Machine Learning In Data Analytics: How It Works For Your Business?

Machine Learning in Data Analytics: How It Works for Your Business?

In this post, we will try and answer the question – how does machine learning in data analytics work for your business?

In part one of this blog post, we looked at how artificial intelligence (AI) can help business managers or owners take informed decisions beneficial to their business.

We don`t think so.

Many organizations can benefit from traditional data analytics without the need for more complicated machine learning (ML) applications. Data analytics can help quantify and track goals, enable smarter decision-making, and then provide the means for measuring success over time.

For all of that, in many cases, traditional data analysis is enough to do the job. You can generate reports or models of what happened in the past, or of what’s happening today, getting useful insights to apply to the organization.

The Difference Between Traditional Method and ML-based Algorithm

One key difference between the traditional method and ML-based algorithm is that the former applies a strict mathematical approach, while machine learning (ML) is more data-oriented. In short, if your business really has vast repositories of big data, and making sense of it is all is beyond the scope of your team of human analysts, then deploying machine learning in analytics is better.

Whether you’re trying to estimate future sales, optimize your supply chain, or choose the optimal product price, forecasting is about predicting the future using past data. When it comes to analyzing such large amounts of data, that too in real-time, nobody can beat a machine, right?

Progress in recent times in neural networking is pushing ML technology to new levels, like providing businesses answers than mere models to predict answers.

Product price optimization is one of the many use cases of ML. AI tech writer Igor Bobriakov explains:

Having the right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitor`s offers. The tools for data analysis bring this issue to a new level of its approaching.

…the data gained from the multichannel sources define the flexibile prices, taking into consideration the location, an individual buying attitude of a customer, seasoning, and the competitors’ pricing. The computation of the extremes in values along with frequency tables is the appropriate instrument to make the variable evaluation and perfect distributions for the predictors and the profit response.

As ML evolves as a predictive analytics tool, coupled with business intelligence tools like Tableau, business managers can make even better sense of their big data.

Leading organizations are using machine learning tools to extract valuable insights from raw data to solve complicated, and data-oriented business problems.

Machine learning algorithms enable computers to discover various types of hidden insights and play a key role in expanding the business analytics community.

Machine learning in business improves business operations and enhances business scalability for global companies. Companies are making use of AI to enhance customer loyalty, automate finance, detect fraud, and measure brand awareness.

If ML is implemented in the right way, it can serve as a boon to a wide variety of business problems, and predict customer behaviors. Major technology giants including Google, Adobe, Microsoft, and Amazon are using their own Cloud Machine Learning systems.

Machine learning has become popular because of different factors including growing volumes, affordable data storage, and cheaper computational processing. Hence, businesses, if they want, can learn how to use machine learning in their operations to get benefits from it.

3 Use Cases Where Machine Learning in Data Analytics Works

1. Marketing: Common use case of ML is in identifying and acquiring prospects with attributes similar to existing customers. They can also prioritize known prospects, leads, and accounts based on their chances of taking action.
2. E-commerce: To predict customer churn or even fraudulent transactions.
3. Customer service: ML can be used to process outcomes from earlier encounters with clients like total time taken to resolve a ticket, response-time of customer relationship executive, and so on.

When enterprises employ ML-based predictive analytics, it is essential to discover hidden patterns in unstructured data sets for new information.

But do remember, to build comprehensive data analysis and predictive analytics strategy, an enterprise requires big data and progressive IT systems, so the cost factor, too, has to be factored in. Till such time, your organization can get along using the traditional data analytics methods.

4 Top Benefits of Machine Learning in Business

1. Reduces manual data entry: Today, the most common problem faced by businesses is inaccurate and duplicate data. Both, ML and predictive modeling algorithms use discovered data to avoid such errors. Hence, employees can make better use of their time by deploying machine learning.
2. Detecting spam: Machine learning adds more value to businesses by detecting spam. Earlier, email service providers were making use of already-existing, and rule-based approaches to filtering out spam. Nowadays, new rules have been created by spam filters with the help of neural networks to detect spam, and fraud messages.
3. Financial analysis: With huge volumes of both accurate, and quantitative historical data, it is abundantly used in financial analysis. It is widely used in finance for detecting frauds algorithmic trading, managing portfolios, and loan underwriting. Furthermore, upcoming applications of machine learning in finance will cover customer service, various interfaces for sentiment analysis, and chatbots.
4. CLV prediction: Today, the most common problems faced by marketing professionals are CLV prediction and customer segmentation. Businesses can use large quantities of data to derive valuable business insights as they can access such data easily. Both, data mining and ML can understand the browsing, and purchase histories of users, and based on that send the best offers to them.

References:

thoughtspot

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