Project
AI4sales&mktg
What is AI4Sales&Mktg
AI4Sales & Mktg is a solution, based on Artificial Intelligence techniques, designed to determine customer analysis in both the B2B and B2C fields.
Who is it for
Our solution has an impact on the Sales and Marketing divisions of companies and is valid for both traditional physical channels and digital E-commerce channels.
AI4Sales & Mktg is aimed at companies belonging to the following sectors:
- Retail
- GDO
- Banking / Assurance
- Distribution
Clusterization
Clients
AI4Sales&Mktg consente AI4Sales & Mktg allows you to perform a customer analysis:effettuare un’analisi dei clienti:
CHURN PREDICTION
Which customers are prone to abandonment and what are the main parameters related to abandon
OTHER ANALYSIS TIPOLOGIES
Identify clusters of customers who, for example, may be more inclined to certain types of purchases (upselling and cross selling), could be directed to certain sales channels (digital, traditional).
Churn Prediction
This feature has as its focus the identification of the probability of abandon of a customer through Artificial Intelligence techniques.
The main goal is to provide a tool that allows you to minimize the churn rate:
- Define the probability of abandon on different clients’ clusters
- Visualize abandon’s trends
- Determine wich parameters contribute most to the abandon (price? customer service? rivalry? …)
In this way the company has objective data to apply necessary interventions to avoid the abandon.
Churn Prediction Methodology
Examples of data to define the customer as accurately as possible:
- Customer demographics
- Products/Services aquired
- Transaction
- Buying habits
- Customer Service
- Rivalry data
Cleaning of received data and memorizing of a support database
Application of the Features Tools in order to define which features to apply for the identification of the model.
Refinement of the procedure in order to identify the final model that best represents the data.
Churn Prediction
Machine Learning
The Model is trained to recognize an abandoned customer compared to a loyal customer.
We obtain:
The classification of a customer, that is, whether he is a candidate to leave or not
The probability of belonging to the assigned class
The confidence interval with which this probability is assigned
Churn Prediction
Machine Learning
In summary, it is possible to predict whether a customer will abandon or not by building a descriptive dataset of the customer’s behavior starting from the sources present in the company.
It is also possible to give an estimate of which abandonment parameters have the greatest weight, obtaining information on where it is most advantageous to act to prevent defection.
Churn Prediction
Dashboard
Detailed analytics dashboard: who are they and how much do customers churn?
Request an online demo
One of our consultants will assist you with the explanation.