8. Predicting Cross-Selling Opportunities with No Code Machine Learning

Today, we’re gonna build a model to predict cross-selling opportunities with existing customers.

We’ll start by:

  1. Reviewing the dataset
  2. We’ll then build a Machine Learning model for predicting cross sell opportunities.
  3. And instantly see how we can make predictions and share them with others on the team.

So let’s get started.

Number 1: Reviewing the dataset

In this dataset, every single row is an individual customer. And every column tells us something about that customer. Like Gender, vehicle age, vehicle insurance, etc. At the end, we have a column here called Response. This tells us if a customer in that row responded positively to a cross selling opportunity. 

This is a historical dataset. And we now wanna use it to predict whether a new customer is likely to be interested in a cross selling opportunity.

Number 2: Building the machine learning model

To build a model, we start by uploading this CSV.  We can upload spreadsheets, Connect to apps & services like Salesforce and Dropbox and even databases like MySQL and BigQuery.

Once uploaded, all you have to do is select your prediction column.  In this case, it’s the Response column.

From here, Obviously AI will automatically build a machine learning model to make predictions, in less than 1 minute.

Number 3: Make predictions and share with your team

Once your model is built, it’s ready to be used right away. No deploying, no maintenance. You can see it’s accuracy and performance details here. 

Seems like we’re good to go! We start by heading to Personas to make a first prediction.

Let’s say we have a new customer. They’re 34 years old, had recent vehicle damage and pay about $20,000 dollars in annual premium.

As I change these details, I can see the prediction for this customer come up right here. Their likelihood to be interested in a cross sell opportunity is 65%.

Now, let’s head to export predictions. We can upload a list of new customers and get a prediction on each one. An API can also be used to automate these predictions and embed them in your own app or website. You will also have the option to get a shareable link to send to anyone on your team. It will enable them to use the model you built to start making their own predictions.

So, what would you like to predict today?

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