Launching new products is crucial to any growing business. Bigger businesses frequently launch new products as they venture into newer markets. Often, a product new to a company may already exist under a different brand name. A unique product is rarely introduced in any market.
Whether your product is unique or not, before you line shelves with and new product or decide to have a huge launch, you need to know how much production will be adequate. However, the problem with new products is demand is often complicated to forecast. With no past data to consider, demand forecasting for new products can feel like finding a needle in a haystack.
Forecasting demand for a new product goes beyond simply production needs. It includes everything from your pricing and promotions to inventory. An inaccurate forecast can make you miscalculate output, leading to one of the following harmful scenarios.
1. Underestimating sales leads to a stockout situation
2. Overestimating sales leads to an increase in inventory waste and even deadstock
1. Inaccurate allocation of products based on an inaccurate forecast leads to lost sales or paying heftily for transport.
Any of these situations will mean a definite loss for your business. It might even damage your company’s reputation. When it comes to forecasting new product demand, traditional methods often fail. Though there are a few ways to calculate demand, none of them can be deemed accurate.
But that doesn’t mean there is no way to predict demand for your future products. You can forecast your demand for new products almost as accurately as any other product. Before we discuss that, let’s establish why traditional ways prove inadequate for the purpose.
Why is demand forecasting for new products difficult?
When your new products are similar to your current ones or are replacement SKUs for existing products, then you can easily use your current tools to forecast demand based on past sales of your existing products.
But this isn’t often the case.
Though the traditional method would assume so, why would you want to launch a product identical in many ways to your older products in terms of function, style, price, and quality? New products often stand out from the older ones in more than one way. This is where it gets tricky.
New products bring new challenges, like geography. You need to forecast data for every inventory location accurately. An overall forecast can only help you with how much production will be required. It can’t help you generate a location-wise segregation strategy. This can lead to stockouts in one location while overstocking at another. For a new product, forecasting demand also gets difficult considering the demographics, local competition, constraints, and more.
Another aspect is the cannibalization effect. If you launch a product similar to an older one, it will affect the demand for the initial product. Even if your product is unique, you must account for inventory, pricing, and demand changes for other products affected by the launch of the new product.
The problems mentioned above can arise for evergreen products. When we consider seasonal products, the forecasting process gets even more complex. You must also account for seasonality and trend changes to ensure your forecast is accurate.
The cumulative effect of these factors makes demand forecasting for new products rather cumbersome and risky.
Traditional ways of forecasting demand for a new product
As we mentioned, you can forecast demand for new products using the conventional methods, but not always accurately. Creating the first forecast is often the most difficult. If you have similar products that exist, you can derive data by comparing their demand and monitoring their forecasts.
But, if that isn’t the case, it becomes difficult to understand how likely your product is to sell. You need to study your customer’s behaviors, conduct surveys, get more insights into new product categories, and more. You would also have to factor in competition in your chosen market to predict demand better.
You can employ a pilot project to understand your future demand. The risk here is that a pilot project may express an inaccurate demand due to its location. If demand is predicted based on this data, you might experience loss as you might understock or overstock in other locations. One way to avoid this is to run a pilot project in more than one location, but this would be a rather expensive solution.
Prior to launch, your marketing team can start promoting your products online or start accepting pre-orders to assess the demand and performance of your product. But this needs a strong promotion strategy in place to ensure your product reaches a wider audience. It would also be required for you to measure the success of these promotions by comparing your pre-orders and in-season sales.
Why are the traditional methods inaccurate?
None of the traditional forecasting methods works because their results aren’t absolute or entirely based on data. They all involve a fair amount of guesswork, which renders them inaccurate. This is why companies often fail to launch a new product or cater to customer demand successfully. Some companies launch new products with an introductory discount to increase demand. But this can hurt them once they start selling their products at their retail prices.
Where the conventional demand forecasting methods fail, automating demand planning with advanced software can work. Supply chain software like TransImpact’s demand planning solution helps you forecast demand for your new products almost as accurately as for your older products.
Our demand planning software uses 250+ algorithms to monitor similar products, analyze competition, and perform market research to give you an accurate demand forecast for your products. It uses sales data of similar products in the market to provide accurate forecasts while accounting for factors like seasonality and changing trends.
With TransImpact, you get accurate results every time. To get a demo of our amazing demand planning tools or get to know our SaaS+ products better, get in touch.