Nowadays machine learning is already very useful for marketing.
Alongside the creative aspect, marketing also always has an analytical one: user behavior statistics (buying behavior, number of website visitors, app usage, and so on) play an important role in the decision of certain advertising solutions. Usually the rule is that the larger the amount of data, the more information can be extrapolated. In order to develop such a mountain of features, intelligent programs are needed.
At this point Farrago intervenes, recognizing the recurrent models, making reliable forecasts and providing more reliable analyzes. It simplifies the representation of analysis results, provides a correct representation of data and information. This is particularly important because it allows us to understand what has been discovered and expected. Let us remember that with huge data streams it becomes difficult to represent the results of the evaluations by yourself.
Furthermore, machine learning can also exert an influence on the creation of contents: as happens for example in generative design. Instead of designing the same path for all users, consisting of the steps that the customer takes to buy a product or service, dynamic systems are able to create personalized experiences thanks to machine learning.
Machine learning can also be used to better organize chatbots. The abilities of the current chatbots are usually very limited and the possible answers refer to a manually managed database. A chatbot based on a self-learning system and which has good vocal recognition manages to convey to customers the feeling of really communicating with a person. An additional benefit of machine learning for marketers is recommendations.
Among the important success factors of Farrago there is also the ability to predict what the user will want to have.
Self-learning systems are able to recommend other products to the user regardless of the data collected. What previously was possible to do only on a large scale (“Our customers like product A, so most of them will also be interested in product B”), now it’s possible even on a smaller scale thanks to our modern programs (“Customer X liked products A, B and C, so she will probably also be interested in the product D”).
In summary, we can see that self-learning systems will influence four different aspects of online marketing:
Programs that work with machine learning and that have been well educated to process huge amounts of data and then make forecasts for the future. Marketing experts are able to draw more precise conclusions about the success or failure of advertising campaigns and solutions.
The analyzes take time if done manually. With Farrago the speed of work increases and therefore it is possible to react more readily to changes.
Through automatic learning it is easier to automate procedures. Since modern systems have the ability to autonomously adapt to new circumstances with the help of machine learning, even complex automation processes become possible.
Software is able to assist countless customers. Since self-learning systems detect and process data from the individual user, they can deal with these clients exhaustively. Customized tips and customer journeys eveloped specifically help to apply marketing measures more effectively.