An Intro to Creative Data Predictions


Inspiring non-technical people to make creative data predictions.

Obviously, we are always looking ahead. When Googling “the best quotes about the future,” you are taken to a predictable listicle of the top 10 aggregated future quotes that say something like:

Study the past if you want to define the future. - Confucious

I couldn’t think of a better quote when it comes to data predictions. While data can make everyone an oracle, not everyone knows the capability of creative data predictions. 

This post is meant to help inspire you to use the Obviously AI platform effectively and creatively. Going back to our first post, we stated the goal of this blog was to inspire creativity in our users by providing them with thoughtful posts on creativity, the future of work, and data literacy in relation to machine learning. In other words, when creating content, we think about the user’s decision-making process and how they can be creative with machine learning to improve it.

Examples of Creative Data Predictions

The only limit to machine learning is creativity. On our blog, we've been publishing posts on the possibilities of machine learning and providing use cases on what you can do with our platform.

Here are some examples:

Later in this post, we will discuss even more possibilities of data predictions and using no-code machine learning in your workflow. But let’s discuss how you should approach machine learning. 

If you want to hear more about AI creativity, here’s a video I like on using AI in the creative process:


And if you want to discuss more on how you're being creative with machine learning, reach out to us via Twitter

Things to Know Before Making Your First Data Prediction

If you’ve made it this far, you’re probably tired about learning about the importance of creativity. The last thing I need say about creativity and data predictions is the only limit in machine learning is your creativity.

Let’s get started on how to use the platform, if you’re using it for the first time. Take note, this isn’t necessarily a tutorial video. You receive tips on how to use Obviously AI when you sign up. This post is to inspire and inform on all the other workings of data predictions and what you need to know when getting started.

To Make Creative Predictions, You First Need Data.

And the more you have, the better decisions you can make and more creative you can be. There are many ways to go about collecting data and using it to make predictions. 

You can:

  • Collect data from surveys
  • Observe and record data
  • Interview subjects
  • Conduct a focus group
  • Use online databases such as Kaggle or Google datasets 

Here’s an awesome post I found all about using Kaggle for your data science experiments.

Isolate a Business Problem and Aim to Solve It

Data can be qualitative (numbers) or quantitative (attributes) and easily be inaccurate depending on your data collection procedures. 

Before collecting data, I recommend isolating a business problem, defining it, and choosing a metric you want to better understand. I also recommend getting the maximum information out of a few variables as possible. You don’t want your prediction model to use unnecessary variables and ruin the trend you’re trying to clarify. 

Data cleaning is important to remove any possible errors and delete duplicate entries. If you use the Obviously AI platform, we make sure your data is squeaky clean before it predicts an outcome. 

Humans Can Create Biased Data

One of the biggest flaws in humans collecting data is unconscious biases which could affect the overall prediction you're trying to make. Before you try to use machine learning to make predictions with your data, peer review your findings and discuss your thought process behind collecting data. 

AI is only as powerful as you allow it to be. To truly be successful, you need accurate data to make accurate predictions. You can’t be Confucius if your data is inaccurate. 

Check out our blog post to learn more about bias data predictions.

Once you have a proper dataset, Obviously AI allows you to drag-and-drop your data directly into the platform.

How to Creatively Predict Based on Data

The value of predictions is limitless. Smarter business decisions are made every day using AI technology, creating a competitive edge for those who prioritize data collection. Now business users can predict customer churn, credit card fraud, labor costs, demand, daily foot traffic, maintenance, competitor analysis, financial risks, etc., using historical data. 

Let’s do a mini-use case using public data on Mt. Rainier. 


I downloaded the data from Kaggle into a CSV file, looked it over, and decided I wanted to ask a question in plain English.

What causes high success rates when summiting Mt. Rainier?

I asked this somewhat selfishly because if I did choose to summit the active volcano for the first time, I would do it successfully based on data and look like an all-telling alpinist oracle when passing by unlucky climbers who chose to descend. 

From a business perspective, with this data you can also:

  • Predict what products climbers need to ensure maximum success rate and market to them accordingly.
  • Build content around the top drivers of summit success i.e route and gear guides.
  • Predict sales and a perfect price point for a product based on seasonal weather conditions. 

Additionally, some other data you can gather to make your predictions more powerful include success rate depending on if the climber had certain products (crampons, ice axes etc.), when guided tours are most valuable or necessary, forecast demand ahead of the climbing season. These are just some of the possibilities. 

And this example doesn’t even scratch the surface of what is possible creatively with ML. 

Creative Data Predictions Can Do More Things Like Reveal Potential Profit

Airbnb hosts are running a business. They need to know how to price their homes for incoming guests. Imagine you are brand new to hosting and have no idea what the value per night of your entire home in Manhattan is. If you price it too low, you could miss out on profit. If you price it too high, you miss out on business. You can take a quick look around Airbnb’s site to make an educated guess on where to set the price or you can use machine learning.

Image via


Using the data set, I predicted the price using attributing factors like neighborhood group and room type. Using the data, you can conclude the top driver by impact is the room type. 

The average predicted price for the entire home is $211.38/night. The price is the highest in the neighborhood group of Manhattan. 

This is a good starting point for a novice Airbnb host, but there are other factors at play here in determining your potential profit. 

Image via Obviously AI's platform.


Imagine you wanted to go deeper and ask your data more questions. 

Let’s take a look at average price vs. number of reviews:

Image via Obviously AI's platform. 

The price generally trends down if you have more reviews, indicating your Airbnb profit will decrease as time goes on. 

From the Kaggle data, you can also average out the minimum nights in the dataset. I already know the answer, so I’ll just tell you it’s 7.02 nights. Multiply average minimum nights by average price and you’ll make ~$1,483.88/week with the possibility of the profit decreasing as reviews start rolling in. Additionally, don’t forget to subtract Airbnb’s pay cut, which is typically under 13%.

As You May Have Guessed, There Are Many More Examples of Creative Data Predictions

If you want to see more examples on the possibilities of what you can achieve with data predictions in business. visit our case studies:


  • Calculate dynamic pricing
  • Reduce churn
  • Detect fraud
  • Predict mitigation risk
  • Find subrogation possibilities


  • Predict customer behavior
  • Optimize assortments
  • Improve operations and marketing
  • Plan labor


  • Predict customer lifetime value
  • Predict net promoter score
  • Model marketing funnel
  • Find the best ways to cross-sell and up-sell
  • Attribute multi-channel marketing


  • Predict default rate and financial crime
  • Manage cash
  • Predict loan demand


  • Predict gamer churn
  • Predict gamer purchases
  • Understand live stream stats
  • Optimize gameplay
  • Predict wins and losses


  • Predict KPIs
  • Predict in-app purchases
  • Build customer personas
  • Reduce affiliate/referral fraud


  • Reduce readmissions
  • Predict drug adherence
  • Predict no shows
  • Predict length of stay
  • Predict churn

Creativity Will Set You Apart in the Machine Learning Field.

The interesting thing about predictive analytics and modeling is you don’t need the same technical skills as you needed before. Think of technical skills as hardware and the basic concepts of machine learning as software. It’s much harder to change the hardware than the software. It’s time to start thinking of machine learning as a creative tool, rather than inaccessible technology reserved for techies.

Here's how to get the most out of predictions with Obviously AI:

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