01 Sep How to Effectively Forecast Demand for New Products and Optimize for Launch
Forecasting products you’ve never sold before is always a challenge. Without historical data to draw upon, how do you predict the best way to allocate them to stores, pricing, promotional demand, and balance online versus in-store sales? Guess wrong and you will run out of inventory, have too much inventory, or have the right inventory in the wrong place. – all costly mistakes.
The traditional approaches to estimating demand only hint at how a particular product will perform. Your store footprints, existing product mix, customer demographics, and many other variables are too difficult for analysts to fully consider. Retail Artificial Intelligence (AI) and advanced analytics significantly improve retail performance by incorporating hundreds of data inputs to produce reliable demand forecasts for new products.
Without Past Performance, Product Forecasts Create Risk
Introducing replacement SKUs or line extensions fits naturally into your existing demand forecast process. But the existing process cannot handle products without clear sales histories. As a result, new product forecasting introduces more uncertainty and risk into your business.
You make significant commitments to your vendors when you launch a new product. Their forecast windows lock your initial orders and early replenishment orders. Co-marketing funds you receive require investments in store merchandising and promotional campaigns. And especially for mid-sized and large retailers, the future of your vendor relationship will depend on the new product’s sales performance.
Without a solid demand forecast, you risk misallocating inventory and missing your revenue targets while watching turns collapse. Fixing the problem through stock rebalancing or price markdowns will be expensive. Not to mention lost opportunity of bringing another product instead and losing cash.
Getting off on the right foot is critical. The larger your business, and the more frequently you introduce new products, the less you can rely on best guesses. How you estimate demand for a new product matters. But systematic approaches get stymied by the number of variables that influence demand.
Why Forecasting New Product Demand is a Challenge
Even the largest retailers can’t employ enough analysts to understand everything driving product demand. When it comes to forecasting products without any history, the job becomes almost impossible.
Each store has its own combination of geo-demographics, personalized assortment, space constraints and other conditions that requires unique inventory levels and demand/sales forecasts. Smaller retailers can make educated guesses for a handful of stores. But what is the optimal store-specific forecast across dozens or hundreds of stores?
Retailers need to also account for new products impacting the sales of other SKUs through product demand cannibalization. It is very difficult to predict the cannibalization effect of related products, risking losing demand and sales in areas you didn’t expect. Cannibalization is a very subtle interaction of relative pricing, inventory levels, changes in demand, shelf positioning, and other factors.
Figuring out how to forecast demand for a new product may be straightforward for reliable evergreen items. But forecasting seasonal or limited-run products is harder. At the extreme are categories like video games where sales are heavily front-loaded with huge spikes on launch days.
Introducing new products during promotional campaigns will skew your forecasts. Uplifts are difficult to calculate when campaigns center around the new product. Even more challenging, measuring the impact on new product sales when campaigns center around adjacent categories.
Running hand-in-hand with the inventory forecasting is your product pricing strategy. What should initial pricing look like? Is launch pricing different from everyday pricing? Is the pricing seasonal or does the vendor have a predictable cadence of price drops? And how do you set promotional pricing to optimize sales, inventory, and profitability?
How To Estimate Demand for a New Product
As complicated as it is, the first forecast has to come from somewhere. Buyers and analysts try to do their best by finding data that hints at the new product’s future performance. In some cases, you may have existing products that are “close enough” to draw comparisons. Different-styled apparel meant for the same customer; laptops with different specs but similar prices; or last year’s products from the same holiday season can all inform new product forecasts.
Outside market research from retail specialists can give some insight into new product categories. Consultants can help you understand new kinds of customers, assess a product’s mindshare on social media, or conduct local focus groups.
A pilot project may provide actionable information when a new product’s demand is too uncertain. If you don’t have enough stores to create a representative sample, a limited-run pilot in all stores may be your only choice.
Some retailers use pricing to discover optimal demand. They introduce the new product at a higher price, reducing it over time until reaching the most profitable run rate. However, this approach runs the risk of cutting too far and is heavily reliant on trial and error. Once you’ve set a lower price in customers’ minds, reversing course is difficult.
These and other traditional approaches to forecasting products suffer from the same failings. None of them are accurate predictors of demand. None of them predict your business’ unique situation. And to an extent, all of them simply inform gut decision-making. Reliable data only arrives after the fact which can make new product launches very expensive. This is why new product sales forecasts have traditionally been more of an art than a science.
Forecasting New Products With Predictive Analytics and AI
Retail AI platforms like Retalon take demand forecasting for new products to new levels of accuracy. Machine learning algorithms can analyze many more inputs and tease out trends better than any analyst identifying the factors that impact demand for the new product. By using Predictive Analytics, you can produce more accurate by-SKU-by-store demand forecasts even when you have no sales history.
Predictive Analytics automatically generates a forecast based on a new product’s attributes rather than on the product as a whole. For example, how do red shirts and short-sleeve shirts and cotton shirts perform? The completed analysis yields intelligently-allocated inventories for each store and distribution center. At the same time, Retalon’s AI evaluates the cannibalization of other products and reduces their upcoming orders.
The system automatically accounts for seasonality, assortment depth vs diversity, store times, e-commerce fulfillment demand, promotional uplifts, price elasticity, related product and much more.
Once the new product launches, Retail AI learns more about the product’s true store-level demand faster than traditional analysis. The system automatically responds to the reality on the ground and adjusts future orders, allocation, promotions, and pricing accordingly.
Retalon’s Predictive Analytics platform enables a unified, analytical approach to retailing that integrates pricing with inventory management. The AI not only identifies future demand but also optimizes pricing throughout the product life cycle to align with store-by-store inventory levels to maximize revenue and profit.
The best way to see how your specific business can benefit from this technology is to request a demo on a sample of your own data. Get in touch with our team to see how our Predictive Analytics can reduce the risk and maximize the return of your next new product launch.
Discover how Retalon’s AI driven retailing solution unifies inventory, merchandising, pricing, and promotions — using the world’s most accurate demand forecast.