Telemarketing is an interactive direct marketing technique conducted over the phone, widely used by banks to sell their products or services. As with every direct marketing campaign, it can be a very effective way to generate sales and customers. However, the vast number of telemarketing campaigns has reduced their effectiveness on the general public, leading to increased costs and more rejections. Thanks to artificial intelligence techniques, there are ways to increase the effectiveness of telemarketing campaigns in the banking sector. One of the best ways to increase its effectiveness is customer segmentation.Customer segmentation is the process of analyzing customer features (such as age, education, interests, and spending habits) and creating groups (called segments) that share interests in specific products or services.

In this way, customer segmentation enables companies to identify the most suitable customers for their purposes and invest their resources more effectively. Moreover, it is an economical and fast way to increase customers and effectively manage existing ones.

It has been proven that artificial intelligence can select potential clients much better than traditional methods. These operations are very useful in the marketing ambit, allowing them to determine with considerable accuracy which customers are promising candidates for a specific marketing campaign.

Data set

In this real case customer´s case the data set contained 1,000,000 clients with 500 features, including:

  • Personal Variables: Sex, age, nationality…
  • Account Variables: Account balance, credit card debt…
  • Product Variables: Home mortgage, car leasing…
  • Insurance Variables: Life insurance, Home insurance…
  • Transaction variables: Cash point usage, Website usage…
  • Success: 1 if the purchase was made in the previous campaign made by the bank, 0 otherwise

Our target variable was a success because it aligns with what our customers wanted to improve. Before contacting us, they had a success rate of 1%.

Using artificial intelligence techniques like artificial neural networks, our team studied the data to determine how the success rate could increase.

Purchasing factors

After studying the correlations between the variables, we realized that all the variables had a very low correlation (under 10%). This indicates that no single individual significantly influences product purchases, so we can say that the problem is complex, with various factors influencing the purchasing process.

Logistic correlations between the input variables and the output variable

The variables with the highest correlation are the number of services with the entity, the number of cash point operations during the last month, and whether the customer had life insurance.

The factors that most influence the purchase process are those related to engagement.

Neural Network

After studying all these factors, we used artificial neural networks to build a predictive model that analyzes every variable and selects the best target for the telemarketing campaign.

Customer targeting neural network

That study concluded that the conversion rates could be increased from 1% to 2.5% by selecting the proper target. This is a very significant increase since, without using the model, we got one customer out of every 100 contacts, whereas now we have one conversion for every 40 contacts.

Conclusions

By analyzing the cumulative gain obtained with the predictive model, we can see that by contacting 30% of the most likely customers, the bank would achieve 80% of the total sales.

Cummulative gain chart

Finally, after knowing the precision with which we identify customers, it is essential to understand how this would affect our benefits.

We will assume that the unit cost per contact with each potential customer is 5€ and that the unit benefits if they buy the product are 1,000 €. The following graph shows how the benefit would behave without the predictive model (grey line) and with it (purple line).

Profit chart

As we can see, the maximum benefit is produced by contacting the 35% of customers with the highest purchase probability. This benefit would be €750,000, whereas without using the predictive model, it would be €125,000.

The use of new methods and tools for advanced analytics provides a significant advantage to Business Intelligence Departments, ultimately benefiting the bank.