Using data to inform precise and rigorous planning(1) Make it a collaborative effort – Introduce a core team that is responsible for developing and managing the reforecasting process through the launch period until demand planning becomes more predictable. This team should be made up of people from across the business, including marketing, sales and operations. (2) Make assumptions – Together, look at qualitative and quantitative data from market research and testing as well as buyer surveys. From this data, you can identify a set of assumptions that will form the basis of a forecasting model, such as the number of consumers in the target market and proportion expected to buy the product. (3) Prepare to be flexible – Not everyone buys at the same time, so a forecasting model needs to be granular enough to reflect when different market segments from different places might purchase and for how much. It is important to monitor the few days and weeks after a product launch to understand how demand might grow. Sales and finance will be most interested in monthly data. Don’t let that stop you from developing daily forecasts for the first quarter to track sales. (4) Create a range of forecasts – It pays to create a range of forecasts, changing assumptions and probabilities in the model. If you have a modelling solution that can be recalculated in real-time then internal experts and business leaders can create and test different scenarios easily. (5) Give consumers what they want, when they want it – Getting stock levels right is one of the hardest tasks. The period just after launch is particularly challenging. This process needs to be as seamless as possible. Building a fully integrated forecasting model that compares existing stock level and automatically creates a replenishment report for every location as soon as there are significant changes is the answer. (6) Take a reality check – When possible, check your forecast against how sales of competitive products are doing to see if it’s realistic. Estimate how your market share might change as new competitors arrive on the scene. Keep an eye on sales and qualitative feedback such as product reviews, mentions in the media and customer feedback. The core team should then decide whether the assumption in the forecasting model might need to change and if appropriate, reforecast daily. (7) Have a contingency plan – A lot of new products fail. It is much better to pull the plug on a product that isn’t performing as soon as possible. Agreeing on what means failure ahead of the product launch will give you a clear cut off point that will ensure that decision can be made quickly. Imprecise planning or weak practices can result in strategic errors that have serious consequences for turnover and profit margins. For example, budgets for samples are sometimes far exceeded while product collections are developed. Why? The collection plan is badly structured, and expectations from product managers are not precise. And when budgetary constraint is rigidly applied, the quality of the product is reduced. So much of the forecasting process for a new product is based on judgement rather than statistics. That’s why collaboration, using all the quantitative and qualitative data available is key. When data is consistent and persistent, it enables decision makers to form stronger convictions and share them more rapidly, starting at the initial planning phase. Karen Clarke is area vice president, UK, Ireland and Nordics at Anaplan
Product launches are one of the riskiest and most stressful experiences a business can face, but this challenge can be overcome.
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