Industry
BFSI
The solution framework led to the selection of almost 405,000 customers as prospects for credit card acquisition with an accuracy of about 80%
Aided the client to design an informed campaign that befitted the credit card desirability index of customers
Collaborated with the marketing and digital team to support the deployment of campaigns for acquisition
The parameters generated from the analysis are reusable and can easily be scaled across other brands.
The procurement teams are now able to plan their budgets more accurately and negotiate better prices with vendors, leading to significant cost savings.
A retail and entertainment conglomerate in the MEA region wanted to cross-sell credit cards to customers. This required uncovering insights on target groups who were profitable and exhibited the highest propensity to buy the client’s credit cards.
TheMathCompany helped the client to identify the most profitable customer segment in their retail and entertainment user base by analyzing customer behavioral patterns. These patterns were used as metrics to identify customers who were not only profitable but also exhibited the maximum propensity to buy the client’s credit card.
A three-phase solution framework was built to evaluate and rank customers across retail and entertainment businesses. This step-by-step evaluation was done to gauge the maximum profitability and then the propensity of individual customers to subscribe to credit card offers.
Our team classified customers into two groups to derive the requisite data. The first group comprised of customers who were already subscribed to our client’s credit card and used it to make transactions at our client-affiliated retail and entertainment outlets. The second group comprised of customers who had not subscribed to our client’s credit card but only accounted for transactions in our client-affiliated outlets.
The information from the first group, such as monthly expenditure in the hypermarket, types, and quantity of items purchased, was used as training data to build a segmentation model to rank the profitability of customers in the second group. Findings from this analysis led to the identification of close to 450,000 customers. This identification was in line with the metrics that were driving the idea of the profitability in the data model.
The information derived from the first phase of the solution framework revealed only customers who did not yet possess credit cards under the client’s banner. It was not enough to guarantee subscription and subsequent profitable utilization of the credit cards. To be more specific, this hypothesis was not enough to help the client to count on the degree of the propensity of customers to buy the credit card.
To further segregate the database, our team imported data from a campaign that was previously launched by our client to promote credit cards. The information of customers who displayed engagement in this campaign was tallied with the profiles that were filtered from the second group with the help of the customer profitability model analysis results.
The engagement quality varied during the course of the credit card campaign. For instance, from a digital marketing perspective, the initial analysis of the click-through rate of this campaign issued an expected 70% customer conversion rate. However, a high degree of bounce rate of viewers (who could be potential customers) from the product landing page or while filling the subscription form, contradicted the initial expectation of conversion. This hinted at a couple of possibilities - customers either exited midway because they were not satisfied with the offer or they faced a forced exit because of technical issues. The varying nature of engagement brought heterogeneity in the datasets. This necessitated the utilization of the decision tree model to help to segregate customers based on their engagement levels.
Customer segregations conducted at different levels in the previously launched campaign, helped to identify the specific set of customers who displayed the maximum propensity to purchase the client’s credit card. However, the measurement KPIs which determined the profitability and propensity of customers in the retail business could not be applied to the customer base in the entertainment business. The frequency and time of transactions made by people tend to vary across these businesses. This necessitated the undertaking of two separate data projects across these sectors to identify customers. Streamlining the project course, which included manipulation and modeling of data for the retail sector, helped our team to automate most processes while conducting the second project in the entertainment business. This allowed to minimize the project delivery timeline by almost half.
