How to forecast sales for a new product
Sales forecasting is a challenge, but particularly so for new products. Most sales forecasting methods require historical data to be gathered from a CRM or other source of bookkeeping and then crunched using some kind of software, such as Excel. Complicating the sales forecasting process is the number of variables in quantitative data and the general inaccuracy of qualitative data. That said, sales forecasting works well enough when sellers have even a modicum of data to run on. But what about how to estimate demand for a new product? Especially when a product is remarkably innovative and unlike anything else on the market, there are practically no trends or insights to pull from.
Risk is tied up in every product launch. And a lot of the pressure to make a new launch successful falls to the sales team. Then there’s a question of how appealing the product seems versus how appealing it actually is. If leadership believed in the product enough to send it to market, then, hopefully, it has enough market research showing demand for it. At the same time, that puts even more pressure on sales to engender its success and communicate its value to prospective customers.
Of course, there are certainly ways to forecast sales for a new product. Considering the number of businesses that have constantly launched new and exciting innovations (as well as those that have flopped), there are a lot of lessons to be learned about how to calculate market demand for a new product. Let’s look at a few.
How do you calculate demand for a product?
Sales forecasting starts with estimating demand for the new product. There’s going to be an element of the unknown that’s unavoidable, but there are methods for finding both quantitative and qualitative data that can lead to a good estimation of demand.
Market research: Ideally, when a business decides to launch a product into the world, it has done copious research, including surveys, panels, or one-on-one interviews. The results from those lines of questioning should provide insight into the type of demand a business may see for a new product. It won’t be indicative of the entire market, but it’s a start.
Past demand for similar products: Unless a business is stepping far outside of its lane and offering a product unlike anything else it’s ever released, sales data should exist for other products similar to the new one. And even if the product is different from other products the business has sold, it’s likely to have some kind of competitor counterpart on the market (unless it’s radically new). Getting in-depth data for competitors is difficult, but basic information is available.
Sales team instincts: If a new product is being released, there’s a good chance the sales team has started to float the news out to their accounts. And those accounts have either expressed interest or not. That data is strictly qualitative but valuable nonetheless. Even word-of-mouth interest can prove helpful in future forecasting.
Sales forecasting methods for new products
There are several types of sales forecasting that can help businesses calculate demand for a new product, but two will seem familiar and provide solid results. These are opinion forecasts (also called “qualitative”) that are based on seller opinion and instinct, and historical forecasts (also called “quantitative”) which employ past sales and market data to project future sales.
Traditional forecasting methods can work, but they might require slightly different types of data than sellers might use when forecasting a well-established revenue stream. The standard Excel formula can be used to analyze quantitative and qualitative data as long as all of it is in place. This process provides basic insights upon which to build. Some considerations for forecasting traditionally:
Forecast small. Market segments react remarkably differently for new products, with some jonesing for the hot new thing and others waiting for later versions. Small, focused forecasts best predict their actions.
Be flexible with time. Any time a new product is released into the market, sales may differ wildly. These numbers will be most valuable in future forecasting, so setting forecasts for windows of a week — or even daily — will show how demand may change.
Run multiple forecasts. In a lot of ways, a new product release is a gamble. But risks can be overcome if sales teams forecast a wide array of potential situations. Alter as many of the variables as possible to get a range of results. From there, analyze the forecasts for similarities.
Reforecast frequently. Once the product is on the market, more quantitative and qualitative data will exist. Run more forecasts with new data frequently. In addition to sales numbers, look at customer reviews and marketing data. Be willing to pivot quickly with new forecasts.
How do you predict demand for a new product the modern way?
The alternative to all the hard work of the above sales forecasting examples is to use an AI-powered forecasting solution such as Collective[i]. We use data from an ever-growing network of sales professionals to produce highly accurate sales forecasts and recommendations for sales teams. As it gains more information, our AI solution learns more about customer behaviors and patterns that can help sales teams working on a new product launch pivot their strategies — and make more thoughtful decisions. We can tell if you’re targeting the wrong demographic with your approach and give insight as to which demographic is the correct one.
In other words, as sales teams bring new products to market, Collective[i] learns how the market is responding to provide actionable insights from the get-go.
Click here to try Collective[i], and treat your new product like it’s part of your core offering.Explore Collective[i]