Artificial intelligence makes a myriad of techniques available to companies to gather information and be able, not only to sell more, but even to anticipate customer needs. The predictive analysis is the ideal tool to anticipate market demand and in this article we tell you how it is possible and through what actions can be applied predictive models.

How to predict market demand

Although it seems science fiction, it is already possible to anticipate market demand . Thanks to advances in predictive models , artificial intelligence and big data make a wide range of possibilities available to marketing and sales departments.

Counting on technologies and databases with clean and quality information, it is possible to predict market demand:

Segmentation models

The customer segmentation is key in marketing. The ability to create groups based on certain criteria and create campaigns appropriate to each group is a supported and proven strategy.

Predicting market demand through predictive models is possible by creating segmentation groups based on behaviors . Information about what a customer has bought, how much they have spent, from what location and through which channel allows you to identify common ground and discover key trends.

In the case of product-based segmentation groups , we would be facing a casuistry similar to those based on behaviors, with the difference that, by analyzing the products, it is possible to track the specific purchase trend.

Propensity models

The dictionary defines “propensity” as the “natural inclination to certain behavior.” In this way, it is very common for marketing departments to invest the vast majority of their resources in attracting new potential customers , who are likely to become a customer.

However, it is six times more expensive to get a new customer than it is to keep an existing customer . Thus, studying the propensity of a customer to stop being a customer is key to analyzing market demand. Predictive model analytics can forecast when a customer is acting alarmingly so teams can start working on strategies to maintain it.

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 web shows possible complementary products to increase the average ticket. In this way, the chances 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 totally feasible as long as the customer databases are in a position to do so. It is not possible to use predictive models to analyze market demand if customer information is not orderly, clean and of quality. In this article on our blog we give you some tips on how to analyze data and how to implement strategies to obtain better data from customer profiles.

At Cognodata we are experts in predictive modeling and we have applied predictive techniques thanks to Machine Learning and Deep Learning in more than 500 projects. For us, applying smart technologies is essential to achieve the correct segmentations and generate propensity models for the success of your business.

More Relevant Topics:-

  1. Programmatic Marketing: Why Are DMPs So Important?
  2. Business strategy: key to the success of a business
  3. Fintech Spain: an innovative sector with a great future
  4. BI tools: what are the 5 most powerful on the market?
  5. Digital profile: how to locate your potential clients on the internet?