Predicting Portuguese Bank Term Deposit Subscriptions
Identifying Likely Customers for Conversion Optimization
By Huggingface Hub [source]
About this dataset
This innovative dataset is gathered from a Portuguese banking institution's direct marketing campaigns to identify customers who are likely to subscribe to a term deposit, with the ultimate goal of maximizing their conversion rates. With the utilization of telephonic marketing campaigns, this bank has sought out information on individual selection characteristics such as age, job type, marital status, educational level, default history and banking balances that could potentially bring insight into what renders somebody more or less likely to subscribe. The dataset produced provides detailed data on customer contact day and duration in order to answer questions surrounding customer inclination towards the term deposit offers made in these telemarketing campaigns. Furthermore it also considers previous outcomes from similar calls with the same customer as part of its featureset. With all this knowledge at hand we are thus presented with an opportunity to drastically augment conversion success rate through learning which factors yield positive results when attempting to attract new customers for this particular product offering
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How to use the dataset
This dataset can be used to gain insights into the direct marketing campaigns of a Portuguese banking institution, and to better understand customer subscription behaviors to their term deposits. The objective is to use this data to predict which customers are most likely to subscribe a term deposit, as well as maximize conversion through this information.
In order to take advantage of the dataset provided, we suggest using tools such as classification and predictive modeling methods. These methods will enable you to perform an assessment of the likelihood of customer subscription, given certain parameters in regards to their self-reported information and interactions with the bank itself. For example, you may run regression models in order determine which charactersitics are significantly correlated with subscription rates so that you can appropriately targeted your effective marketing strategies more accurately. Furthermore, various machine learning techniques could also be employed for building predictive models that scale over time so that customer trends can be identified on a continual basis.
Overall, this dataset is useful for understanding customers’ engagement and how they interact with banking services- it provides valuable insights for predicting whom among potential customers have a tendency towards subscribing terms deposits at banks institutions- For example from exploring duration or day parameters about phone calls or from balance , age or job parameter combo. by doing categorizing potential customers under respective segments according them regarding education level or marital status; This provides ample opportunity con develop custom tailored solutions based on real world data gathering methods and application scenarios like feature scaling etc.. Moreover it is equally applicable large scale cost optimization measures such search engine campaigns – because different categories population demographics exhibit certain trend patterns related offers pertaining interest areas based on past analytics enables instant decision making process at disposal marketers what segment needs more promotion etc.. Welcome Every one And Explore Dataset further !! maximize your profit strategy tailor made real word scenarios…
Research Ideas
- Developing customer segments with similar profiles, and developing targeted campaigns to reach them. This could include gathering customer behaviors, preferences, and other demographic information into categories to help identify the most specific target markets for successful conversion.
- Utilizing machine learning algorithms such as Random Forest or Decision Tree models to create a predictive model that can forecast which customers are likely to subscribe. This could be utilized as a tool when evaluating potential prospects and only targeting those with the highest probability of conversion rate optimization.
- Creating a goal-based system by setting up milestones in an effort to increase customer retention rate or offer existing subscribers further incentives might prove beneficial for better converting campaigns in lower cost marketing efforts over time
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: train.csv
| Column name |
Description |
| age |
Age of the customer. (Integer) |
| job |
Type of job the customer has. (String) |
| marital |
Marital status of the customer. (String) |
| education |
Education level of the customer. (String) |
| default |
Whether or not the customer has a default on their credit. (Boolean) |
| balance |
Balance of the customer's bank account. (Integer) |
| housing |
Whether or not the customer has a housing loan. (Boolean) |
| contact |
Type of contact used to reach the customer. (String) |
| day |
Day of the week the customer was contacted. (Integer) |
| month |
Month of the year the customer was contacted. (String) |
| duration |
Length of the call with the customer. (Integer) |
| campaign |
Number of contacts performed during this campaign and previous ones. (Integer) |
| pdays |
Number of days that passed by after the customer was last contacted from a previous campaign. (Integer) |
| poutcome |
Outcome of the previous marketing campaign. (String) |
File: test.csv
| Column name |
Description |
| age |
Age of the customer. (Integer) |
| job |
Type of job the customer has. (String) |
| marital |
Marital status of the customer. (String) |
| education |
Education level of the customer. (String) |
| default |
Whether or not the customer has a default on their credit. (Boolean) |
| balance |
Balance of the customer's bank account. (Integer) |
| housing |
Whether or not the customer has a housing loan. (Boolean) |
| contact |
Type of contact used to reach the customer. (String) |
| day |
Day of the week the customer was contacted. (Integer) |
| month |
Month of the year the customer was contacted. (String) |
| duration |
Length of the call with the customer. (Integer) |
| campaign |
Number of contacts performed during this campaign and previous ones. (Integer) |
| pdays |
Number of days that passed by after the customer was last contacted from a previous campaign. (Integer) |
| poutcome |
Outcome of the previous marketing campaign. (String) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.