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How Can Machine Learning Boost Business Analytics Practices?

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How Can Machine Learning Boost Business Analytics Practices?

How Can Machine Learning Boost Business Analytics Practices?

If you’re committed to growing your business, you’ve probably got a lot of ideas arrayed before you. Maybe you’ll apply for development grants so that you can hire more staff or invest in a bigger space. Or, you might be planning to launch a new product or expand your audience. Whatever your strategy, though, there’s one tool that can help you grow: business analytics.

Business analytics benefits business development by providing key data about your operations, offering the sorts of insights that aren’t obvious on the surface. From basic data like how many readers open your emails, to advanced predictive analytics that can boost ROI, you need high level insights to succeed in today’s competitive environment. And while basic analytics systems have come a long way, investing in machine learning can further enhance these systems.

Diving Into The Data Lake

One key reason businesses are shifting towards machine learning-enhanced business analytics is that it allows them to rapidly navigate the enormous amount of data available to them. Sometimes referred to as the data lake, businesses are faced with an overwhelming amount of data, and it’s distributed across a wide-range of platforms – buried in email, spreadsheets, and CRM software. Machine learning makes it possible for businesses to leverage this data by mining even unstructured systems and this can provide a vital strategic advantage.

The Predictive Edge

In addition to enabling access to the data lake, many businesses are eager to learn about machine learning because such knowledge helps them make use of predictive analytics. This is critical because predictive analytics fuels ROI by allowing businesses to better understand customer behavior. Based on this information, companies can target sales and attract more customers, and do so for less. The better a business’s targeting is, the less expensive it is to reach them and the more likely those customers are to respond.

Exploring The Variables

Just as modern data lakes make it hard to cull the necessary information to understand client patterns and business needs, the number of variables at play can also be a problem. How do you A/B test marketing campaigns when you actually have to deal with evaluating options A through Z? Machine learning-driven systems can rapidly evaluate those variables, presenting end-users with data visualizations and KPI dashboards that clearly represent those variables.

Already, numerous industries have begun adopting machine learning as a tool for customization, automation, and overall efficiency. The insurance industry, for example, has discovered that machine learning allows companies to offer personalized products to meet customer needs, something previously only possible because of the expertise of experienced vendors. Though this certainly raises concerns about employment for skilled workers, those same skilled individuals may simply shift to other roles interpreting client information.

Machine learning is what’s next in CRM and marketing, but businesses need the skills to leverage that information. Still, it’s much easier to learn to use this new artificial technology than to try to navigate the data lake manually. It’s the best way to make your data work for you.