Overview
Last updated
Last updated
In today's competitive market, understanding and anticipating customer behavior is crucial for business success.
AI-driven predictive analytics offers a powerful tool to forecast key customer metrics, enabling businesses to make informed decisions and tailor strategies effectively. That’s why we’ve launched our own AI predictions, enabling you to enrich your customer data with churn likelihood, expected lifetime value and product recommendations.
We've developed custom machine learning models based on neural networks, tailored specifically for retailers. No technical skills required to use these models. You can generate predictive ML / AI models in just one click, trained on your own data, which understand and considers your customers' usual behaviours.
Before starting, ensure you have the following:
Access to a DinMo workspace, with “Predictions” module enabled
A users model containing all the consumers for whom you'd like to obtain predictive attributes
An events model linked to your user model, containing all your historical data.
(For product recommendation only) A product model linked to this specific events model
In the sidebar, click on "Predictions" and request access. Access will be granted based on your subscription conditions.
If you don't have access to predictions under your contract, but would like to try out our feature, please contact your CSM.
Once approved, you will be able to click on "Predict your first attribute" and follow the end-to-end instructions for computing each attribute:
How to set up and use LTV and churn attributes
How to set up and use Product recommendations
AI predictions can be leveraged to analyse, forecast, and engage customers based on their buying habits, or to reduce churn among customers.
Below are some examples of common use cases:
By analysing purchasing habits and behavioural patterns, AI models can forecast future spending, allowing businesses to:
Personalise marketing efforts: Target high-value customers with exclusive offers or loyalty programs to enhance retention.
Optimise resource allocation: Focus on segments with the highest predicted LTV for better ROI.
Identify up-sell/cross-sell opportunities: Identify potential customers likely to purchase soon and recommend similar or higher value products.
Churn prediction identifies customers at risk of discontinuing their relationship with your business. AI algorithms detect patterns indicating potential churn, enabling proactive measures to:
Implement retention strategies: Address issues before customers leave, such as offering personalised incentives.
Enhance customer satisfaction: Identify pain points and improve services to meet customer needs.
Reduce revenue loss: Maintain a stable customer base by minimising attrition.
AI-driven product recommendations analyse customer data to suggest items aligning with individual preferences and behaviours. Benefits include:
Increased sales: Personalised suggestions can lead to higher conversion rates.
Improved Customer Experience: Relevant recommendations enhance satisfaction and loyalty.
Transform up-sell/cross-sell opportunities: For each customer, recommend products that are either similar or of higher value, encouraging additional purchases.
Privilege Errors: Ensure that DinMo has the necessary privileges on the predictions schemas and tables in your data warehouse. You can find the relevant SQL scripts for each warehouse at the following links:
