How to Predict Future Demand to Optimise your Marketing Effort
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Many businesses encounter the issue of both over- and underselling and seek innovative ways to deal with that, as any of these scenarios could have a primarily negative outcome for the company. For example, in the recruitment industry, you do not want too little applications for a vacancy because this increases the probability that you cannot deliver new employees. On the other hand, in the travel industry, it costs money when a type of accommodation is not booked and thus left unrented. Additionally, you do not want to invest marketing budgets in vacancies or accommodations that will most definitely be filled or sold. Luckily, with demand forecasting, this can be avoided.
Forecasting demand is of great value for all businesses for many different reasons, but it is especially important for businesses that are subject to one of the following situations:
- Flexible Supply – Flexible Demand: the future supply of the product or service is flexible and the company has some sort of influence to modify it.
- Fixed Supply – Flexible Demand: the future supply of the product or service you are selling is fixed for future time periods and the company has rarely any influence in modifying it.
With a demand forecasting system, the expected demand (vacancies or reservations of accommodation) is predicted for the upcoming 52 weeks. This includes the future offer to determine whether you need to take action. In other words: the demand forecasting system predicts the demand for a certain product per week, up to 52 weeks into the future. This is compared to the delivery of the product for each individual future week. When there is a gap between supply and demand, the product is flagged and actions can be taken to proactively prevent over- or underselling.
A machine learning forecasting system enables companies to gain a good insight into future demand, which is compared with the future supply (stock). You can read more about how that works and what positive effect it can have on companies in the longread.