The recent years have seen a rapid growth in the quantum of data created which has led to a greater need to leverage the big data and to unearth correlations in scattered and seemingly unrelated raw data to the benefit of businesses. There has been an influx of investment in the field and accordingly, more analytics courses to help working professionals and interested individuals to equip themselves with these most wanted skills.


One of the important developments in the field of data analytics and big data has been machine learning. Machine learning (ML) algorithms provide computers with the ability to study and analyze data and improve itself from experience rather than explicitly programming every step and without much human interference. The computers observe data and learn patterns to use it in the future for better decisions and actions. ML can effectively analyze and large volumes of data faster and can provide accurate results for businesses to improve their outcomes. It is touted as one of the most crucial things that will shape the future of data analytics.


Getting maximum off big data with ML

Boosting predictive analysis: The right ML tools can help organizations to use big data and engage in predictive analyses. Using the ML tools, past behaviors can be studied based on the data at the businesses disposal and future course of action can be predicted. So, businesses can anticipate how customers are going to behave and prepare accordingly. This has helped businesses forecast churn rate, understand reasons for customer dissatisfaction and provide seamless customer experience, in turn, reducing costs in the long-run.


Improved Risk Management: By using ML tools, big data can help businesses identify potential risks by analyzing large volumes of data relating to risky situations, and accordingly, put in measures to mitigate risks or reduce their impact. This has especially been useful for e-commerce companies and financial institutions in better fraud detection and significantly reducing financial risks.


Enhancing Segmentation: In today’s day and age, most businesses are moving away from providing a generic product to customers and generic ad campaigns to market these products. Marketers across the globe want to personalize products/ campaigns for their target audiences. Segmentation is a step to this end and using clustering algorithms, ML segments potential customers into homogeneous groups based on certain characteristics like demographics, preferences, purchase history, etc. ML also probes further into the data to look for trends and patterns. Through segmentation, brands can target customers better, build brand loyalty and improve sales.


Enabling Customization and Hyper-personalization: Segmentation is only the starting point, brands today want to tailor the product/ marketing campaigns further to create AHA moments for customers. ML has enabled customization and hyper-personalization possible. By studying individual customer’s patterns, needs, journey, trends and preferences, etc. from the large volume of data generated, ML customizes the products, ads and recommendations that individual customers see and automates the experience based on the particular customer’s behavior and needs.


More brands and industries are adopting machine learning to leverage big data and enhance their business outcomes. Even senior marketers are equipping themselves with data analytics certification to give themselves and their clients a competitive edge.

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