Solution | Next Best Offer for Banking

Overview

Business case

Marketing campaigns are most impactful when intelligently targeted. Sending the same material to the whole customer base isn’t only costly, but also ineffective. Knowing how likely a group of customers is to subscribe to a product is essential to make the right decision.

This solution completes the customer segmentation solution within the marketing suite for banking. The user can plug the same data as in the former solution and build an initial model. The Project Setup enables users to control how to define the target customers for the marketing campaign.

Installation

The process to install this Solution differs depending on whether you are using Dataiku Cloud or a self-managed instance.

Dataiku CloudSelf-managed

Dataiku Cloud users should follow the instructions for installing solutions on cloud.

1. The Cloud Launchpad will automatically meet the technical requirements listed below, and add the Solution to your Dataiku instance.

2. Once the Solution has been added to your space, move ahead to Data requirements.

Technical requirements

To leverage this Solution, you must meet the following requirement:

• Have access to a Dataiku 13.2+* instance.

Data requirements

The project is initially shipped with all datasets using the filesystem connection.

The input data should be separated into five different datasets with the same time frequency:

Dataset	Description
revenues	Includes the revenues generated by each product per customer over time.
product_holdings	Includes product information and duration period of each product held by customers.
customers	Includes customers’ static information. You can add optional columns to this dataset.
balances	Includes balance amounts of each product per customer over time.
additional_information	Includes optional additional columns.

Workflow overview

You can follow along with the Solution in the Dataiku gallery.

The project has the following high-level steps:

1. Connect your data as input, and select your analysis parameters via the Project Setup.

2. Explore the data and understand the model through the Analytics and ML Model dashboard.

3. Analyze and compare your top customers and top campaigns for cross-sell in the Marketing Campaign dashboard.

4. Visualize specific information about a chosen customer in the Customer Focus dashboard.

Walkthrough

Note

In addition to reading this document, it’s recommended to read the wiki of the project before beginning to get a deeper technical understanding of how this Solution was created and more detailed explanations of Solution-specific vocabulary.

Plug and play with your own data and parameter choices

To begin, you will need to use the Project Setup. The project comes with sample data. You can replace it with your data, assuming that it adopts the data model described above.

You can do this in three ways:

1. Upload data directly from the filesystem in the first section.

2. Connect to your database of choice by selecting an existing connection.

3. Copy connection settings and data from a Next Best Offer for Banking project already built.

In options 1 and 2, users must click the Check button, which will load the data and verify the schema.

Tip

Be sure to refresh the page so that the interface can dynamically take your data into account.

With data selected and loaded into the Flow, you can move to the following sections:

Section	Description
Subscription Probability Prediction	Allows you to set the horizon on which you want the probability of subscription to be computed and select which products, if any, should be excluded from the prediction.
Top Prospects for Cross-Sell	Allows you to select some parameters about top prospects for cross-selling a selected product. It requires you to specify the product to be analyzed and choose between two methods to parameterize the output.
Top Campaigns for Cross-Sell	Outputs top prospects for cross-selling each product present in the data. It requires you to specify the number of top customers (ex: 100 customers) to see as output for each marketing campaign.

Cleaning and preparing historical data

In total, seven Flow zones prepare and clean data for this Solution. We won’t go into heavy detail about each Flow zone. See the project wiki for these details.

These Flow zones help construct the consolidated input dataset. The Solution generates a unique row for each client, date, and product combination, enabling it to determine whether the client held the product at a given point in time.

After training, testing, and scoring a classification model, two final Data Prep Flow zones compute the expected gain and impact values. These Flow zones are important to get the data into a format that can generate visualizations and metrics for the dashboard.

Exploring input data

To better understand the input data and verify that it’s coherent, it’s important first to explore the historical datasets. Doing so allows for identifying the population distributions and trends of the customer base and product holdings.

The Customer analysis and Today’s holdings analysis Flow zones compute all metrics and values needed to generate charts for the two first pages of the Analytics and ML Model Dashboard.

Predicting probability of subscription

The Subscription Prediction zone creates the prediction of the subscription probability.

The classification model first orders the training dataset by the observation date column. The model is then trained on the first 80% of the data points in the train dataset and tested on the remaining 20%.

The Score recipe applies the regression model to the dataset containing the values to predict (to_predict_data) and generate the predicted values.

Note

To address potentially imbalanced values in the subscription variable, the model performs class rebalancing. This involves randomly selecting approximately 100k rows to rebalance all modalities of a column equally. Users can customize the parameters for this sampling method in the Flow.

Visualize model performance

In the same dashboard as the previously mentioned Exploring Input Data section, you can find a third page, named Machine learning model analysis, to visualize the explainability and performance of the classification model built to compute the subscription probability prediction.

Exploring cross-sell impact

The last page of the Analytics and ML Model Dashboard allows for understanding the effect of offering additional products to customers on the sales of existing products.

Marketing campaign dashboard

The Marketing Campaign Dashboard covers the following topics across two pages.

Page	Description
Top Prospects for Cross-Sell	Displays information regarding the list of top customers, which were selected based on the parameters configured within the Project Setup.
Top Campaigns for Cross-Sell	Presents a comparison of the gain and impact associated with each product analyzed based on the parameters configured within the Project Setup.

Customer focus dashboard

The Customer Focus Dashboard offers specific information about a chosen customer. To find a particular customer and adjust the analysis, users can use the search engine at the top of the page to select a customer ID and update the visuals.

Responsible AI considerations

The Next Best Offer solution is designed to serve as a powerful marketing campaign targeting tool. However, you shouldn’t use it for the distribution of offers or promotions to a select group of customers. Misusing this Solution in such a manner may lead to unintended consequences, such as the unfair treatment of certain customers.

Reproducing these processes with minimal effort for your data

This documentation has reviewed provided several suggestions on how to derive value from this Solution. Ultimately however, the “best” approach will depend on your specific needs and data. If you’re interested in adapting this project to the specific goals and needs of your organization, Dataiku offers roll-out and customization services on demand.