More power to HSBC’s Power Vantage Account!

hsbc_pva_cross-sell-case-study_formatted2

Overview

HSBC is a global banking and financial services organization with 7,500 offices in over 80 countries. The bank’s businesses range from personal and private banking to corporate and investment banking.

Challenge

HSBC wanted to develop a Cross Sell strategy to sell its liability products to an asset customer base. More specifically HSBC was seeking to cross sell its Power Vantage Account (PVA), a Savings type account, to its Credit Card base. PVA is a premium product that offers many advantages aimed at the more affluent customer.

Solution

Xerago was brought on board to undertake a detailed study of the challenge. Xerago proposed building a response model across the Credit Card customer base. The aim of this model was to identify Credit Card customers who were most likely to respond to a PVA campaign.

Data Study

The base customer data (all credit card accounts with 6 month vintage) was identified as 1.177 million. Xerago eliminated all the duplicate accounts and identified the absolute number of credit card accounts (with 6 month vintage) dating back to two years. A month on month distribution of responders and non-responders were also identified. Finally the responders (those who had accepted the PVA offer) accounts were observed over a 6 month window.
With the above analysis Xerago identified that the response rate for the entire customer base was just 0.19 %. Thus Xerago decided to further segment and spruce up the data to derive more meaningful insights.

Segmentation and Clustering

Xerago undertook a clustering approach to ensure the statistical representativeness of the population. However due to large amount of dormant customer and minimum balance owners, the cluster data was found to be skewed. The PVA offering being a premium product was deemed non compatible with this portion of the population and hence were dropped from the overall base. Xerago strove to ensure a healthy population base and not lose too many responders. The final base numbers were
Customer base: 654010
Responders: 1848
Non-responders: 643162
The pruned base was further subjected to a 30% sampling rate for final segmentation.
Factor Analysis and Final Clustering:
Xerago derived a basic set of 9 variables for use in the factor analysis and in further clustering. After working with the bank Xerago was able to drop out 3 of the variables based on the internal criteria without compromising on the cluster robustness. Xerago was able to derive good differentiating segments which were subject to further sampling.
From each segment, 30% of the non-responders were sampled. To boost response rates, the number of responders was kept unchanged.

Response Model

The response model building was an iterative process. Different sets of input variables were used to derive combinations of new variables before arriving at a final set of input variables. The identified variables were chosen with the objective of cross selling across the card base. Hence most of the variables chosen pointed to active and moneyed customers, who were the original target group. The input variables were also subjected to basic Multi-Collinearity tests to ensure the variables’ invulnerability.
This resulted in creating the final model which was then tested across the rest of the base that was not part of the modeling base. Once the robustness and accuracy of the model was tested, it was then used to score the new base.
Thus, Xerago delivered a model that was the basis for the liability cross sell strategy.

Results

The bank was able to use Xerago’s model to undertake an analytical and streamlined approach to targeted cross selling and power cross sell campaigns.

Get in Touch

For queries regarding our Customer Value Maximization platform or our Solutions

* Mandatory

captchaLoading Page - Xerago

dear

Name

Thank You

for reaching out to us. One of our representatives will get in touch with you shortly.

What's your score?

Know what? You are not alone – less than 0.2% of the world’s financial services brands can stake the claim that they can do all this.

See how our Customer Value Maximization Platform can help you maximize value in your engagement with your customers!

Take me to the Platform

congrats-icon

Congratulations! You are probably in the top 0.2% of the world’s leading financial services brands.

See how the Customer Value Maximization Platform can simplify your engagement with your customers!

Take me to the Platform

Score how well you are maximizing customer value

Answer 7 questions to figure out how well you are maximizing customer value.

Do you continuously engage with every customer in your portfolio – even when they don’t visit your branches – not just to wish Happy Birthday or Merry Christmas, but to truly grow value?


Is every non-branch interaction with your customers intelligent enough to consider relationship history, relationship value and customer exhibited behavioral traits and preferences? Every single interaction?


Do you know which customer to include and exclude, when to do so, and with what message, when you want them to buy your products and services? Do you do this without offline / online spamming?


Is there a clearly articulated strategy, understood by everyone? Is it being implemented by all stakeholders cohesively working together, to maximize customer value?


Do you proactively arrest customer value decline, even when customers don’t yell, or send stinkers to support? For example, when card spends decline steeply, do you auto-activate those customers?


Can you do all the above by clicking a few buttons – without investing in an army of IT, Analytics, Marketing and Product Managers?


To sum up, do you continuously grow customer value – measured on both revenue and relationship dimensions – evidenced by portfolio revenue and business value metrics?

To score yourself, you need to take the quiz!

Show Buttons
Score yourself
Hide Buttons