Post by rakib123 on Mar 10, 2024 22:32:47 GMT -5
Or mixed. , really knowing our client and their circumstances, to be brands that connect with people. Let's imagine that we can cross-reference all the data from our brand's Social Ads Campaigns with all the data from the portfolio of clients who have purchased the promotional products in the last 2 years. What type of customers have purchased, when they purchased, where they purchased, what was their relationship with the brand once they purchased the product, behavior of online versus offline customers, average purchase by geolocation, problems that were found in the purchase process.
purchase and shipping… With all this data, the Social Ads campaigns Chinese American Phone Number List could be much more refined at the level of audiences and micro-publics, copy, products, locations, interests... to measure and evaluate results again. Reducing investment and maximizing sales. BI doubts To finish the post I want to delve into the darkest part that this whole explosion of data, BI and Big Data, machine learning and artificial intelligence (AI) entails. It is true that the use of real-time BI for decision making and the design of predictive models is increasingly advanced. Training complex algorithms helps find solutions to increasingly complex problems.
So far phenomenal, but we cannot ignore the risks that must be controlled. – Whoever designs the algorithm (especially if it is black box) is the one who conditions the final answers. It is possible to manipulate the process from the beginning for good or bad purposes. For this reason, white box algorithms are advocated where the entire process is transparent. No to black box algorithms and yes to white box algorithms. – There is no single version of the truth. Whenever you work with data this sentence is universal. You can see the data one way (to do evil) and another way (to do good).
purchase and shipping… With all this data, the Social Ads campaigns Chinese American Phone Number List could be much more refined at the level of audiences and micro-publics, copy, products, locations, interests... to measure and evaluate results again. Reducing investment and maximizing sales. BI doubts To finish the post I want to delve into the darkest part that this whole explosion of data, BI and Big Data, machine learning and artificial intelligence (AI) entails. It is true that the use of real-time BI for decision making and the design of predictive models is increasingly advanced. Training complex algorithms helps find solutions to increasingly complex problems.
So far phenomenal, but we cannot ignore the risks that must be controlled. – Whoever designs the algorithm (especially if it is black box) is the one who conditions the final answers. It is possible to manipulate the process from the beginning for good or bad purposes. For this reason, white box algorithms are advocated where the entire process is transparent. No to black box algorithms and yes to white box algorithms. – There is no single version of the truth. Whenever you work with data this sentence is universal. You can see the data one way (to do evil) and another way (to do good).