What are cross-selling prediction models and how do they work?

Cross-selling prediction models are strategic frameworks that businesses use to increase customer value by encouraging existing customers to purchase related or complementary products or services. The goal is to make relevant offers to the right customer at the right time through the appropriate communication channel.

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These models typically encompass several key elements:

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- Identifying Potential Buyers: This involves making educated guesses about who is likely to make a purchase and when. This helps businesses plan their marketing efforts to get the best results without spending too much money.‍

- Optimizing Marketing Campaigns: This step involves figuring out the best mix of products, ways to reach out, and timing to get people to buy more, all while making sure customers don't feel overwhelmed by too many offers.‍

- Grouping Customers: This involves grouping similar customers together. That way, businesses can tailor their approach to each group instead of treating everyone the same or trying to reach each person individually.‍

- Calculating Customer Value: This involves determining how much a customer will likely spend over the entire time they do business with a company. By looking at how long they'll be a customer and how much money they'll bring in or cost, businesses can decide who to target with special offers.

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Overall, cross-selling models are analytical tools that enable businesses to optimize their marketing campaigns and increase their profits by selling more products or services to existing customers.

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Why is risk assessment important for cross-selling prediction models?

Risk assessing cross-selling models is crucial to ensure their effectiveness and to mitigate potential financial implications. In some cases, companies adopted a machine learning-based cross-selling model to suggest additional products to existing customers, aiming to boost revenues. However, this current model could be underperforming, leading to a lower positive response rate and inadequate return on investment (ROI).

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A cross-selling model can underperform or not work as well as it should for a few reasons:

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- Bad Data: The model learns from data we give it. If the data isn't good or doesn't represent the real world well, the model will make mistakes when it's used in real life. For example, if all the data we give it is about people who love cars, it might think everyone loves cars and try to sell car insurance to everyone, which isn't correct.‍

- Simple Model: If the model is too simple, it might not be able to learn complex patterns in the data. It's like trying to understand a movie by only watching the trailer. The model might miss important information.‍

- Missing or Unhelpful Information: The model learns from specific pieces of information we give it, like age or where people live. But if we miss out on important information or give it unhelpful information, it might make wrong predictions. Like, trying to predict someone's favorite food only based on their shoe size.‍

- Wrong Success Measure: If we're not measuring the model's success in the right way, it might think it's doing a good job when it's not. It's like rewarding a cat for catching a mouse, and then it starts catching birds too.‍

- Unfairness: The model might make decisions that are unfair to certain groups of people, which can lead to trouble. Like if it always recommends more expensive products to certain people based on where they live. This is unfair and can get the company in trouble.

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To avoid these problems, we need to regularly check how the model is doing and make sure it's learning the right things from the right data.

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Calvin Risk Assessment for Cross-Selling Models

Calvin Risk’s AI Risk Management platform was developed with the sole purpose of becoming a reliable partner for businesses in their journey to make their cross-selling predictive models more effective, trustworthy, and fair.

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So, how does Calvin work? In the initial stages, Calvin assesses the model in question, analyzing the type and the input features it uses, such as customers’ details and product information. Next, it evaluates how well the model has been performing based on its training metrics and how fair it is in its predictions, especially concerning potentially affected protected groups.

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After this, Calvin analyzes the model's robustness, which is its ability to handle changes or errors, and its explainability, that is, how easily we can understand its predictions. Calvin then reviews documentation, development processes, stakeholders, and compliance with legislative requirements to paint a full picture of the model's development journey.

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With this wealth of information, Calvin then calculates risk scores for various risk dimensions of the model and identifies high scores, which are hinting at a higher chance to unwanted model behavior.

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And it's not all doom and gloom; Calvin also suggests ways to improve the model, like tweaking the number of neurons used in the model or refining the training data to better mimic real-world conditions. In essence, Calvin is like a watchdog and a guide rolled into one, keeping an eye on risk while also showing the path to improvement.

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Intrigued? We invite you to see for yourself just how Calvin Risk can transform your business. See firsthand how this amazing platform can keep your models compliant, fair, and working at their best. Don't let uncertainties about your models hold back your business potential. Book a demo with us today and start your journey towards predictive models that are not just profitable, but ethical, trustworthy, and dependable. Discover Calvin Risk - your partner in building a better, fairer, and more successful business.