NPD has a number of benefits, one of which is risk diversification. With a good range of products, you can distribute your risk across your product range. Moreover, having a wider collection of products helps reestablish brand identity and top-of-mind awareness; launching new products reminds people of your brand and its quality on a regular basis.
If you’re wondering just how many benefits launching a new product might have, let’s not get carried away and forget what’s involved in a product launch. Businesses, whether well-established or new, big or small, must first forecast demand for their proposed products. In this article, you’ll gain insight on why forecasting new products is important and how can you do it effectively.
Why forecasting new products is important
Forecasting is a crucial business practice, whether you’re looking at sales or demand for new or existing products. Having sufficient inventory to meet customer demand depends on accurate product demand forecasting. For example, if you’re a skincare company, you might develop a new face mask with an ingredient such as avocado. Forecasting the product’s demand can help you produce the quantity needed at launch time by allowing you to plan for critical points in your supply chain.
Forecasting also gives your team better and clearer direction. This is particularly true for those directly involved in the production of the new product. By forecasting the product, you’ll gain vital information including demand rate, supply rate (production rate and inventory), insight on inventory management, and insight on how the product will be marketed and sold as well. While some of this information may be just predictions or approximate values, it is still highly valuable to teams throughout your organization, including production, merchandising, marketing and warehouse/receiving. Data provides a direction and set goals and targets for your team to achieve.
How to forecast a new product
The following tips will help you successfully forecast a new product for better business performance. While forecasting will involve several unknowns, this process can help inform your initial order.
- Look at your existing products and identify any trends. Make use of historical data regarding your existing products, which are a gold mine of valuable information. Examine how, why, when and where they were profitable and apply these findings, where relevant, to your new product. When looking at past product launches, pay attention to any patterns. For example, do your products launch really strong for a few days then level off for continued sales in the following days and weeks? Or does demand for your products build over time, taking several weeks to attract strong interest from your customers?
- Use similar products from your line as a reference point. For example, if you’re going to launch a new skincare mask with a special ingredient, there’s a good chance that your other products have similar features and benefits. You can and should use your existing face masks and their performance as a reference point for your new mask. Unless you’re launching something totally different from your existing product line—for example, you’re a clothing brand releasing skincare products for the first time—crucial information, such as the best distribution channels or selling season, can be gathered from the existing product and applied to the new one.
- Look at option set performance. When launching a new product that will have several variants, look at other products or categories using similar option sets to inform how many of each variant to order. For example, if you launch a new sweater in red, green and blue, look at other products using this same color option set. If current sweaters in your store sell 50% in red, 30% in green and 20% in blue, use those proportions to inform your initial order for the new sweater.
- Take into account the cannibalization effect. Product cannibalization is a common occurrence after the launch of a new product. Basically, when a product launches, it’s bound to have some effect on the existing products in your portfolio. Cannibalization refers to a situation where the new product takes a portion of the market share and revenue of your other products. Because of great market performance, the new product becomes the best-selling, higher profit-generating product. But the grim-sounding term “cannibalization” shouldn’t mislead you into thinking that it’s necessarily a bad thing.
The cannibalization effect has a huge role to play in determining production levels, raw material supply and inventory levels. Fully understanding this effect can help you reallocate resources. By estimating your new product’s cannibalization effect, you’ll have a better idea of supply quantities needed for the new product as well as existing products.
Now that you’ve learned the basics of new product forecasting, it’s important to understand that there may be some setbacks. Forecasting isn’t easy. And contrary to its name, it’s all based on past data, which can prove insufficient. Be prepared for the following challenges when forecasting new products
- Limited availability of data: When launching a new product, the only useful data you have comes from your existing products or similar products by your competitors. While you can look at similar characteristics in other products, your new product isn’t guaranteed to perform in the same way. Moreover, it’s unlikely that your existing products will give you a true, accurate picture of your new product’s performance.
- Organizational bias: It’s important to note that bias is a very common issue in forecasting. You, as the business owner or the great mind behind the new product, will naturally have a slight bend toward your product. You will want it to look good on paper and make it to the final stage of being launched. This bias can cause you to develop untrue, unreal and inaccurate results and values.
- Unexpected and unknown occurrences: One problem in forecasting leads to another. Since you’ll be using past records and data from other products, it is likely that this data won’t help you predict other possible outcomes and occurrences. For example, a charcoal skincare product launched by one of your competitors was hugely popular and profitable a year ago. But because the product has already been in the marketplace, the consumer’s reaction to your charcoal product may not be the same. We can call these unknown occurrences, which are usually qualitative rather than quantitative.
While the aforementioned tips should help you overcome most forecasting challenges, test marketing is the best way to minimize errors and mishaps. Test marketing is a technique used worldwide by all kinds of businesses. It basically involves launching your new product on a smaller scale and in an unofficial manner. This means that you make the product available to only a segment of your target audience to “test” your product’s performance in market. This helps you gather feedback and reactions from your customers based solely on your product rather than past data. However, test marketing is time-consuming and costly, which may not work well for product launches where time and/or money are constrained.
Many business owners and managers find themselves stuck when it comes to forecasting a new product. You may feel like there’s no data to get started. But every new product has a prerequisite that you can use as a reference point. This could be from your existing product portfolio or even your competitors’ products. Above all, keep all business departments and operations coordinated. Start looking for forecasting data in related product categories, competitors’ moves and your existing product line, leveraging the history of similar products and feedback from stakeholders.
About the author:
After six years as an entrepreneur and three as an Inventory Planner customer, Jill Liliedahl now works with Inventory Planner merchants. Inventory Planner saves eCommerce merchants time and money and helps them better meet customer demand through informed inventory purchasing decisions. Inventory Planner integrates with Cin7, helping forecast customer demand and create purchase orders based on replenishment recommendations.