Increase Revenue With AI-Powered Cross-Selling

Use Cases

Learn how to use AI to automate mundane tasks and become a cross-selling expert.

If you’ve ever added a recommended item to your existing cart, whether on Amazon.com or in a grocery store, you’ve experienced a “cross-sale.” Cross-selling means selling one item with another that a customer already planned on buying.

For example, if you’re buying coffee beans on Amazon, you might get a recommendation for a coffee grinder. A store clerk might take advantage of the same cross-selling opportunity.

This might be easy enough to do manually if you only have a few products, but if you want to accurately recommend the right products to the right people, it becomes impossible to do manually, at scale.

No-Code AI For Cross-Selling

We’ll use Obviously.AI to predict cross-sell opportunities on insurance company data, update a CRM with those recommendations, and automatically email sales offers to customers who are likely to convert.

By predicting which customers are interested in a cross-sale, we can increase revenue, and also ensure that we don’t annoy customers who aren’t interested in additional products.

However, truly optimizing KPIs is about more than just building AI models. You need to implement those models and predictions in the real-world. What good is a prediction if it’s not acted upon? In this guide, we’ll explore how to deploy our model to predict whether new customers would be interested in cross-sales via Zapier, a no-code automation tool.

1. Building A Cross-Sell 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 the insurance company cross-sale data from Kaggle, which features around 381,000 health insurance customers and their responses to a cross-sell offer for vehicle insurance.

Column attributes include demographics like the customer’s gender, age, and region, as well as vehicle data like its age and damage, and finally policy data like the customer premium, sourcing channel, and tenure.

Our goal is to predict a KPI called “Response,” which is the value 1 if a customer was interested in vehicle insurance, and the value 0 if a customer was not interested in vehicle insurance.

After clicking to upload a CSV file, you’ll need to verify the dataset, and simply 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 “Response.” In the background, a series of machine learning models compete to create the most accurate predictions for whether a customer would be interested in vehicle insurance or not.

And we’re done! Now, we can also see how various attributes impact the Response KPI. Whether a customer was previously insured is the main driver, followed by vehicle damage, the car’s age, the sales channel, and the rest.

Let’s move on to building a Zapier sequence, so you can deploy predictions in the real world, and predict whether a new customer would be interested in vehicle insurance (or any cross-sell offer).

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 of new customers. For this demo, our dataset will be a simple Google Sheets, so 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 customer 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 up-sell “response”).

For instance, since my training data had columns like Vehicle_Age, Vehicle_Damage, 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 how you pull information from Google Sheets into Zapier, like so:

Simply match up the column names in your data with the column names used in your model.

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 “cff97990–7385–11eb-9a76-af3704ed1fd7.” 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 customer sheet to our up-sell prediction model, and we’ll get the response 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 probability of a positive or negative response. This can be done with the action titled “Update Spreadsheet Row in Google Sheets.”

Then, all the way at the bottom, add the aforementioned probability of response to the last column.

Now, let’s take it a step further. We can create a “filter” in Zapier to send a sales offer to a customer that’s has a particularly high chance of being receptive to it. Below, I set up a filter that gets activated when the probability of a positive response is higher than 70%.

If the probability of a given user being receptive to a cross-sell is higher than 70%, we can automatically send them an offer email (as long as there’s a column in your CRM with their email).

Summary

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 increase revenue by taking advantage of cross-sell opportunities.

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