Forecasting New Product Launches

Forecasting New Product Launches

By Marco Singh, Commercial Finance Director at Ensono

Forecasting new product launches are a tricky business with plenty of emotional baggage. They are also often, inevitably, wrong. This blog argues that when commercial finance or FP&A professionals are involved they should focus equally on model flexibly as well as the outcome.

Mission impossible?

‘If you can look into the seeds of time, and say which grain will grow and which will not, speak then unto me.’– Shakespeare

The web is littered with quotes from famous people talking about how difficult the future is to predict. The same applies to business, as outlined in this article McKinsey Forecasting.  Forecasting sales of new products is no different. It’s a tricky business packed with political and emotional baggage as well.

  • Forecast too low, and the business case never gets off the ground as economies of scale are never reflected in the return on investment modelling. 
  • Forecast too high and you risk sucking in too much scarce capital into something that is destined never to reach the lofty heights everyone is hoping for.

Think about model dynamics – not just outcome

Every product launch will have a business case, and behind every business case, there will be a forecast model. Certainly, best practices need to be followed when building the model(s) and producing a forecast - and a helpful overview of challenges and insights can be found in this study KPMG Economist Forecasting, which is still relevant today. For example:

  1. Ensure you engage with the business owners, particularly the sales teams every step of the way
  2. Get your assumptions out early
  3. Focus on data quality, your systems cannot magic away poor input data
  4. Ensure your model can handle multiple levels of inputs in the form of business drivers (non-financial KPIs), and changes in both assumptions & time periods
  5. Create a range of scenarios and get the outputs early to users

As long as your model is able to reflect how costs vary with volume (see these links for an often neglected perspective on this HBR Sales Learning Curve; ACCA Learning Curve it’s almost always better to focus on ensuring your model can be dynamic - able to reflect scenarios such as different business driver inputs, channel mix, forecasting methodologies, timeframes and volumes. 

But don’t throw the baby out with the bathwater

That is not to say the important role of Finance as an impartial seeker of accuracy should be ignored. However the finance professional should be careful how much effort to put into creating a forecast. The forecast is inevitably going to be ‘wrong’ and often the focus can mistakenly be on aspects of a model that will only deliver spurious levels of accuracy. 

The reforecast

Hopefully, there will be a monitoring process for product launches post event (and if there isn’t you should create one) and it is key that not just the revenues and volumes are tracked. 

Before producing a reforecast, every effort should be made to track the underlying non-financial KPIs as these are most likely to impact your modelling assumptions.  Sales & Marketing teams should also provide their insights based on any qualitative feedback that becomes available. If you’ve got a flexible model, chances are that it will be able to cope with the new driver data, and edge you towards a better reforecast.

Concluding takeaway – plan for new information

Commercial finance / FP&A need to be involved in forecasting new product launches, but must accept that pinpoint accuracy is a mission impossible. Instead, they should focus on building a model that can cope with flexibility and new information. Also, there can be lots of pressure to deliver a reforecast, but holding off until there is new, tangible, business driver data can make all the difference. In the words of Shakespeare: ‘Give every man thy ear, but few thy voice.’


The article was first published in Unit 4 Prevero Blog

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