22 Jun Sales Forecast vs. Demand Forecast – What is the Big Deal?
Sales Forecast vs. Demand Forecast
– What is the Big Deal?
Despite the fact that forecasting is the backbone of most retail organizations, there is still much confusion about the distinction between a sales forecast vs. demand forecast (as well as their respective benefits and drawbacks).
So, why are forecasts so important for retail success?
All retailers use some form of forecast to anticipate the future. The forecasts provide insight on how to maintain a successful and profitable retail business. Of course, this goes much deeper, but let’s focus on the high-level functionality.
For example. retailers must know how much inventory to purchase, keep in stock and have on shelves. Without an accurate forecast, they could end up buying too much of the wrong inventory and not enough of the right inventory, resulting in;
- Unnecessary markdowns
- Lost sales & revenue
- Diminished brand reputation and customer satisfaction
- Increased stock-outs
At the end of the day, a good forecast stands between being profitable and going bankrupt.
This make-or-break situation is why retailers are becoming more data-obsessed, especially in regard to forecasting.
Why is the distinction between a sales forecast vs. a demand forecast so important?
As we have just explained, forecasts are the backbone of today’s digital retail landscape. Unfortunately, there are still a number of retailers that are guessing with their data.
What we mean by this is that they don’t know how to use their collected data to build accurate forecasts or how to leverage their forecasting to build solid, results-driven strategies.
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A major reason for this difficulty is that most retailers don’t understand the difference between sales forecasting and demand forecasting.
As we will explain, these two methods produce forecasts with very different levels of accuracy. This disparity in the accuracy of results creates a massive performance gap between retailers who leverage advanced predictive analytics (like demand forecasting) and those that do not.
The former are flourishing in the digital age, and the latter is rapidly sliding into the retail apocalypse.
Let’s examine why.
What is Sales Forecasting?
Sales forecasting is an approach retailers use to anticipate future sales by analyzing past sales, identifying trends, and projecting data into the future.
The simplest version of a sales forecast will look at sales in Store A during last year, assume a continuation of some multi-year trend for Store A (e.g. some percentage of growth or decline in sales), and project forward to predict sales in Store A this year.
Sales forecasting has been the standard approach to retailing from the beginning of the industry itself.
Although modern retailers have more data than ever (as well as new tools and retail dashboards), and can easily leverage more advanced and accurate forms of forecasting, sales-based forecasting is still the backbone of most retail organizations.
Sales Forecast Pros and Cons
The Pros of using a Sales-based forecast:
Using a sales-based forecast is a fairly simple approach that is easy to implement.
Most retailers use a combination of their ERP, business intelligence tools, and (in many cases) good ol’ Excel to generate a forecast based on their sales history.
This means virtually every retailer, small or large, can build basic sales forecasts without investing in additional new tools or processes.
The Cons of using a Sales-based forecast:
The problem with this approach is that if you’re a modern retailer that sells through multiple channels (with multiple locations and distribution centers), a sales-based forecast will result in far too many exceptions.
What does this mean?
In simple terms, if you use a standard sales forecast approach for a complex organization, you will soon find a lot of inaccuracies in its predictions.
Dozens of factors (that a sales forecast does not take into account) will need to be manually reconciled by teams of analysts to get a more accurate picture. Some of these factors include:
- New product launches: If your retail store experienced a unique product launch last year that will not be repeated again this year — its effect needs to be removed from your forecast.
- Seasonal merchandise peaks: If last winter was unseasonably cold with a longer peak, extending the lifetime of your winter jacket product line — there’s no guarantee the same will happen this year.
- Price changes: If product pricing has changed on your best-selling products since last year, there’s very little chance that they will sell in the same quantities
- Promotions: If you had a series of wildly successful promotions last year, your baseline sales numbers are going to be severely inflated.
- Increased or decreased competition: If a new player opened up shop across from you, or a major competitor went bankrupt — basing your sales forecast on last year’s numbers will be completely wrong.
- Moving holidays: If you forecast sales on a daily, weekly, and monthly basis at the location level — you will need to manually adjust days and weeks of sales uplift due to holidays and days off
And these examples are just scratching the surface, as there are hundreds of factors that have a powerful impact on your bottom-line sales (e.g. product cannibalization, inventory constraints, assortment diversity and depth, vendor constraints, etc.).
So, simply put, a sales-based forecast requires constant “massaging of data”, human interventions, generalization, estimation, and customization — defeating the purpose of what a forecast aims to achieve (an accurate prediction you can base your strategy on).
Another major con of using a sales forecast is that it forces you to repeat the same mistakes over and over again.
For example, if you sold less merch last year because you ran out of stock, a sales forecast will suggest the same inventory level (or slightly more with assumed nominal growth) for you in the future, repeating last year’s costly mistake again and causing you to miss out on sales.
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Demand Forecast Definition
Demand forecasting is the process of predicting consumer demand for products.
Simply put, demand is the willingness of customers to buy a product at a specific price point.
In other words, if you know how much demand a product will have at a specific price point, you will know exactly how many units to stock to maximize sales and minimize cost.
While this would have sounded like magic a few decades ago, retail demand forecasting is already being used by the most sophisticated retailers.
Advances in mathematics and artificial intelligence make it possible to calculate the effect of influencing factors (e.g. price, seasonality, cannibalization, etc.) and generate prescriptive forecasts that not only accurately predict your future sales, but will also tell you how to maximize them.
Under the hood, this is a fairly complex process. In order to remove the effect of factors like seasonality, inventory availability, and price changes, retailers require the ability to identify every major influencing factor, accurately weigh the impact of each, and map out how they influence one another.
For the purpose of running a business, building a plan, and buying effectively, however, a retail-specific AI and predictive analytics. make demand forecasting a cinch to use for retailers.
Demand Forecast Pros and Cons
The Pros of using a Demand-based forecast:
The benefit of using a demand-based approach is a highly accurate, dynamic, and reliable forecast.
Accurate demand forecasts allow retailers to confidently execute merchandise assortment plans, bring the right quantity of inventory to the right location to match promotional uplifts, and avoid unnecessary markdowns.
This also means if you sold less merch last year because you ran out of stock, the system will identify those lost sales and account for this when suggesting inventory levels for you in the future — allowing you to avoid past mistakes and increase your sales.
The proof is in the pudding. According to a 2019 McKinsey report, retailers that use advanced analytics (like demand forecasting) are outperforming their competition by over 68% — with the number growing exponentially.
The Cons of using a Demand-based forecast:
Most retail ERPs and business intelligence solutions don’t offer true demand-based forecasts powered by advanced retail AI.
Unfortunately, this means that even if these systems are generating forecasts, they are not going to be as accurate – which could lead to a costly end result.
Of course, in order to achieve this level of accuracy, retailers will need to invest in new technology.
Retailers are often reluctant to take this route due to potentially high costs and lengthy implementation processes.
On the flip side, retailers relying on approximated demand and archaic forecasting methods will never be able to distill data into specific, tangible, and actionable recommendations. This will eventually lead to;
- Inflated costs
- Decreased gross margins
- Lost revenue
Just like anything else, there are some demand forecasting challenges but in the digital age, it’s a must-have for any retail business that wants to experience success and future growth.
Next steps for driving real results from forecasting
Retailers must be able to anticipate the future to some degree of accuracy in order to run a profitable business.
This necessity to “see the future” has made forecasting the backbone of today’s data-driven, digital retail landscape. But, it’s important to remember that not all forecasting methods are created equal.
Generating accurate forecasts requires in-depth data analysis, and the ability to account for multiple factors and variables – something that can only truly be achieved through the use of a unified demand forecasting solution powered by advanced analytics.
The industry is moving fast and those who hesitate will be left behind.
Still not convinced?
Book a demo with our knowledgeable team and run a proof-of-concept using your own data to see the difference in forecast accuracy and the potential impact on your business.