Demand analysis: why it’s so important and how to do it using forecasting and AI


    Marketing intelligence is about identifying your company’s market through, among other things, the analysis of purchasing behaviour, selection criteria, opinions towards your competitors, and your current and potential customers. The aim of marketing intelligence is to help you to detect current and future needs.

    Marketing intelligence also encompasses demand analysis, a strategic tool underpinning business decisions. Analysing demand means forecasting consumer and customer behaviour.

    Against a backdrop of growing uncertainty, however, it is increasingly difficult to build a reliable forecasting model which encompasses all the factors that are driving the volatility in demand. Artificial intelligence-based methods allow companies to take multiple variables into account to build a realistic and reliable forecasting model.

    One of the features of these innovative methods is that they do not require any hypothesis on the model followed and the relation between the forecast value and the variables considered.

    In the view of experts, such as the Osservatorio Industria 4.0 at Milan’s Politecnico University, analysis of the literature shows that applying AI methods results in better forecasting results than traditional methods; this is why the market for AI-based forecasting systems continues to grow (see chart). 

    Predicted growth of market for AI-based forecasting – Source: Gartner

    What demand analysis consists of

    Demand analysis consists of market research which aims to understand the characteristics and dynamics behind demand for a product or service. Demand analysis has significant strategic value; it allows companies to identify the critical factors for success and optimally establish growth strategies, business plans, marketing and communication campaigns and innovation projects.

    The benefits of demand analysis translate into:

    • Increased product ROI;
    • Smoother go-to-market;
    • More effective communication campaigns;
    • Greater segmentation due to understanding the actual needs of the closest target customer base to your offer.

    Traditionally, forecasting demand is a form of predictive analysis, in which the process of estimating customer demand is analysed using historic data. By using artificial intelligence, organisations can take advantage of machine learning algorithms to forecast changes in consumer demand in the most accurate way possible. These algorithms can automatically recognise models, identify complicated relations in large data sets and detect signs of changes in demand. 

    Demand analysis and AI forecasting

    Using artificial intelligence allows companies to significantly improve the quality and reliability of the forecasting they base their business decisions on. Specifically, AI means they have a forecast that is:

    • Accurate: this translates into more precise production planning and more efficient inventory management. Over the long term, a demand probability curve can be built which will help to balance an excess or a shortage of stock. Being able to confidently give suppliers a more reliable idea of demand also reduces the chances of an interruption to product supply;
    • Realistic: models based on artificial intelligence allow companies to take many more independent variables into account than traditional models. Considering dynamic factors – such as time, marketing activities and local events – as part of a forecasting model allows analysts to simulate a more complex environment and therefore make more reliable decisions;
    • Reliable: artificial intelligence algorithms can accept a range of variables as inputs and can identify which are the most significant; this means they don’t require any preliminary hypothesis on the relation between variables.

    Finally, sharing accurate, realistic and reliable forecasts across the supply chain contributes to more effective management of the other processes in the chain. Data collected in real time, as required by AI algorithms, make it easier to quickly respond to sudden changes. This, in turn, makes the supply chain much more resilient.