As we mentioned at the beginning of this article, one of the biggest challenges in demand prediction is the number of factors that can affect market behavior and product demand . These variables can be external (a pandemic, competition, macroeconomic factors, etc.) or internal. In the latter case, we would be talking about, for example, actions related to communication or distribution.
The good news is that, to deal with all this, there are ways to detect these variables and include them in a predictive algorithm that improves the accuracy of companies' internal models. Through ML and AI models, Advanced Analytics facilitates the detection of patterns that allow for high-precision predictions. If we add to this a personalized algorithm that is fed by external data, we have a perfect combination of elements that will allow companies to anticipate the impact of variables that affect them, and make more effective and productive decisions.
New Trends in Demand Forecasting
Beyond traditional methods, new trends are emerging in the field of demand forecasting. The use of Big Data and real-time analysis are revolutionizing the way companies approach this challenge.
Giants like Amazon and Walmart already have real-time vietnam number data analytics to adjust their inventories and marketing strategies almost instantly . This rapid response capacity not only improves customer satisfaction, but also optimizes inventory levels, reducing costs and waste. Since the arrival of democratized artificial intelligence, this aggressive competitive advantage is no longer only in the hands of large corporations like this one; medium-sized companies with a solid data history (even small ones) can now develop their own demand prediction models with the support of specialized companies, such as the Spanish company Kraz , focused on MMM and LMM models for marketing and the field of data with digital impact.
In addition, the integration of social media data and Internet search trends allows companies to anticipate changes in demand with greater accuracy. This data provides a broader and more up-to-date view of consumer behavior, allowing for proactive adjustments in product offerings.
Kraz's Guide for CMOs on Demand Forecasting Models
It is clear that demand prediction is redefining the business profit landscape. Companies that invest in these models not only improve their operational efficiency, but also gain a competitive advantage in an increasingly dynamic market.
In this context, to help Chief Marketing Officers (CMOs) understand and apply these models, Kraz, an analytical consultancy, has developed a detailed guide that you can now download for free from its website . In it, they offer a valuable roadmap for CMOs who want to lead this transformation in their organizations.