22 Jun What is the Big Difference Between a Sales Forecast vs. a Demand Forecast?
Forecasting is a critical aspect of retailing, and yet, there is much confusion about the definition of sales forecasting vs. demand forecasting (as well as their respective benefits and drawbacks).
All retailers use some form of forecast to anticipate the future. Without having an idea of how many items you will sell tomorrow, building a successful and lasting retail business is impossible. And yet, Forrester Research found that while 74% of retailers want to be data-driven, only 29% are able to connect analytics to action.
This is primarily due to the fact that there is a big difference between using sales to generate a forecast as opposed to using a multitude of factors (including sales) that affect demand. In other words, sales only represent one data point.
Why is this distinction so important?
This is because the two methods produce very different forecast accuracies — which results in the massive gap between those retailers that are flourishing in the digital age, and those that are rapidly sliding into the retail apocalypse.
Sales Forecasting Definition
Sales forecasting is an approach retailers use to anticipate what future sales will look like by analyzing their sales history, identifying trends, and projecting data into the future.
This has been the standard approach to retailing from the beginning of the industry itself, and although modern retailers have more data than ever (as well as new tools and business intelligence dashboards), sales-based forecasting is still the backbone of most retail organizations.
Sales Forecast Pros and Cons
The Pros of using a Sales-based forecast:
This is a fairly simple approach that is easy to implement. Most retailers use a combination of their ERP, BI tools, and (in many cases) good ol’ Excel to generate a forecast based on their sales history. However, this is a deceitful “pro” because this simplicity will certainly cost you in the long run.
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 DCs, a sales-based forecasting will result in far too many exceptions.
New products, seasonal peaks, price changes, promotions, competition, moving holidays and dozens of other factors that influence your business make it impossible to simply rely on what happened this time last year.
A sales-based forecast requires constant “massaging of data”, human interventions, generalization, estimation, and customizations that defeat the purpose of a forecast.
To make matters worse, if you sold less merch last year because you ran out of stock, this approach will actually suggest the same inventory level for you in the future, repeating last year’s costly mistake again.
Demand Forecast Definition
Demand forecasting is the process of removing the effect of factors that influence sales data to uncover true past demand for your products at all locations and channels.
This is an advanced approach to forecasting that can only be accomplished using retail-specific AI and predictive analytics.
In order to remove the effect of factors like seasonality, inventory availability, and price changes, retailers require the ability to identify the weight of each, and how they affect each other.
Demand Forecast Pros and Cons
The Pros of using a Demand-based forecast:
The benefit of using this 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.
According to a 2019 McKinsey report, retailers that use advanced analytics 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 don’t offer true demand-based forecasts powered by advanced retail AI. This means retailers will need to invest in new technology. On the bright side, this investment will pay off in spades, and companies like Retalon make it easier than ever to implement Retail AI into any platform.
The digital age requires a more focused, agile, and proactive approach. Not being able to distill data into specific, tangible, and actionable recommendations creates a ripple effect though the entire business that dramatically inflates costs and decreases gross margins.
It’s a must have for any merchant that wants to experience success with their retail business in the digital age.
As Gartner’s VP of Research, Robert Hetu, recently put it, “Retailers that cannot successfully leverage prescriptive product recommendations will find themselves outside of the path to purchase. Not only will they lose existing customers, they won’t be able to attract new customers due to their lack of relevancy and ability to influence.”
The good news is that if you’ve managed to read up to this point, and so you’re becoming more aware about the power of advanced analytics for demand forecasting in the digital era. But don’t wait too long.
One of the biggest mistakes retailers make is overthinking their approach with new technology. Don’t hesitate to pilot new ideas. Run a pilot or proof-of-concept on your own data to see the difference in forecast accuracy and the impact on your business. You’ll be pleasantly surprised and it will be hard to argue the results.
Discover how the most accurate demand forecast in the world can help you maximize your GMROI.