What are top uses of Machine Learning in business Analytics and Sale

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What are top uses of Machine Learning in business Analytics and Sale

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I am learning Machine learning from beginning and want know more about its uses in Business analytics? What are top uses of Machine Learning in business Analytics and Sale?

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September 13, 2020 at 5:15 PM

What are top uses of Machine Learning in business Analytics and Sales

The top uses of Machine Learning in business Analytics and Sales, I found myself thinking a lot about a more fundamental way that ML could be used by our business and our clients. As I was studying my topic, I found myself thinking about our own processes. In the end, it was a deeper understanding of the process that allowed me to uncover, define and ultimately utilize the concepts that I'm going to unveil in this post.

There is a very clear path that has led to this point: A simple, yet powerful, ML pipeline. Let me explain.

A lot of folks have a misconception that a machine learning pipeline is an implementation of an algorithm. It certainly isn't that, and if you're reading this and you think this is how you do a machine learning pipeline? you're wrong.

In fact, it's an extension of an algorithm, an evolution of that algorithm, an evolution that is simply being applied to the problem domain that you are trying to solve. Think about it this way: An ML algorithm might predict a product or product category based on a user demographic. Or, suppose you are in charge of building and implementing another machine learning algorithm. Perhaps it's a recommendation engine. Or, the predictive power of Google's search suggestions is built in. You can build a model of any kind, and then use it to build the pipeline at hand. Or, this is the definition of an "ML pipeline" as I use the term, which is an iterative process in which algorithms are applied until you have a model that you're satisfied with.

For example, imagine that you are in charge of building the machine learning pipeline for a product called "SalesPro."

You find an example using this pipeline in some blog. What this tutorial does is show you some of the steps that are needed to build and implement the sales pipeline. One step may be to build an MVP pipeline which includes creating the SalesPro API and generating an instance of SalesPro ? the service that SalesPro will use to sell products or services to customers.

How do you do this process?

At the very start of this tutorial, I explain that you begin by creating a Google Spreadsheet and saving a copy for each member of your sales team. You'll then create an instance of this spreadsheet for each Sales person which we'll call a Leader. Every time SalesPro is used to sell products or services, you open a copy of this spreadsheet so that everybody knows what a Salesperson does and what their role is in the Sales team.

Also, in this spreadsheet, you add your Leader and his or her team members to the list. After that, when SalesPro is used to sell products or services, the spreadsheet goes dormant until you make any updates.

You're able to build this for different product services because the product that you build depends more on who or what is using the product. You could create a product for "Marketing" or "Sales" and the Sales people are the Marketing personnel. Or you build a product for "Customer Service" and only Salespeople are the Customer Service personnel. There is definitely room for flexibility in the pipeline.

So you create the Spreadsheet, save it, and the Process Begins:

You create several examples with these formulas. For example, say that you were building your sales pipeline for "SalesPro," you might need to:

  • Create an MVP and calculate a cost of sales in terms of sales tax.

  • Create a Leader and calculate a minimum number of sales.

  • Create MVPs for each employee.

For each customer who needs to purchase a product, you calculate their order values in order to make the order flow.

Finally, you generate an order for each product and calculate the cost of shipping and receiving products for each customer.

Now, you create additional examples for different product types, and you have multiple cases to work from. In the end, you have a set of pipelines, which can be applied to many different business problems and applications.

It is not that simple to execute all these steps all the time. It is more of an evolution process. Instead of developing an algorithm each time that you need to build one, I recommend devising a "diet" of how the pipeline works so as to minimize redundancy and maximize the usefulness of your pipeline.

This is an example of what I've built so far for my business. It's really not hard at all.

We've got Salespeople and Leaders, where everything can be calculated based on different customers. For example, "Marketing" customers have higher values than "Customer Service" customers, and Marketing Customers should have a higher order value than Customer Service customers. The order value (or customer number) is calculated using simple formula. A single input is used to calculate the number of sales.

Learn more at Data Science tutorials section.

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