The amount of data is growing? and a person cannot process so much information manually. The problem is solved by marketing canada phone number dataautomation ? which includes machine learning – a method of artificial intelligence. Robots collect and study big data. And machine learning makes the information useful for marketing purposes. Marketers begin to:
- Correctly understand and interpret the collected data. The use of machine learning eliminates the human factor and subjective assessment. Information about orders? purchases? feedback? preferences is not lost and is taken into account in data analysis.
- Study the client at 360 degrees. AI technologies are capable of processing terabytes of internal (customer base? results of email and other campaigns) and external (comments on social networks? likes? reposts? etc.) data to find patterns. The result of such analysis will be precise personalization of offers.
- Predict trends. Machine learning in marketing is needed to identify future trends in customer behavior? user tastes in the future. This approach is useful for improving products? services? and content.
Machine learning forecasting is different from trendwatching? a method of finding trends here and now. Because ML forecasts trends based on past data and “looks” into the future.
Examples of trend forecasting
Since 2018? the Pyaterochka chain has been using machine learning to customize special offers and promotions in the future. they lead to new ideas and conclusions relies on customer reactions and sales volumes? although the data is anonymized. As a result? the company receives a personalized approach.
Walmart predicts whether customers are likely to purchase a product and personalizes product offerings on its website. Predictive analytics also manages delivery times and other processes to ensure that customers receive their products on time.
Content and advertising platforms also use machine learning. So? in VKontakte? algorithms are needed to optimize advertising campaigns? in particular? without ML it is impossible to offer an audience similar to the advertiser’s clients. That is? the robot predicts who else would like the brand’s offer.
chine Learning and Data Analysis: Challenges
Dependence on data quality . Predictions of the future are made based on existing information. If the information is insufficient or incomplete? incorrect? nothing can be corrected at the moment.
Distance from offline business . Machine united states business directory learning is not able to fully cover the actions of users in reality. If in the digital world all orders? user actions? reactions to content are tracked? then in an offline store this becomes more complicated. But it is still possible to use AI.
The need for significant amounts of data . These are not hundreds? but even thousands of units. If the sample is insufficient? there will be no result.
Cost. The forecast must be done by a trained specialist. Resources are also spent on the system itself? setup? integration. Only a business with a sufficient budget can afford ML.