Bank Segmentation for Marketing

Randy Johnson & Roland Shabani , Western Kentucky University BI 420 Data Mining

Question

• We’re looking to find customers’ usage patterns of banking methods over the course of a three month study period. Using 100,000 case examples. We’re wanting to find out which banking method is being used and which ones aren’t. Using that information we want to make the customers’ banking experience easier and more convenient.

Background

• 100,000 active customers were study.

• The dataset has six attributes. Which one, ID, being a special attribute used to identify the customers.

Attribute Name Model Role Measurement Level Description ExamplesID ID Nominal Customer ID

CNT_TBM Input Interval Traditional bank method transaction count

In-bank services; deposit and withdraws. Building a relationship

CNT_ATM Input Interval ATM transaction count

ATMs

CNT_POS Input Interval Point-of-sale transaction count

MoneyGram, Western Union

CNT_CSC Input Interval Customer service transaction count

Tellers, personal bankers, financial advisors

CNT_TOT Input Interval Total transaction count

Data Mining Approach

SAS Segmentation Approach• Using RapidMiner and SAS, we

ran different filters to attempt to find any patterns or commonalities within the dataset.

RapidMiner Segmentation

Model Results

• Cluster 0; traditional method the most similarities.• Clust 1-4 follower in order from

greatest to least.• Most banking methods used1. Traditional2. ATM3. Point-of-sale4. Customer service

Conclusions

Recommendations for Segments

• Segment 1: Old-Fashioned; these customers were more likely use traditional making methods. To offer more convenient banking, we propose telling them about online banking or offering them debit cards.

• Segment 2: Transitionals; these customers also used traditional banking more, but they were closer to the distribution centers on the other transaction methods. This seems to be the more balanced segment. We wouldn’t offer them as much alternative methods because they are on both side of the spectrum.

• Segment 3: ATMs; These were customers that used ATMs the most. We suggest informing them about more ATM locations in the area, so they’ll have more access to them at their convenience.

• Segment 4: Cashless; these group of customers did the least amount of traditional banking. We propose offering them one-on-one consulting section to get them into the bank and finding out what kind of investments they might want to making in the near future.

• Segment 5: Service; this group had higher than average rate of customer service contract and point-of-sale. We propose offering them the banks traditional methods they might have not heard of.

Dataset Recommendations

• We felt that the dataset was insufficient in some ways. It didn’t offer enough information about the customers.• All the dataset told us was the amount of the bank methods each

customer used. Which doesn’t really give us much insight in what they want and how the bank can better it. • May if we knew information like their age, bank transaction history. We

might be able to group customers more accurately and find more patterns within the dataset. • We suggest that the bank survey their customers; in the bank or even

online to get a clear depiction of how their customers feel about the bank and what they can improve on.

Sources

• Acknowledgements: Dr. Leyla Zhuhadar for finding the dataset and helping us analyze it thoroughly.• Data Source: Profile.csv • Author Contact Info: Randy Johnson, (270)421-3849, randy.johnson958@topper.wku.edu Roland Shabani, (270)320-1108, roland.shabani544@topper.wku.edu