Predicting and Improving e-Commerce Sales With AI

Use Cases

This article will explain how to build a predictive model using AI to increase sales and ROI of e-Commerce sites.

Predicting e-commerce sales can help entrepreneurs improve product listings by tweaking the variables that matter.

However, for e-commerce stores to realize these benefits, they need to be able to easily build and deploy AI models, which can't be done with traditional, code-based solutions.

Below, we’ll explore how to build a model in Obviously AI and deploy it via Zapier, a no-code automation tool.

1. Building a e-commerce sales prediction model

To start, simply make a Pro account on Obviously.AI — you get a 14-day free trial, with no credit card needed to sign up.

Once you log-in, you’ll see a dashboard with an “Add Dataset” icon on the left. For this example, upload a simple tabular dataset from Kaggle, which features 43 attributes across 1,341 product listings.

Our goal is to predict a KPI called units_sold, which is the number of units sold.

After clicking to upload a CSV file, you’ll need to verify the dataset, and check off that the first row is the column names and that all requirements are met.

Now, we select the column we want to predict, which is called “units_sold.” In the background, a series of machine learning models compete to create the most accurate predictions for units_sold.

And we’re done! Now, we can also see how various attributes impact the e-commerce sales KPI. The main drivers discovered by the automated machine learning models were rating_count and rating_five_count.

Let’s move on to building a Zapier sequence, so you can deploy predictions in the real world, and predict e-commerce sales on new data.

2. Building a Zapier Flow

Now, make a free Zapier account, if you haven’t already. We’ll start by connecting Zapier to a dataset in Google Sheets. We’ll select “New Spreadsheet Row in Google Sheets” as the trigger to activate a Zapier sequence.

After selecting that trigger, I simply located my demo Google Sheet, which I titled “New Data.”

This sheet should have the exact same column names and data types as the file I used to make the predictive model (minus the actual target column of “units_sold”).

For instance, since my training data had columns like rating and uses_ad_boosts, and so on, I use those exact columns in this Google Sheet.

Next, we’ll select “Webhook,” and then “Custom Request.” This is how we’ll integrate with Obviously AI.

To set up the Custom Request, add in these details:

The data field is the most important, which requires matching up the column names in your data with the column names used in your model. Check the documentation if you’re unsure!

To set it all up, you’ll also need your Report ID and API key. Your API key can be found in your account, which gets added as a “Header” in Zapier. To get the Report ID, head to the “Export Predictions” tab in Obviously AI, and click on the shareable report link.

The Report ID is the ID you see in that URL. In the example above, it’s the string “cff9...1fd7.” As you can see in the documentation, this gets added in the bottom of the “Data” field in Zapier.

Now, we can test our Zap, which sends data from our new data sheet to our e-commerce sales prediction model, and we’ll get the e-commerce sales probability as an output. With these predictions in Zapier, we can now do anything we want with them: Send them to a Slack channel, send an email to our team, add the prediction to our sheet, or anything else.

For now, let’s just update our sheet with the estimated e-commerce sales. This can be done with the action titled “Update Spreadsheet Row in Google Sheets.”

Then, simply add the probability created in the aforementioned webhook.

Now, let’s take it a step further. We can create a “filter” in Zapier to send an email notification when the predicted e-commerce sales is lower than a threshold that we set. I set up a filter that gets activated when the e-commerce sales probability is less than 100 units sold. You can set this to any filter that makes sense for your data and your business.

If the e-commerce units sold probability is less than 100, we can automatically send a notification email so that we’re aware of the situation.


Zapier is a powerful way to automate practically anything in your workflow. With Obviously AI, you can integrate the power of machine learning to create AI-driven workflows, including to predict e-commerce sales and help entrepreneurs create more accurate budgets and business plans, besides improving product listings by tweaking the variables that matter.

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