New artificial neural networks are useful in a broad spectrum of ways, but one of the most popular applications is to the marketing world. Neural networks can revolutionize market segmentation and other marketing practices by bringing targeted, precise data to complex marketing operations, and taking over a lot of the labor-intensive analysis that traditional campaigns used to require.
When it comes to market segmentation, marketers are trying to divide people into distinct, manageable groups and set up goals for each of those different groups. Marketing segmentation has a lot to do with the efficacy of marketing and how well it works toward conversion.
Neural networks can be essential in market segmentation because many of them are adept at the practice of scanning large amounts of customer data and grouping customers into identifiable groups according to characteristics – an easy way to think about this is to imagine an enormous database with all of the customer demographics compiled in one easy repository. A human user could go through and read all of that demographic information manually in order to put customers together in groups, but that would take a long time. With machine learning algorithms applied to artificial neural networks, all of this cognitive work is done by the technology at lightning speed. These complex algorithms can learn and adapt over time, and get better at carrying out marketing segmentation work.
Subsequently, better marketing segmentation can control what messages marketers send to particular customers, how they reach out to target audiences, and how they are able to fine-tune customer relationship strategy. The results can boost conversion and response rates enormously, which is why so many businesses are considering vendor products that use artificial neural networks to streamline some of the marketing segmentation and other marketing or that drives profits and long-term success. Another major use of artificial neural networks is in shopping cart management – because ANNs can seek out fairly specific data from a big data field, they can help to remind customers who abandon shopping cart items, and reach out in other automated ways to really have granular interactions with particular customers.