10 steps for forecasting demand and revenues for new products
Forecasting demand and revenues for new variants of existing products is difficult enough. But forecasting for radically innovative products in emerging new categories is an entirely different ball game. There are no past trends to reassuringly extrapolate into the future, just a ton of uncertainty about whether the latent demand that the marketing folk suggested to secure the R&D funding is real or not. And after so much investment, the board is sure that this is the product that is going to become the next cash cow. Sure, you could manage their expectations by reminding them that something like 80% of new products fail and name drop a few of the spectacular flops of Fortune 500 companies. But that would be career limiting. A better alternative is to take control of the situation and adopt some of the forecasting best practices approaches that others have found to work.
Step 1: Make it a collaborative effort
Identify a handful of key people from marketing, sales, operations, and relevant technical departments and form a working group. This core team will be responsible for developing and managing the reforecasting process through the launch period until demand planning becomes more predictable.
Step 2: Identify and agree upon the assumptions
Collectively review all the available qualitative and quantitative data from market research, market testing, and buyer surveys. Use the data to identify a set of assumptions that can form the basis of a forecasting model. Ideally this will include assumptions about:
- Number of consumers in the target market
- Proportion expected to buy the product
- Anticipated timing of their purchase
- Patterns of repeat purchasing and replacement purchasing
Be prepared to commission additional research or consult external industry experts to fill any important data gaps. And always let the working group use their collective judgement to identify a realistic range of values for each assumption.
Step 3: Build granular models
Not all consumers will purchase a new product at the same rate. Some may be prepared to queue all night around the block to get their hands on it, but others will want to wait for subsequent versions when any unforeseen bugs are fixed and prices are typically lower. So it is important to build a forecasting model that is sufficiently granular to reflect how and when different market segments in different geographies might purchase the product and at what price.
Step 4: Use flexible time periods
Sales over the first few days and weeks in the life of any new product need to be carefully monitored as they will quickly show how demand is likely to grow in the future. So although the sales and finance function may only be interested in monthly data, it pays to develop detailed daily forecasts for the first quarter against which to track actual sales.
Step 5: Generate a range of forecasts
Run through a number of iterations, changing various assumptions and probabilities in the model to generate a range of forecasts. This is easily done if a modelling solution that can be recalculated in real-time is deployed as internal experts and business leaders can generate and test alternative scenarios on the fly.
Step 6: Deliver the outputs that users need quickly
In new product launch planning, agreements may have been reached with a number of suppliers to deliver rapid replenishment designed to prevent stock outs in the most uncertain period immediately after the launch. However if reforecasting the exact replenishment needs of every distribution point in the supply chain involves multiple steps, much of that precious time will evaporate.
Building a fully integrated forecasting model that compares existing stock level and automatically generates a detailed replenishment report for every location as soon as any high level assumptions change precludes such delays and shortens the replenishment cycle.
Step 7: Combine different techniques
Bottom up modelling based on purchasing intentions is not the only method available for forecasting demand for new products. In some markets, such as technology and consumer electronics, products can go through an entire life cycle in a matter of months. Such narrow windows of opportunity make it vitally important to assess demand as accurately as possible. The most damaging situation is having a stock shortage while the product is still hot, leading disappointed consumers to purchase a competitor’s product.
These sectors make use of sophisticated modelling techniques developed by academics that use substitution and diffusion rates to forecast how rapidly new technologies replace older ones. Such methodologies might not be appropriate to many businesses, but the message is the same; combining different forecasting techniques gives more accurate results.
Step 8: Reality check the forecast
Whenever reliable data exists, always check the forecast against the sales evolution of comparable products to see if it is realistic. Similarly you should also estimate how your market share might evolve as new competitors came into this emerging category and how the total market might grow. Unless this macro overview is credible, be prepared to rework the assumptions behind the model.
Step 9: Reforecast, reforecast and reforecast some more
Diligently monitor sales and qualitative feedback such as product reviews, media mentions, and customer feedback, and agree with the members of the working group how the assumptions in the model might need to change. If it’s appropriate, reforecast daily.
Step 10: Be prepared to cut your losses
Finally, always have a contingency plan. A high proportion of new products fail and it is better to pull the plug on an ailing new product that is unlikely to achieve a viable level of profitability at the earliest opportunity. So quantify and agree what level of sales penetration constitutes failure well before the product launch. That way, the decision will be swift and the existing stock can be quickly and cost-efficiently depleted.
Forecasting demand for new products is not an exact science and relies on judgement rather than statistical techniques. Key to success are collaboration, using all the quantitative and qualitative data that is available and having a modelling solution that can quickly and easily be updated to generate detailed forecasts for all users across the business. The benefits can be impressive both in terms of reduced inventory costs and improved customer satisfaction, something that is vital for a new product to flourish.