Artificial intelligence provides companies with endless techniques to gather information and be able, not only to sell more, but also to anticipate customer needs. Predictive analytics is the ideal tool to anticipate market demand and in this article we tell you how it is possible and through which actions predictive models can be applied.
How to predict market demand
Although it may sound like science fiction, it is already possible to anticipate market demand. Thanks to advances in predictive modelling, artificial intelligence and big data offer marketing and sales departments a wide range of possibilities.
Using technologies and databases with clean, quality information, it is possible to predict market demand.
Segmentation models
Customer segmentation is key in marketing. The ability to create groups based on certain criteria and to create appropriate campaigns for each group is a proven and supported strategy.
Predicting market demand through predictive modelling is possible by creating behavioural segmentation groups. Information about what a customer has bought, how much they have spent, from which location and through which channel allows you to identify commonalities and uncover key trends.
In the case of product-based targeting groups, this is similar to behavioural targeting, with the difference that, by analysing products, a specific purchase trend can be tracked.
Propensity models
The dictionary defines “propensity” as the “natural inclination to a certain behaviour”. Thus, it is very common for marketing departments to invest the vast majority of their resources in attracting new prospects, who are likely to become customers.
However, it is six times more expensive to acquire a new customer than to keep an existing customer. Thus, studying a customer’s propensity to churn is key to analysing market demand. Predictive modelling analysis can predict when a customer is acting alarmingly so that teams can start working on strategies to keep them.
Smart recommendations
Many websites and apps, mainly in retail, recommend a class of products related to previous searches or purchases. Amazon is a clear example of smart recommendations: based on the user’s history (searches, purchases, wish lists…) the website shows possible complementary products to increase the average ticket. In this way, the possibilities of creating a cross-sale thanks to a correct filtering of recommendations are very high.
Importance of data management in predictive models
These realities are entirely feasible as long as customer databases are in place. It is not possible to use predictive models to analyse market demand if customer information is not ordered, clean and of high quality. In this blog article we give you some tips on how to analyse the data and how to implement strategies to obtain better data on customer profiles.
At Cognodata we are experts in predictive modelling and we have applied predictive techniques thanks to Machine Learning and Deep Learning in more than 500 projects. For us, applying intelligent technologies is essential to achieve the right segmentations and generate propensity models for the success of your business.