Companies like Amazon, Walmart, and Target owe a lot of their success to cross-selling algorithms. In fact, McKinsey reports that 35 percent of purchases on Amazon come from product recommendations based on such algorithms.
Akkio's predictive algorithms can help your company identify the users who are most likely to be interested in a cross-sell, so you can target them with the right offering at the right time.
It's a win-win-win situation for all involved: The customer gets what they want, your company makes more money, and your sales team is freed up to focus on closing bigger deals.
If you sell just a few products, it's fairly easy to identify the customers who are interested in a cross-sell. But if you have many products or many customers, Akkio can help your company identify what would be a good cross-sell for any customer, and what the best time to pitch it would be.
Cross-sales can be an easy way to lift up your company's revenue without increasing marketing spend. After all, the best time to close a deal is when the customer is already engaged with your company. This process doesn't require any additional sales staff, or even technical talent, meaning that it's an extremely affordable way to boost your company's revenue.
To get started, sign up to Akkio for free. As with any machine learning task, the first step is getting historical data and picking a column to predict.
We’ll use a Kaggle dataset titled Health Insurance Cross Sell Prediction.
The dataset is from a health insurance provider that wants to know which customers would be interested in vehicle insurance. With a model to predict which customers are interested, they could then optimize their outreach strategy, and provide targeted offers that increase their revenue.
The dataset includes information about customer demographics (gender, age, and region code type), vehicles (vehicle age and damage), the policy (premium and sourcing channel), and more.
Each of these columns are potentially indicative of customer behavior, and each will be given a corresponding weight in the model. For example, perhaps a customer is more likely to purchase a policy if the vehicle they drive is an SUV. However, that may not be the case if they're a young male.
These sorts of relationships in the data are automatically discovered by the model. Crucially, the file includes our desired target variable, or whether the customer purchased vehicle insurance. We can use machine learning to predict cross-sale interest, and then prioritize efforts accordingly.
There are many factors that could affect interest in a cross-sell. With Akkio, you can build custom models to address the specific drivers of your business. And with our drag-and-drop interface, you can build any predictive model you want. First, we’ll just download the dataset as a CSV, and upload it to Akkio in a new model flow. You'll get a preview of the dataset, including modifiable data types, columns, and row count.
Now, we can click on the second step in the AI flow, which is “Predict.” Under “predict fields” you can select the column to predict, named “response.”
You can add or remove as many columns as you’d like, and we'll build a model based on the selected columns. Then just hit “Create Predictive Model,” and you’re done.
You can select a longer or shorter training time—ranging from 10 seconds to 5 minutes—for potentially more accurate models. Keep in mind that longer training times will not always necessarily perform better.
Also note that you don’t pay for model training time, unlike with many typical automated machine learning tools, so feel free to build as many models as you’d like.
After a model is created, you get a simple overview, which highlights the top fields, model prediction quality, sample predictions, data segments, and more.
For example, segment 1 highlights a customer group that's particularly interested in vehicle insurance. We can see that they’ve not been previously insured, and report having vehicle damage. This would be a good group of customers to target with a cross-sell, since they're likely to buy vehicle insurance.
In contrast, segment 3 highlights customers that are not interested in auto insurance at all. These customers have no vehicle damage, and have essentially brand new cars.
There’s also the option to “See Model Report,” which lets us easily collaborate with others and share these model details with anyone, even if they don’t have an Akkio account. In short, we’ve discovered that it’s very easy to build an accurate model to optimize inventory.
Now that we’ve built a model, it’s time to deploy it in the real world.
With Akkio, it becomes trivial to deploy complex machine learning models. Deployment via web is an easy way to instantly serve predictions. It’s also possible to deploy through Salesforce, Google Sheets, Snowflake, and the Akkio API, with many more methods coming soon.
Further, you could deploy the model in practically any setting with Zapier, a no-code automation tool that connects tools with “Zaps.” Zapier allows you to link up thousands of different tools, so no matter where your data is coming from, it’s easy to connect it to Akkio to get a prediction, even if you don’t have any technical expertise.
If you’re looking for more technical power, you can also use Akkio’s API, which is formatted as a Curl command, allowing you to send a GET request that contains your flow key, API key, and input data, and get a prediction back.
We’ve explored how cross-sales can be a powerful tool for increasing your revenue. To get started, sign up to Akkio for free.
We’ve built a platform that includes no-code AI predictions, a drag-and-drop visual interface, and a cloud-based data storage and processing system that allows you to create scalable machine learning models with no technical expertise.
Akkio powers many other machine learning use-cases, from fraud detection to churn prediction, and we’re working hard to add more in the future.