Top 8 Challenges In Marketing AI & Machine Learning Solutions

Machine Learning (ML) and Artificial Intelligence (AI) technologies have gained increasing popularity in the investment list of every IT company. Embracing the opportunities that come with using AI and ML technology for marketing is something that needs to be done by companies to stay competitive.

The digital haul that has exploded in the industry in the last decade is only going to continue its growth over years. Although the AI-powered marketing platforms are becoming simpler and common to use, there is still some pitfall that comes with AI.

Top 8 Significant Challenges in Marketing AI & ML Solutions

Top 8 Challenges In Marketing AI & Machine Learning Solutions. Undoubtedly, AI-based marketing tools can turn out beneficial for your business. However, there are several reasons that instead of boosting your business, AI leads to high failure rates across data science, machine learning projects, and analytics. So, in this blog we are about to learn the major challenges anyone can face in Marketing AI along with Machine Learning Solutions. (Future of Artificial Intelligence: The Fourth Industrial Revolution)

1. Access To High-Quality Data

Data is the basis for machine learning. One of the challenges in ML is to assure accurate information and results. Be it data Machine Learning or AI both rely on data to understand undergoing algorithms. For the success of AI initiatives, access to meaningful and clear data that can help in solving the problem at hand is essential. But, the data provided by the enterprises are noisy, unstructured, biased, and full of errors. Also, several companies neither have a data infrastructure nor enough quality data.

To avoid the challenge of accessing high-quality data, a company must have the master data preparation tools that can be utilized for formatting, data cleansing, and certain standardizations before placing data in data marts and lakes. If an enterprise overlooks the importance of quality data, the AI or ML project can derail easily.

Read: Top 11 Tools and Libraries for Data Visualization

2. Balancing Accuracy

The balance between model interpretability and accuracy in prediction can only be achieved by selecting the appropriate model approach. Where higher accuracy means hard to interpret and complex models; easy interpretation uses simpler models that compromise with accuracy. Instead of the traditional black-box technique where only minimal insights are generated, nowadays, the AI team uses white-box models. WBM offers clear explanations on how they generate predictions, how they behave, and variables that are influenced by the model. If you are still using the black-box model then it can create trust issues with the customers by decreasing transparency. So, using WBM can save you from balancing accuracy and building trust at the same time.

3. Detecting Problems for Business

AI is an amazing and powerful tool but it cannot be a remedy for every business problem. If you are building AI just because everyone is doing the same to solve any problem you through without specifying objectives is a way to failure. AI is incredible when it comes to discovering customer patterns, searching insights, and moving through a huge amount of data.

To gain success, you will need to prioritize complex and hard to solve problems with clear objectives. Then, you can define the criteria for success and measure it with relevant metrics.

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