Introduction
Digital marketing is the core of every business,Marketing techiniques are upgraded to attain the goal of the company easily.The business will not grow if you are following outdated marketing techiniques.Digital Marketing is simply means the adoption of marketing techiniques with the latest technologies.As increasingly shifts their focus online,digital marketing becomes even more critical in reaching and engaging with target audience
The traditional marketing techiniques includes the printed media,broad casting,direct mail, advertising etc.These practise causes more money and its considered as the time consuming methods.The reach of these techiniques are limited.
In wider terms the digital marketing is the use of websites,appa,mobile apps,social media,search engines,and other digital means to promote and sell product and services.There for considering these factors should be the responsibility of the digital marketer.care ful scrutinizing of every details is effective
Key Points
- Digital marketing promotes products and services through digital channels
- Digital marketers have a number of tools to measure the effectiviness of their campaigns
- One of the biggest task for the digital marketets to stand ou in the competitive digital world
Technological Advancement
Artificial Intelligence and Machine Learning
Artificial Intelligence
Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans. AI can predict consumer behavior, personalize marketing messages, and optimize ad targeting, making campaigns more efficient and effective.Artificial intelligence works by using algorithams.eg: chatgpt,tesla,betflix,google transalate
Machine Learning
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
- A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
- An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
- A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.
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