4 Critical Reasons for Why Machine Learning Takes So Long
Machine learning has enormously benefited businesses in various ways. The Gartner research shows that more than 75% of companies will invest in big data in the next few years. So why has machine learning become so important to companies?
Machine learning can enhance customer experience by guiding and personalizing the customer journey in the shopping process which creates more possibilities for increased sales. The big data intelligently identifies to companies’ new markets that may help their businesses gain additional revenue streams.
By learning customers’ behavior from data analysis, companies are also able to produce new products and services to adapt to the changing business environment. Machine learning utilizes AI technologies to effectively focus on the target audience and optimize content for captivating the accurate customer segment. This can significantly increase the ROI and reduce the waste of traditional marketing. With big data, marketing departments can bring strong revenue growth and positive contributions.
Why machine learning takes so long?
Machine learning has proven enormous data competence in efficient marketing processes but it can take ample time to run these systems. There are 4 reasons why machine learning can be a challenge:
Not enough training data
By capitalizing on machine learning technologies, professionals can understand algorithms and run effective marketing. While machine learning still has limited ability it requires extensive data for most algorithms to perform well. This means that if there is inadequate training data for machine learning, even AI technologies cannot provide accurate analysis for businesses to understand markets and customers.
Poor quality of data
Machine learning is not perfect, so the poor quality of data delivers errors, distractions, and inaccuracies. This may affect the machining learning model to learn customer behavior properly and can provide an inaccurate analysis.
Those knowledgeable in the field has suggested to clean training data to improve the machine learning performance. Kazil and Jarmul states that “cleaning up your data is not the most glamorous of tasks, but it’s an essential part of data wrangling. […] Knowing how to properly clean and assemble your data will set you miles apart from others in your field.”
Not all the data is relevant to your business’s objectives. The training data used in the machining learning model categorizes the features according to their relevancy: relevant, less relevant, and non-relevant. If big data present irrelevant features, it could mislead professionals into considering it to be accurate information which is why feature selection can greatly improve on the machining learning model. Feature selection techniques can help machines select the target variables and remove redundant variables. It enables the refinement of more accurate data and enhances marketing efficiency.
Outfitting or Underfitting
Generalization in machine learning refers to a good quality of concepts delivered by the machine learning model even not seen in the operational process. An effective machine learning model aims to generalize any data from domains that enables data experts to predict the future model. However, outfitting and underfitting in machine learning can cause poor performance in the learning process. Luckily, there are always to avoid this issue:
– Select fewer parameters to simplify the machine learning model
– Reduce the number of attributes in training data
– Accumulate more training data and learn from experience
– Minimize the distractions in the learning process
These approaches can help improve generalization in machine learning and magnify its efficacy.
Accurate use of machine learning in marketing
Machine learning has increasingly facilitated companies in many ways in the past few decades, and it will still dominate the innovative machine learning of big data. With the help of AI technologies, businesses are able to trace customer journeys, tailor marketing strategies and create additional revenue streams.
Robotic marketer leads the marketing revolution in machine learning. The company is adept at delivering data-driven outcomes, and our robot marketer intelligently solves business problems. If you are interested in AI and machine learning, feel free to reach out to our marketing experts, and we will customize marketing strategies to your specific needs.
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