Predictive analytics enable:
Assess customer CLV looking at their behaviour and going beyond socio-demographic classifications.
Predict customer churn in order to activate a protocol to retain them.
Build customer profiles on their estimated willingness to buy to plan targeted sales.
create a strategic database using combined data from different channels.
Increase approval rating and satisfaction index of the customer base.
Pythía – our platform
Through Pythía it is possible to manage:
- The file transfer process.
- Data preparation for the AI engine.
- Model training and validation.
- Delivery of output to the dashboard.
This dashboard allows users to visualise data extracted by the AI engine together with more standard statistical data.
Customer: the user can visualise all the statistics related to single customers: CLV, churn rate and probability of purchase on the basket of selected products. This is in addition to other information that might be available regarding asset liabilities, credit card usage and online activity.
Products: by selecting a product the user can visualise the segmentation of the client base.
Data sources: this feature allows you to configure the path where files are uploaded and contains coding and decoding hash/obfuscation keys.
Artificial intelligence in predictive analytics
AI is the most effective way to obtain accurate, automated predictions on a large volume of data that cannot be obtained using traditional methods.
Deep Neural Embedding
Customer profiling happens inside a neural network that renders it specific for the target of the network itself.
better prospective profiling
RNN - Reti Neurali Ricorrenti
A model of a temporal dynamic behaviour of customers with automatic detection of relevant variables.
better prediction accuracy
Inverse Reinforcement Learning
Extraction of the maximum amount of information from the interaction with the customer through learning processes similar to those of humans.
better marketing campaign effectiveness
Predictive analytics for client profiling
CUSTOMER LIFETIME VALUE
Assessment of expected profitability.
Assessment of winning product/customer combinations from a profitability standpoint.
1. Customer segments that are probably interested in purchasing a given product.
2. Customer segment that could leave for a competitor.
An example of digital strategies for retention/upselling
Applied to customer segments processed using AI.
Available in the database
- Mobile phone.
- Customer profile on the home banking platform.
When the user that is part of a given segment browses in the home banking platform while being logged in, it is possible to install two cookies on its browser. Those cookies are provided by Facebook and Google and identify them as being part of that specific segment when they will browse on those platforms in the future (see following slide).