How to outperform your competitors by optimising your customers’ lifecycle
By analyzing your customer purchase history, KNIME can easily spot the stage of each customer into the lifecycle and run the proper prediction model depending on the customer stage. This enables you to take appropriate actions afterwards.
How? A KNIME workflow classifies each customer into its lifecycle stage (new customer, increasing customer, declining customer, deactivated one,…).
Next, some other KNIME workflows containing products recommendations models are triggered afterwards (one by customer stage with all customers of the stage included).
With new customers, as the amount of data available is still limited, a simple model pushing products usually bought by the other customers having the same profile is triggered.
Whereas for customers with a high churn risk, a more advanced re-order reminder model is triggered. Thus, spotting the products that each customer would buy given his order history, but is no longer buying.
This approach requires customer data management and customer data analytics implemented in transparent workflows.
Cover all options in a fast and flexible way
Every customer-stage group obtains a specific products recommendations model and all those models are triggered and running in parallel in KNIME.
At the end, every customer has a very specific and customized list of recommended products depending on his purchasing history and profile. This maximizes the chance of every customer to move up (and not down) along the lifecycle.
KNIME Analytics Platform integrates seamlessly in your existing IT architecture.
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