Tackle Business problems with Data Science!

Tackle Business problems with Data Science!, Each aspirant data scientist must believe that their work begins when someone else provides them with a dataset and a clearly defined metric to aim for, such as accuracy.

However, it doesn’t. The process begins with a business challenge that you must comprehend, frame, and resolve.

Additionally, We’ll demonstrate how to practice this data science skill using a real-world scenario in this article.

The Role of Data Science in Preventing Fraud »

Tackle Business problems with Data Science!

A Data Science Project’s Starting Point

In the real world, projects using data science begin with a business issue. They naturally move a crucial business metric (KPI).

Translating a business problem into the *correct* data science problem is the data scientist’s responsibility and then finding a solution.

You must do two things in order to convert a business challenge into *the right* data science problem:

  1. Pose inquiries
  2. Investigate the data for hints.

Creating a terrific data science solution for the wrong business problem is the most irritating thing in the world.

Not Satisfied with statistical significance (p-value) »

Let’s give an illustration.

Example

Consider yourself a data scientist at a prestigious ride-sharing app business. Your product lead also says: “We want to decrease user churn by 3% this quarter”

When a user decides to cease using our ride-sharing service, we remark that he/she is churning. Different factors contribute to user turnover.

For instance:

  1. “Another ride-sharing app provider (i.e., direct rival) is providing better costs for that geo.” (Pricing issue)
  2. “The wait periods for cars are too long.” (Supply issue)
  3. “The app’s Android version is quite sluggish.” (Performance issue with the client app)

How to Apply AI to Small Data Sets? »

By posing the appropriate questions to the other team members, you can create this list. You must comprehend the app user’s experience from HER perspective.

Churn is frequently caused by a mixture of these factors rather than any one of them alone. Which one should you concentrate on, then?

This is the time to explore the data and use your excellent data science abilities.

You investigate the data to determine how likely each of the aforementioned possibilities is. The result of this analysis is a single theory you ought to investigate further.

Your approach to resolving the data science problem will vary depending on the hypothesis.

For example:

Scenario 1: Better Prices Are Being Offered by a Competitor (Pricing Problem)

One option would be to identify or anticipate the user group most likely to churn (perhaps using an ML Model) and then send push notifications with tailored discounts to that group.

You must do an A/B test to determine whether your solution is effective, thus you must divide a portion of the app’s users into two groups:

  1. the A team. This set of users will not be eligible for any discounts.
  2. the B team. Users from this group who the model predicts are likely to leave will get a price break on their subsequent visit.

Machine Learning Impact on your day-to-day life! »

More groups (such as C, D, and E…) might be added to test various price points.

Scenario 2: The wait times for cars are excessive. (Supply Issue)

Instead of a pricing issue, there aren’t enough drivers available to pick up customers in this situation. Since the problem is unique, the solution must be too.

You can encourage divers to fill these slots by identifying the place and time where supply is too low and offering a pricing incentive.

By doing so, you can better balance supply and demand while cutting down on car lines.

Scenario 3: “The App’s Android Version Is Very Slow” (Problem with App Performance)

Imagine you investigate the app’s memory usage data and learn that the most recent version of the app uses approximately twice as much memory as the older versions.

App memory usage should be broken down by app version (Image by the author)

This is odd, so you go and inquire with the customer service staff to see if they have heard from any users.

Why Do So Many Data Scientists Quit Their Jobs? »

It turns out that the majority of customers cease using the app and utilize an alternative instead of contacting assistance.

However, a few customers have continued to voice their dissatisfaction, claiming the latest app version was not “particularly responsive”.

How would you address this? Present the breakdown of app use churn by version to the frontend developers and persuade them to publish a new, more performant version of the app.

Summary

The essential data science competency that distinguishes a senior data scientist from a junior data scientist is the ability to translate business problems into *the right* data science problem.

To reduce the list to one, ask the correct questions, make a list of potential answers, then investigate the data.

Surprising Things You Can Do With R »

You may also like...

Leave a Reply

Your email address will not be published.