping significantly over time, one of these factors may be identified as the main culprit:

  • A new forecasting model was implemented 
  • Data used for calculations was changed (more months included) 
  • New products were added to existing product categories etc. Once the root cause is identified it's possible to take corrective actions in order to bring forecasts back on track. One common problem with rolling forecasts is that they tend to drift away from real results more and more over time so if you see this trend, now would be a good time to look into what might have caused it. This way you will avoid large deviations between forecasts and actual demand in future. Knowing how much the forecast has drifted over time also helps when forecasting new products because you can expect similar accuracy to be achieved.

Step 10 - Use forecast data in product life cycle management process

Since rolling forecasts are built based on previous sales performance, they should be used in planning future demand, i.e.: for product life-cycle management activities. For example forecasts might be used for these purposes: 

  • Create release schedule according to demand curve, taking into account that some products may have faster or slower sales development 
  • Plan promotion events based on peaks in demand forecast 
  • Plan production capacity accordingly. What's also interesting is that by using the same approach it's possible to build "reverse" rolling forecasts which are actually short term forecasts for next x period based on long term forecasts for next x+1 periods. This method is useful especially when products have similar sales behavior over time and less accurate demand data, as well as to find out how much actual product sales will deviate from long term forecast if no corrective actions are taken.

Step 11 - Use rolling forecasts in marketing activities

Apart from using forecasting information in product life-cycle management process, it can also be used by marketing planning activities to create more accurate plans for upcoming events such as: 

  • Planning marketing campaigns based on peaks in demand 
  • Measure overall marketing ROI against different campaigns There's a lot of things that could be done with rolling forecasts, but remember that you can't manage what you don't measure so make sure you always measure your results. Also, make sure to monitor forecast accuracy over time in order to take corrective actions when needed in order to avoid unexpected surprises in future.

Once you have decided on the forecasting method, gather data from your business and start creating a forecast based on it! Don't forget to consider what might happen if something changes with these variables too - often forecasts only look at things staying the same but this won't help you prepare when events come up that can affect future figures. For example, an increase in gas prices could cause budgets to decrease so make sure you take that into account.

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Harsh Gupta

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